Techstrong TV – October 17 2024

Hey, everybody. I'm Mike Ard. Today we're gonna be talking about, well, Intel and a MD are strange bedfellows.

Lenovo seems to think AI needs a drink of water and BMCs at a crossroads. You're watching Textron. All right, folks, we're back.

And our guest today, of course, our start with our Mitch Ashley, who is not in Colorado for the, I don't know, for the fourth straight week. I mean, do you have a home? Um, I don Know, I hear this is what sea level is like.

Interesting. I'm, it's, it's nice to experience this. All right.

And you're in the area today, right? Yeah, I'm in San Francisco Bay Area. All right.

And John Schwartz is also in the Bay Area, and I, and I got a nickel that says neither one of you is gonna meet each Other. Well, I'm not. I am.

I amm not in the Bay Area. Mike, I am in Seattle. You Were.

Okay. We're playing musical. That's, You heard I was gonna be in the Bay Area.

Yeah. So I left town. I skipped town.

I owe you some money. So I'm out. And then we have Hope Lynch once again.

Hope I think, you know, are you home? Or you're the only one home? I'm not even at home.

If, if, if we'd done this another day, uh, coming up, I would've been in Atlanta. But I am at home today. All right.

And, and I am outside of my normal New York environment. I'm here in Las Vegas for this BMC event, and we'll explain what that's all about in a minute. But first, let's jump into this whole a MD and Intel have gotten together for a consortium that's gonna promote X 86 together.

I mean, John, you were at this event, but my first reaction was, wow. Hey. Hell did freeze over.

Yeah. Pigs flew. Who knew?

Um, so it was pretty strange. So, so yeah, last week I did a briefing with Lenovo. So this was, uh, at the Tech World's 2024 conference that they do every year now.

And they told me that Pat Gelsinger and Lisa Sue are gonna be at this event. And I was like, no way. I said, well, maybe they're gonna appear separately and there will be, they will be, uh, sandwiched with the other folks in the tech industry speaking.

They said, no, they're gonna be talking back to back, and they're gonna be talking about something they agree on. And it's, it's this thing called the X 86 Ecosystem Advisory Group, which is basically a consortium that's designed to push forward the ecosystem around X 86 processors as we go into the AI age. And so it's interesting.

So Pat Gelsinger walks out and he says, Lisa and Pat finally agree on something that he mentions the group, and he says, who would've thunk it? And he proceeded to be very diplomatic. And then she came out and did the same thing.

And I think what they both wanted us to know, and they made it very clear, was how important it is in the development of this, of the processors as we go into the AI age, and how they want it to remain a bedrock of modern computing and kind of a defacto architecture. Uh, what was also interesting, everyone was that micro Microsoft, which rarely doesn't really talk so much about this technology, was part of this consortium. While the founding members did not include Amazon and Nvidia.

Oh, I just mentioned some of the members of this group. There's Broadcom, Dell, Google, H-P-E-H-P, Lenovo Meta, Oracle, IBM plus, Linus Tour Vaults, and, um, Tim Sweeney of Epic Games. So it was, it was a very interesting event.

And, and unbeknownst to me, and this wasn't to say about me as a reporter, I'm covering this, and meanwhile, the, the, the two, Lisa and PA go backstage and they end up being interviewed by our own Daniel Newman and Patrick Moorhead, who put up a video that I, I linked in the story. Uh, it is a very, very, very interesting, and, you know, it shows that they can't agree on something, but I think they're agreeing on something as kind of a defensive measure against perhaps ARM or, or others. So, um, again, uh, something I didn't expect to see it at a Lenovo conference, to be quite honest.

You know, it's, it's very, I think you can look at the AI chip side of it, but very clearly ARM has made huge in inroads, you know, kind of from the edge and into the data center. So that clearly has to be a big part of it. Not surprisingly, it's, it's interesting to see what this consortium or what, what will come of it.

You know, sometimes these things are, are mostly look and not much output, and sometimes they really do some interesting things. So I'm interested to see where this will go. I wondering the, what, when I looked at it, I was like, the fear factor here is huge.

I mean, for these two to get together and do anything of this, like means that they are, I'm gonna use the word terrified. It's Qualcomm and Arm on one side and Nvidia on the other. And these guys are, you know, walking around going, Hey, the 6 86 thing might just wind up being, you know, a nice legacy architecture that we finally remember at this point.

Cobo go way, the, the, uh, way of the Motorola processor or something. Yeah. Where, where's those, where are those, where are those sun risk processors when you need me?

And I think I've got some that talk. Yeah. You know?

Yeah. It's, it's interesting that Mitch said, you know, he's absolutely right dead on. How many consortiums do we all remember are these initiatives that were announced that amounted to nothing?

They were all ceremonial. The, the in, the difference between that and what's going on with Intel and a MD was, uh, very specific. I've looked at press releases from both companies, very specific ideas of what they want to do.

So they've been thinking about this. And Mike, you're right, they are probably terrified. Mm-hmm.

Hope, you know, from the software community, when you look at these little hardware parties, you know, does anybody in the software side notice care? Are they, or is it just kinda like something that happens, you know, deep down in the hardware that they're not really paying much attention? Um, notice and do care wants, there are some standards and, and, and something you can develop against, right?

So if you have a team that is developing for iOS, they would be interested if there was some announcement in this consortium that had something to do with Apple, but as far as X 86, uh, yeah. If there are new standards that, that they provide, I, I think there will be a lot of interest. But one of the other things that does come to mind, and, and this bears on a conversation we had a few weeks ago, we were saying, you know, what's Intel gonna do?

How are they gonna solve this problem? Is it, is it going to be, uh, a takeover or, or what other solution is going to come about? And, uh, this is one option that did not come to mind for me, that they would, uh, do an enemy of my enemy as my friend tactic and, and, and try to find a way out.

The part that leaves me scratching my head a little bit is both a MD and Intel have other processors besides X 86. And so now they're standing around touting X 86 is like a platform for running your AI inference engine, but are you got other processors that arguably are gonna run those inference engines better? I mean, X 86 may be less costly, but you make a trade off between Yeah.

The capabilities of the platform and the accuracy of the LLM when you use an X 86 processor. So it strikes me as kinda like, well, um, why did we need to tie this to ai? Because ai, well, I think The, the, the intel, intel chip train, I mean, we all have been on this, this, uh, this transportation line for a number of years, right?

Every generation of Intel that comes out, and they come out with faster and faster and faster. And the next generation comes out with a different, you know, footprint for the Intel, uh, chip line, uh, for, for, for processors. And, you know, everything from low power to, you know, very high, high end server kind of things.

So I, I don't know, I, I'm not saying it's signaling an end to that, but I think it's signaling an end to the, that's the reliable path, right? We can't just stay on Intel, can't just stay on that path alone. A MD has certainly become much more competitive and, uh, doing well in, in its technology that it's delivering.

You've got Arm who's also, you know, now competing on the scene. You've got Nvidia and everybody who's making H age a AI chip, or as well as Intel and everybody making their own. So it, it's become, instead of Intel and everybody else, it's become everybody.

And yes, Intel is still a big gorilla, but there's so is Nvidia, and I think it's just a, a, to me it's more of a sign of a receding of dominance in the market and less dominance by Intel and more, I don't wanna say equity, but more strength by multiple players. John, you know, Lenovo, you know, I was gonna tell you before Mitch brought up a good point, you know, um, before I forget, Lenovo was, was hedging its bet, by the way. So later in the program, which is about a two hour keynote in the morning, they brought out a video of the Qualcomm, CEO Kristen, and then later, guess who appeared on stage?

You only get one guess, guess who appeared at an AI event on stage as the headliner to announce a partnership with Lenovo. Yes. Jensen, did He have a fancy leather jacket?

Of course, as the same one, right? Steve Jobs. Steve Jobs would wear the turtleneck he wears the, wasn't Steve Jobs the black Jacket?

Yeah. Steve Jobs with the jacket. The tough guy.

Look, but he came later. We'll talk about what they, In the age of ai, it could be Steve Jobs one of these days, so Pretty. Oh yeah, exactly.

True. Yeah, I'm waiting for that. Um, uh, but, uh, it's interesting.

So in a sense, yeah, so they they crossed all paths. Yeah, well, I, we'll talk about that in, in the next segment about what Nvidia and Lenovo did, which is kind of interesting. It almost feels like a Game of Thrones episode.

It's like nobody dies, you know? It's just the seats are changing and, and a lot of things, you know, games within games, plans within plans. It's just really, it's really fascinating to me.

'cause the market has really changed substantially. Uh, That's That, yeah. That's interesting.

Yeah, it's interesting because Lisa Sue referenced that. They talked about how, how quickly things are moving and how the import of what they were doing, just, it's just in general with this consortium. And it, and it, in a sense, if you really read into it, it's a fear of the unknown.

She talked about short gain versus long vision. That was sort a theme of what she was discussing. And it's the fact that they just really don't know where this is going, and they're trying to hedge their bets.

John, a couple episodes ago we were talking about, uh, Qualcomm and maybe launching a bid to acquire Intel, and we had Wall Street investors and financial services firms were talking about that. And, you know, we were likening it to, uh, I'm sure you remember the book and the HBO series barbarians at the gate. Um, you know, was there any talk of that at this event on the sidelines?

Were people going, Hey, you know, who's gonna be on? No. No.

Um, there wasn't, uh uh, no, not really. Um, that's what happens. I guess I remember that story.

You know, these, these, these companies all talk to each other, right? In one form or another to some varying degrees of, of seriousness. That one had some traction for a bit of time.

I, I wonder if it was a trial balloon in a certain sense. Uh, but no. Oh, by the way, I forgot to mention Motorola also had an executive there actually, before the event started.

I didn't know who this lady was, but I was sitting there talking to her. She ended up presenting on stage. So they, again, speaking of, of companies, you know, in the, in this realm, they were, they were everywhere.

But, um, no, that, that, that rumor, I don't know. I, I don't know how much credence there is to it. I mean, I'm sure there was discussion, but I, I know I I would bet on that.

Qualcomm intel, nah. And did they mention anything from this? Um, you know, the United States government, um, shall we say, uh, offering perhaps, uh, helping hand, I'm trying to avoid the, using the word bailout, but No, but it is what it is.

Um, yeah, hand, yeah. Yeah. The, the, the Pat Handout handout tour, um, no, they did not mention that.

Uh, his, he kept his, I gotta be honest, they had so many tech, he had Adela, Zuckerberg, those are both by video. They're obviously, they were obviously taped beforehand. They had almost, they had the FIFA president there for god's sake.

They had so many moving parts that everything was at the most superficial level. They had the, the major talking points, and they shuffled them on and off stage. Uh, but the, yeah, the government thing is, is a, is a kind of a point of, um, sensitivity for Intel, right?

Given where they used to be and where they are now. So, So It was, it was what Was do there, you know, I mean, it was, Okay, so yeah, this was, what was, this was the big reveal. The big reveal.

So Apple has their one more thing. They literally ripped off that idea. The, uh, yy, who's the CEO of, of, um, Lenovo said, we have one more thing we can't tell you about.

It's state secret. And it was the FIFA president, uh, Mr. Infantino announced that they are the technology partner for fifa.

I know they have a partnership on with F1, and they work with Lotus. This is Lenovo. They're going to be, uh, the, the official tech sponsor or tech partner for the Men and Women's World Cups in 2026 and 2027.

I asked that executive later, you know, if you could tell me, well, what does that mean? Is it you're gonna enhance the fans' experience? Or, well, what are you gonna do?

And then he, he kind of shut me down because I think they don't know what they're gonna do yet. They're, they're gonna formulate ideas of what they might, that's, Yeah, well, they're very superimposed. Should be over by then, right?

Got, yeah. Yeah. Well, let, let's, so let me, let me take back what I said.

Let gi, given the F1 relationship, Lenovo has a very long standing, um, relationship and demonstrated practice of c capturing telemetry. That's what's happening in racing. Um, now I'm not saying, you know, maybe we're putting chips in shoes or, you know, armbands with chips in them or something with, you know, on FIFA players.

I, I don't know that that's true. But there, there could be some very interesting statistical analysis, AI applications, things that they could do to really enhance the game, um, either for the viewers or possibly, you know, for coaching and playing too. So there, there could be some really interesting things that come out of that.

Yeah, Well, the stuff, The stuff that, Yeah, I'm gonna end this conversation, but I'm looking forward to the eu, what it has to say about collecting data off of soccer plumbers. I'm sure I'll be interesting Ation next year. EU AI feedback.

You'll all be fine. There you go. Unless my team wins.

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We're continuing to talk about Lenovo, which in addition to hanging out with Intel, was also getting warm and buzzy with Nvidia, as is everybody else these days. And John, you were there, but apparently they're talking about water cooled systems to run these AI things that we've got going. Yeah, they, so they, uh, they, they, they saved the best for last Jensen came on late Tuesday afternoon, and, uh, they, they talked about, um, expanding an existing partnership with something called Lenovo Hybrid AI Advantage with nvidia.

And they introduced something called the Think system, SC seven, what is it? 7 7 7 v four Neptune. So it's a system that leverages, um, uh, Lenovo's Neptune product to bring, uh, Blackwell and NVIDIA's GB to hundreds of the enterprise markets.

So anyway, what they're trying to do, um, is let organizations build and run accelerated computing for generative AI while reducing the data center power consumption by up to 40%. So it's a, basically a vertical liquid cooling system that maximizes data center power for AI and research. That's, that's the bottom line of what they're doing.

There are a lot of verbiage, but in a sense, it's a collaboration. And, um, it was interesting. It was, to me, it was, it was kind of a change of pace from what Nvidia usually does, which is, um, you know, it's kind of this highfaluting partnership with the ServiceNow or Adobe or whomever, Salesforce, I'm losing track.

Uh, Jensen came out, he didn't talk so much about this initiative, but he talked about the importance of the AI agents. And again, he, he started talking about this idea of unleashing billions of them. He calls 'em Little Jensen, right?

And they would basically be glorified workers or coworkers and environments and, and, and raise efficiency. But, um, one of the, one of the takeaways from the Lenovo event was two things. A star studded array of CEOs to, to Pat Lenovo on the back.

Plus they did, they did make a big play about sustainability. Um, and I was gonna mention this to Bonnie. Uh, this, this was a very hardcore theme throughout the day.

So, again, Jensen shows up, they do this announcement, they present him with some hardware. He genu flex in the glory of Nvidia being the king maker in ai, which he continues to be. And, um, this was the highlights of a day that was devoted to kind of a review, a refresh of the Lenovo product line with the, the, the Greek chorus of support from Zuckerberg via llama, uh, Microsoft via Azure, uh, Qualcomm, et cetera.

Um, it was very interesting. It was quite a stark contrast from last week in the Tesla announcement, which was one of the weirdest, uh, I I would almost, it was almost kind of like a bad LSD trip, right? This long 40 minute video then followed by absolutely very little superficial data, or a numbers here with Le Lenovo is just the opposite.

Every tech company imaginable is involved with, uh, specific numbers, specific statistics, specific topic talking points. So it's quite a, it was quite a contrast comparing the two. Mitch, I'm gonna go with that theme that John brought up.

'cause I am having a flashback here. I'm back in the day when we used to visit data centers, right? They would give us the tour and they were all, you know, so proud of the fact that they had these water cool capabilities and there was pipes everywhere.

And then we spent about the next 10 years ripping out those pipes. 'cause we had finally made the chips cool enough that we could actually run them without all this water, and we could actually just get out, run them in a lower cost cage. So, um, here we are talking about water cooled systems.

Again, are we coming full circle on this? Well, I think it was once the hearing loss kicked in from those high speed, very loud bands in those data centers, I mean, little, you can't decide. We had to be this close to each other, to talk to each other.

Um, I it's may, maybe it's a pendulum. I, you know, it's, I think it's very much representative of how much power, um, how much GE is generated from GPUs. And as we continue to accelerate what they can do, you can't air cool 'em, it's just, you know, there is a, you know, a ceiling of which that goes up against which, so, so if I kind of turn it on its head, let me look at the software side of this.

To me, every announcement we hear from Nvidia partnering with somebody else, we talk about the hardware side. The part we don't talk about is Cuda. It's another place where Cuda is the software framework, right?

That's use for AI applications, strengthens its whole, or strengthens gives us another foothold. Strengthens a foothold it already has in the software world of interfacing to ai. And we talk about Nvidia and its strength and hardware in the end, you know, others may catch up in, in the hardware side of it or become more competitive with Nvidia.

The software is hard to displace. You don't rip that out like you do the next generation servers. We're gonna go with somebody else's chip, right?

Well, no, I built all these apps on Cuda and, uh, that's gonna stay. So we're gonna stick with Nvidia. So I think there's some real, you know, you talk about short term, long term, John, kind of what the, the horizons of these things are.

I, I think it's a huge win for Cuda. Um, and, uh, you know, CUDA is a, as barracuda, if that's what it's named after, is a waterborne fish. So maybe that's why we're seeing all this water cooled stuff, Mike.

I don't know. I don't know. But hope, um, you know, in my experience, every time the processors get too hot and we can't afford to run the systems, it's not too long before we start blaming the developers and the software people for writing crappy in efficient coat.

Yes. So I knew we were gonna get to that. You Yes, no yourself, Mike, unfortunately, but, but I, I think I, I, I think you have, um, there, you know, there, there are boundaries, you know, that, that you can work in, uh, as a developer, the code you write, um, and, and so much of what your boundary is, is based on, on, on the hardware you have.

There have been breakthroughs over the years where developers have been giddy because instead of now the system, uh, being overclocked because of, uh, just how hard they're having to make it run, um, it, it can handle it. But if this is now saying, um, developers can get, lets say, higher performance per watt right out of this. So, um, they can ha harness more of the parallel processing power of the GPUs.

This gives more efficiency for some of the large scale computations than you would get on traditional CPUs. Um, this can reduce overall energy consumption, but also, yes, it, it can help developers, um, when they're trying to build higher performance, uh, systems doing higher performance computations. This, this definitely can be a win.

Will it solve the problem? No, because, uh, developers are gonna still continue to scale up. Once they figure out what the edge is.

They're, they're still gonna push it to the edge. But I think this does give some head, Well hope, you know, every, every time a developer adds a new API call in to the Cuda framework. Another developer gets their wings.

Yes, I believe it. Absolutely. What's up?

Uh, One thing we're coming up on the holiday season. I thought I'd throw that in there, Mike. One, One question I have, or, or I'm looking for opinions here.

One of the things that also I thought about, um, at least in North Carolina, there are a lot of data centers being built, a lot of data centers that already exist. Is this going to be a way that, I don't know, some of, some of these organizations that are building these big data centers, they can come in and say, now, hey, you have these concerns about water usage and power consumption and all of this. Look at this advancement we have, right?

That's gonna let us do more with less in this space with this footprint. The one thing I am looking for, and, and maybe I missed it, is what, what are the numbers? What is the percentage of reduction, right?

In power consumption? Is there a reduction in water use, or are they gonna use more water? Those questions are gonna be really important in the communities where these data centers are, are built and exist.

They're gonna use more, and they're gonna, it is not like they're gonna use lut. They're just gonna say, Hey, now that we got these water cold systems, we can use even more stuff. And then they're gonna wrap themselves in that sustainability flag that John was talking about and say what great citizens they are.

Yeah. John, I mean, you, they were talking sustainability. I mean, what did you hear that at the event?

Well, um, they were throwing out a lot of, a lot of percentages. I think what, one of the things, one of the themes I think, and it just doesn't apply just to Lenovo, but I think they almost feel a responsibility to throw out safeguards, guardrails. These are words that I consistently hear.

Sustainability. Um, we will adhere to regulations, we will, we will gain your trust. These are almost things that they're almost thinking, uh, kind of six to nine months beyond the announcements.

And once the product is out there, they wanna, in a, in a sense, kind of safeguard themselves against backlash, right? So sustainability is something that is, it's like a, in Silicon Valley, it's, it's something that's being overused, I think as a talking point to show that you're a good corporate citizen. Because I think a lot of these companies are a little bit on their heels, a little bit touchy, uh, around their relationship with the government, the this, this trust around ai, which continues now.

So I think they're checking a box. I, I, I don't mean to be too cynical, but I do really do think they're checking a box. I do think they also, I mean, Lenovo claims and I, I don't know the history of Lenovo to either agree with them or disagree that they've always thought about sustainability.

I mean, this is the sixth generation of the Neptune product. So I, I'm gonna, I'm gonna give them the benefit of the doubts. Maybe that's part of their DNA.

But, um, I, my suspicious gene kind of jumps my, the spidey sense goes off a little bit when I, when I hear these things because they're almost, you know, part of the script in a lot of these events, Mike, as you can probably vouch over the years, they, they all tend to follow a certain type of, a type of script in a certain type of talking points. So that's kind of my cynical take. Sorry to, that's all to feel that way.

But I really do, This is an imperfect comparison, but we talk about net carbon neutral, right? We take carbon out of the, and we expend it, right? We almost cut.

We need something similar to that to say we have reduced power consumption. We have reduced, you know, water consumption, whatever it might be this much while we're gain, while we're using more to do more power, but we're using it at a less increasing rate. You need to, you need to know that differential, right?

It isn't that just that it uses 20% or less power while compared to what, right? Right. We've increased the overall Power Consumption just hard.

Yeah. Yeah. All right.

So I, I have two cynical thoughts on this. All. You might be surprised.

Yeah, You did. But, but here's my first thing, right? If there's serious about it, I would like to see a response back from the occasional prompt that says, based on the amount of carbon that you're about to kick off on this puppy, we're not gonna run it.

'cause it's just not worth the trouble, right? We have no, like, metrics in here to say this thing is, you know, outta control. Secondarily, it would be nice, maybe if we had a pricing mechanism that said, Hey, based on the amount of carbon that you're about to kick off for this prop, you are gonna pay more for the privilege.

Right? And maybe we're gonna use that money to upset some of the costs that we are, uh, incurring as we, uh, destroy the landscape in North Carolina. Follow me.

Sorry. Hope You know, it's, it's practic. Okay.

I guess. But yeah. How do you respond to that?

I don't know. Yeah, it's there. You know, I was gonna, I, I was gonna mention that years ago I went to, there was a, at d had this massive data center somewhere in Virginia.

I remember I had to literally had to keep my eyes closed for a while while they were driving me there. It was surrounded by a moat. It had armed guards.

They, one of their prize possessions that they wanted to show me was the water tower in the back. And I think about that as the days of those might be going away. Um, I don't know.

I i, it, it will take, it, it, I don't think this, this announcements is, is as impressive as it may be to some people, is gonna have that significant and impact on the data centers as a, there's a, there's, there's a rush of building them ice. I used to do data center tours in my first job. I love college with ES.

And besides the big control room, you know, the unveil to open the curtain, that was the big wow. The second biggest impressive thing. The tank barrier at the entrance to the, that raised stuff outta the ground.

Everybody envisioned like, wow, I'd hate to run into that. You just never know what's gonna impress us. Was this before or after you oohed and a over all the racks observers.

I mean, where was that in that, That was in the IBM days. They're all enclosed cabinet. You couldn't see anything.

It was just a big cabinets There. It could have Been closed, stored in there. You don't know.

They used to have labels from all the vendors that were in there. Like they were baddies of honor. They did.

Now I just think they all, I think they just get 'em all now from like super micro and there's no labels anywhere. So Yeah. Blinky lights, blinky lights, Blinky light.

All right. Well, we're gonna end this chat about Lenovo and water cold systems and AI and sustainability because, well, it's bigger than all of us. So hopefully we'll figure out something to do about this soon.

But, uh, right now there's a data center coming near you. Stay tuned. All right folks.

And there, we're gonna talk about what's going on with BMC there, here in Las Vegas for their BMC Connect event this week. And, well, it's kind of a tale of two cities. If you haven't been paying attention.

BMC was announced that they're gonna separate into two independent companies. One is called BMC Helix, which is their ITSM platform. And the other is, you know, what we might call classic BMC, where it's basically the automation frameworks and the AI engines that get wrapped around that.

Now, it's interesting 'cause one of the things they were talking about here in terms of Helix, is that they're adding AI agents to the platform. And the, at least the theory is that these AI agents will take on more tasks and they have reasoning capabilities. Now, uh, this is a kind of a separate conversation, but we'll jump into this.

'cause I know John has done some work in this, uh, AI agents and orchestration conversation. And one of the things that I'm hearing from folks is that the, so-called reasoning engines that are in these LLMs are roughly equivalent to, you know, a five-year-old child that burns his fingers, laurel's at the stove is hot. And we are just over hyping the crap out of these reasoning engines.

And one of the things that BMC is saying is, you're gonna need these orchestration frameworks to actually tell these various agents, along with all the humans involved, what to do when. So, um, what is your sense of what's going on with these reasoning engines that everybody's touting and agentic ai? I mean, is this, you know, the second wave of a hype cycle Sounds like it to me.

Um, so was there a story? Wasn't there like a story or a research that was done? Uh, uh, I don't have it in front of me.

It was just, it just happened. I just wrote about it. It is a blur after a while, Mike, there's somebody, a agentic related type announcements.

There was this swarm. We, we, we kept, we don't have to talk about swarm from open ai, but that whole concept of things working in coordination with one another. Yeah.

Um, but then there's result, there's lab results or tests that show that these agents aren't nearly as intelligent as we thought they were. So you juxtapose that versus the hype cycle where we have Benioff and Jensen w at Dreamforce telling us that as we arrive at work, these agents will be doing all the drudgery that we dread doing. We first get into work like all the, the, the, the ditch digging, you know, so to speak.

They're gonna take care of all that. I, that, that seems to me like very optimistic when they, they say, oh, it's right around the corner. Well, what, what do you define as the corner?

Is that five years, three years? Um, and so the more I think about it, I, I, I wonder, wonder what hope thinks about this? Should we be as afraid or fearful of these age ages, genic AI taking our jobs?

Or is this just kind of part of a, of a hype, uh, utopian view that is, that is far away and is not gonna happen? I feel, I, I agree with you. There is a lot of hype right now because it's, it's in the realm of imagination, right?

Everyone imagines and, and, and thinks and dreams about all of the things that can happen before. Um, people on a larger scale, companies on a larger scale have actually tried these things and put them into practice. And one of my amazing cress skin predictions I will make right now is, it won't be as great as some people may believe.

Because imagine you're in an organization and you have all of these AI agents deployed and they are taking care of a lot of the lower level tasks. That is great. There are some things that should be automated away, but if it is some of the thinking tasks that you would do in your role, now you are, to me, building even bigger silos.

You're creating bigger knowledge gaps, you're increasing the opportunity to build tribal knowledge because there are so many things in your role you will not see touched you, don't you, you, you don't know how to do and you don't understand because the agent has always been there, you know, just taking care of it, you know, look out for the day that the agents break, and you're just sitting there trying to figure out how to get your work done. But I think there's a lot of hype right now. I think there are useful, useful places for it.

Like, you know, repetitive documentation, um, repetitive intake types of tasks. But I think, I think some of the others, we should, we should wait a little bit longer before we jump aboard and, and just try to throw everything at agents. Yeah.

They had this, this weird hypothetical situation at the Lenovo conference near the end. They have a, uh, futurist Allie Miller, who, um, is, has quite a following Mm-Hmm. Sheet.

He was talking with the CTO of Lenovo about, uh, car buying agents and a scenario where your, your agent, uh, knows the type of car you want. All the, the a additions you want, your personal financial situations. So how you would pay for it.

And this is all gonna happen very conveniently without you in involved at all. And I was just, I was thinking, come on, this, this is, this is like pie in the sky stuff. Um, I, I'm On, I'm on the other side of this.

I think, you know, we're gonna have all these agents running around doing stuff and they're gonna drive us crazy. 'cause they're gonna be like, you know, they're gonna be overly helpful all the time. And they're gonna be like, can you do this for you?

And you're gonna go, no. And then like two minutes later, you're gonna be like, put that down. And then about half an hour in you're gonna, you do that.

I would smack you upside your head. Yeah. Love It.

Hey, can I, can I mention something? Totally off topic. I have two fun facts about the amazing Kreskin.

Oh, yes. Number one, he was on this tonight. You've gone to tonight's show more than any other guest in the, in the history of the show, evidently.

Really? Yes. Number two, I went to a tech party, a Christmas tech party years ago with, I took my daughter, who is, who's a reporter.

He was performing. He a no one knew who he was, number one. Number two, he bombed on everything.

He, he screwed up everything. And later I, that makes it even funnier. And it was so bad.

It was so embarrassing. And it was just it, and I'm watching it. And it was, it was just so heartbreaking to me in a certain sense.

And I woke up to him later and I said, I'm, I'm sorry, what happened? He goes, you know, that's showbiz. Anyway, that's something that was an insight to life to me.

Right? And I thought that was so great. I I respected of even more.

Sorry, I had to share that. Oh no, that's, that's great. Great though.

Now I have visions of Johnny Carson and the amazing carac going through my mind too. So thanks for that. Oh, Mike.

But you know what, Mike, it's also that the whole, the whole thing with Yeah, you're right. It's gonna be, uh, when you talk about swarm, swarm is a good word to use. 'cause these things are gonna be pinging you from every conceivable angle.

It's like the over zealous GGPS system that says, oh, there's a, there's a matrix. A little bit of a backup there. Why don't you go off the side of the road for a while I've done that, and then I turned it off.

It's like, you know, do this now, do this. I have another suggestion here. This will help you.

It's like, please stop it. Just leave me alone. Mm-Hmm.

And that, that's another, another consequence. Right? And, you know, and I love the, what John's talking about is open AI has this, uh, swarm project, which is gonna be this orchestration engine for these agents that they're talking about.

But it doesn't seem to me they have a lot of confidence in the thing. 'cause the first thing they said is, A, it's experimental, and b, there's No product. No, yeah.

It's not a product. Instead of the blog thing. Yeah.

Basically. Here it is. But, but don't hold us to anything.

Just, just, just, it's just a concept. Don't worry about it. I think what they're saying is, this is something we built.

We're not sure if we can make it work. And we're hoping somebody else can figure this out, but here's where we are. Let's, let's, let's put it out there and, and see what, what happens.

But, uh, yeah. Can I ask you, Mike, about the bm? Was it, they, they talked about generative ai, uh, in the mainframe?

What's that? Yeah. What was that about?

This is a follow up. They were in talking about this as a statement of direction for a few months now, and they are now have an actual AI assistant. And, you know, it's called Amy actually had to explain to them that, uh, you know, there was actually a song called Amy and, you know, basically had to refrain, Hey, Amy, what you wanna do?

But, you know, they didn't, they were totally like way over their heads, but I think they're all looking it up now. Um, but the, the point here is it explains the code on the mainframe, which is an issue because, you know, large numbers of main framers are now sitting on a beach drinking mi ties and being semi-retired. At least that's my vision for them.

I hope that's how it all worked out. Um, and there's all this code that nobody documented back in the day. And now we need to use AI to go sort this thing and say, Hey, um, can we fix this code?

Can we modernize it? Do we need to rewrite it? And I think AI will play a huge role in that.

So BMC is on the right track for that. And now they've come up with an assistant that helps you sort through that. And they too will then probably extend that to include agents for mainframes, and maybe they'll do the actual work of rewriting that code.

We'll see. Um, that's, you know, clearly IBM and everybody else is kind of talking about the same path, but it's interesting. I've seen and hope, I don't know, maybe, uh, I've seen a pullback in the language used for these tools, right?

It was kinda like, we're gonna convert co ball to Java using this. And now it's kinda like, Nope, we're gonna help you upgrade from Java eight to Java 17. And that's about as far as we're getting at the moment.

So, you know, have we kinda like pulled in our horns a little bit on what these code explanation writing tools can actually do for us? I think so because it's been in more hands and people have actually seen not only what it does, but what it does well and, and not so well. Right?

So I think it's tempered the expectations and just the, the use cases that people are applying it to. Uh, if, if we think about though, when, when you're talking about the mainframes, um, my senses tingled a little bit because I'm thinking, you know, there's, there's gotta be a solution. But a lot of, a lot of the systems that are still using mainframes today are the heavy hitting systems that are running the financial backbone, at least in the United States, right?

I know the IRS still has a lot of mainframe systems. There are other companies that still have a lot of mainframe systems. So if you, you bring agents in and now the agents are are, you know, making updates, they're working autonomously.

Um, yeah, I'm gonna say, you know, my brain starts to think about, you know, the bad actors are already there, but, but wow. If they can put these agents to work for them, and by the time we figure it out, you know, we might be a long way down the road. Yeah.

Who exactly has control of what agent's gonna be an issue, right? Because all I gotta do is fish your credentials and then I gotta control of your agents and party's not. Yes.

So yeah, that will definitely be a interesting, um, story to report in about another nine months. Or I think, I think you're right. I think you're right there.

You kind of, what, you kind of wonder if the, if these companies are, uh, like, uh, uh, anticipating these things happening, right? So they put in like little fine prints when they talk about security and they're about trust with their products, right? Like they're anticipating there will be a problem down the line.

Mm-Hmm. So they're trying to get a little bit ahead of it. And I always get that feeling at these things like, it's too good to be true.

So, but you need to read the fine prints, you know, if you're patient enough, nobody is. And, um, that's, that's the other thing that always lends a little bit of suspicion to me at these events. Um, they just, they're just presented as like so clean and, and, and so easy.

And it's just, there's, it's not, that's not the case. It is not, It is not. A lot of effort was made to ensure that the mainframe is still hanging out with the cold kids.

And, you know, those, it, it's part of the mainstream platform. And honestly, you know, I hear all the vendors, you know, doing this kind of thing and I scratch my head sometimes. I'm like going, you know, I think you guys are the only ones who think the mainframe isn't cold.

'cause everybody else keeps using it and they're work doing work on it every day. And you're the audience walking around going, oh my God, is this thing, you know, not happen? And it's like, no, actually the platform's plenty it, everybody hanging around it maybe not so much.

We'll see. Mm-Hmm. All right.

Well folks, that's where we're gonna end it. Uh, it's been an interesting week so far and we got more to come tomorrow. So by all means, stay tuned and we have an awesome lineup of shows right behind us.

Hope John, thanks for being on the show. Mm-Hmm. Thank you.

And, and, and if you hadn't noticed Mitch had to leave early, but hey, we'll be back tomorrow. Thanks for watch Discover Textron Group, the epicenter of tech innovation. We are your go-to for reaching IT leaders and practitioners worldwide.

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This is Textron tv. Hey everyone, welcome back here to techron tv. I've got a really good story.

It may not be a big story to you guys though. It is a big story. You know, in the, in the area they play in open source software and, and licensing and software composition analysis.

This is huge, but it's also on a personal note, a really feel good story for me. I used to cover this company way before there was a tech strong, a media ops. I had just left still secure, and I was, I think I was writing for Computer World or network World, one of the worlds.

And I and Phil, who I find out is still a black hat. Phil was doing a blog there too. Still here?

Yeah, he still a black Phil. And I used to compare notes all the time, and that's how I was really kind of first introduced the Black Duck software. This, of course, was before Open Source was, as you know, prevalent as it is today, as dominating as it is today.

And it was really Black Duck back then was about open source licensing, right? Enterprises wanted to know what was their exposure vis-a-vis licensed for open source. Anyway, not to steal my next yes thunder, but Black Duck wound up getting acquired by Synopsis.

He'll tell us what years ago, you know, synopsis was its old version of Big Brother, big corporation and, and Black Duck, you know, held its own and, and was a valuable component there. But the good news that Duck is back, black Duck is back. Let me introduce you to Scott Johnson.

Scott is VP of Product Management at Black Duck. Hey Scott, welcome to Text Drug tv. Hey, thank you so much.

And uh, like I showed you earlier, I, you can kind of see I brought, I brought, this is actually, you got the black duck. There we go. Yeah.

Now this is one of the, one of the older versions. I think we have cases of them. So the new the new Black duck is a little more velt.

Really? Yeah. I, I kinda tell you what, hey, but I'm a little more velt too, so it all, it's all good.

Alright. And You're looking good. It, it's what's happening.

Scott, before we jump into Black Duck though, let's talk a little bit about Scott. As I said, you're VP product management, but this isn't your first stop in the security universe. Give us, give us kinda your background.

Yeah. The universe is, is, uh, expansive, isn't it? Right.

So I started related It. Yeah. It's big as it, is it six degrees of separation?

Well, we, we talked about just some of the interconnected folks that we know from the industry. So yeah, so I, um, I started, uh, back my cyber journey back in 2002 when I had the opportunity to join internet security systems. Some of your listeners.

Yeah, black Eyes internet Scanner, X-Force X-Force team. Yeah, the original, uh, white, white hat, you know. Yeah.

Good guys, so to speak. Right? So, and I really got the truth.

You didn't call it cyber then? No, no, We Was sec Yeah. That, that evolved into Yeah, That a word for evolved, Right?

And we were still talking about, you know, ninda. I mean, think of all the, the, the really interesting, you know, Trojans and worms and stuff, Ninda and, and, uh, uh, the Anor Cova virus. I still remember the, the, uh, uh, the McCarthy virus took out my, took out and bricked my computer.

Jesse Mahar got my IBM machine. Oh God, I remember that one too. I can't call for that.

But anyway, I Fell for it. I got caught. So, but yeah, so I spent a number of years there, um, got, uh, acquired by IBM and, and the journey ended up in, uh, the AppSec space.

So I, I was with a, a competitor in the AppSec space as a GM for a number of years, and then joined, uh, uh, the Synopsis group about four years ago almost now, when, uh, our current, uh, former GM and now CEO Jason Schmidt came over. And so it's been quite a journey in the, in the security space and a lot of great people along the way and, and just look the continued, uh, challenges to resolve for customers and just how, how critical, you know, cyber is to our day-to-day businesses, right? Our business operations and the enterprises themselves.

I mean, I was, we were just in a user event, um, in London and we were talking about one of our customers was there was Maersk and, uh, we were just talking about the impact in the supply chain. Like when, when the barge hit the bridge in Baltimore, it wasn't a, yeah, it wasn't a cyber thing, but the rippling effects of everything along that in terms of, you know, what's in the supply chain, what's in the box, what's in the container. We had companies there like Recit who makes Lysol.

So there's literally packages there. And so the, the impact and the interconnectedness of things just underscores how important cyber and InfoSec is. So that's a little bit about my journey and I sure I made the decision back then when I was talking to a couple of, uh, uh, the guy named Neil Meta, who was one of the, I think he's a Google now, and he was one of the guys along with our Kon folks that, that, uh, first sort of brought heart bleed to fruition.

And I was talking to him and after a conversation, I'm like, you know what, cyber's where I'm gonna be, I don't know what I'll be doing 20 years from now in cyber cybersecurity, but I'm gonna be there. And, uh, let's just say it hasn't disappointed and we haven't, no, we both have the scars. We both have the scars on our bed.

Hey, I Would say disappointed. Well, let's say, say this as Sanna Dan would say, it's always something inside the security, right? It's always something going on.

Um, Scott, I, I tried to do justice describing sort of the early black hat when I first kind of, you know, was exposed to them. It was really about, uh, a software licensing open source license. Back then you had OSI licenses, there were six or seven, and Black Duck was the standard on how you, you know, made sure you were in compliance with your open source software licensing.

But of course, over the years it, it's expanded beyond just licensing. I wanna discuss that and I want to discuss sort of the, the history of the Synopsis acquisition, and now the Sure. The spinoff.

But let, let's first talk about the Black Duck product line, right? From open source licensing to what? Well, really centering around, um, I think two, two factors.

One, um, the vulnerabilities, right? Being able to identify vulnerabilities associated with the open source components. When you look at the, you know, the predominance of an application today where 70, 80, 90% of the application is, is open source, and all it takes is one vulnerability that gets embedded right into, uh, the, the base of the app, right?

Your mobile app, if it gets distributed, if you're a large financial institution, and it goes out to a million customers, right? You have to identify that. So that's, that's a big part of the evolution that happened there.

The second thing, um, more recently goes back to, uh, the executive order around the supply chain and what we really are many, yeah, the supply chain effect and how black duck SCA is really, um, the, it's really all about, um, supply chain security and open source. SCA is a big part of it, but it's evolved, right? Where you have to be looking at the SBOs, uh, looking at, uh, the various fields, right?

Every customer wants to know dependencies and all of it that the exact, exactly the dependencies and, um, the ability to create an SBO and, and know the ingredients of your software, just like you wanna know the ingredients of your, your peanut butter, you wanna know. And, and that has really changed things. We've done, we've done a lot over the last couple of years, and especially the last, uh, six, nine months to incorporate supply chain capabilities.

We came out with our black duck supply chain addition, which builds on that licensing, uh, aspect that you mentioned, our binary analysis and add additional capabilities so that our customers can really address that challenge and, and, and meet those needs. Because today, every, every application requires an SBO m that goes with it in the, the impact down the stream. When you look at many of our customers and say the automotive space, you know, they're making the software that goes into the chips, that goes into the car, that then drives us to the store or drive or our, or, or our family members to the soccer games.

And if there's issues with those, those chips and the firmware and the software, um, you know, lives could be at risk, right? I still remember going back to Joshua Corman saying, I don't want my automated self-driving car, you know, running over my grandmother, you know, and it's like, that was 10 years ago, and today it's like, yes, we can't have that. And, uh, so that's part of the evolution with, uh, with Black Duck, SCA, um, and then as a company more broadly, which you were alluding to, um, when you look at Synopsis, and really it goes back to Chief Oon, uh, one of the, the co-CEOs and longtime synopsis, uh, leader who really had the foresight to look at the evolution of what Synopsis was doing with EDA and, and building out or providing the, the design, the chip design software, and realizing that wow, InfoSec and vulnerabilities that could be associated there, that synergy is really important.

And so that really led to some of the acquisitions with Verity, black Duck, Sal Koon, uh, and a number of others that really brought us together to create the nucleus for the software integrity group. And now, um, the foundation for what's now Black Duck software, which includes all of those, uh, technologies and solutions for our customers that we'll be able to build on. So I, I want to make sure we, we emphasize that people understand it.

First of all, SCA software composition analysis, right? Which is kind of the, the code name for scanning for vulnerabilities and open source software and components. It's more than scanning, it's testing for it and everything else, right?

But when we talk about this new version of Black Duck, it isn't just that they spun the old black duck back out. This is truly a new black duck that really represents the entirety of that software integrity group. Right?

So Synopsis is, and I, Scott, you, I'm assuming you would know more than I, billions multi-billion dollar market cap company, right? Correct. Yep.

5 billion in revenue, extreme growth, the hockey stick grows, you know, and yeah, I mean, the whole thing Thing, very successful, right? And about, I guess it was maybe a year or less ago, they, they said they were gonna like bifurcate the company and spin up this software integrity group, which does, as you mentioned, had Black Duck and some of the other Cohesity and some of the other companies they had acquired as well as, you know, organic products that they had developed there into one software integrity group. And that was ly, you wanna call it cyber software integrity.

It was probably bigger than just pure cyber. And now that entire group represents the new Black Duck. So this Black Duck Plus plus was, Right?

Yeah. So this is, that's exactly correct. So the, the roll up and, and the, um, software integrity group was sort of created some years back, and each of those acquisitions went into the same business unit.

So the great thing is the foundation that we have, not only with the great technologies, but, um, most of a business unit. So in terms of being spun out, right when, when the organization looked at, you know, um, what made the most sense for us as a business unit and the direction that the, the mothership was going, it made a lot of sense to say, Hey, let's enable, uh, the software integrity group to go out on its own and really drive its focus and, uh, enable us to action our vision. Because as great as being part of the mothership has been, we're still, you know, 10% of a $5 billion revenue company that was more distributed.

So when they made investments, they obviously had to look where we, where do we get the, the, the right ROI for our direction, uh, and um, really, uh, focusing across the board. And so the new entity with us becoming Black Duck. Uh, and there was an interesting quote that I, I heard just the other day from the poet William Blake.

When's the last time William Blake was quoted, uh, on, on your show? Probably never. Well, Maybe when was it ever is the question Exactly, but he said, no bird source too high if he soars with his own wings.

And that's sort of indicative when I thought I said, that's us. We have been a great company, a really profitable business unit within Synopsis, and now, right, we're, we're moving outta the nest and we're having the opportunity to really spread our wings to, to tie in with the, the vision, the, the, uh, um, the imagery to, to address the needs of our customers more directly. And, uh, it's, it's an exciting thing that many of us had really, that are here, have been wanting to do for some time.

And that really is to drive the, the platform to meet the needs of customers at a, uh, at a, at a more granular way to address the challenges that, that are out there. Absolutely. Scott, our audience is, they're a technical audience.

Not many people from the CFO side of the house, but for those who may be wondering, so if Black Duck is now an independent company, independent of Synopsis, is it wholly owned by Synopsis? Is an employee owned VCPE owned, what's the story? Yeah, so we are, uh, part of, uh, two private equity groups with, uh, clear Lake and Francisco Partners.

And so we just Also very, very heavily invested in cyber, both Heavily invested in cyber, right? They, they, in fact, um, uh, one of the, one of the reasons why we are still using the, where we're going with the name Black Duck is one of the private equity team members, um, has been following Black Duck for a number of years. And when the discussion came up, uh, that individual had one of the black ducks and said, well, what a, what about this for the name?

Um, and, and plus just branding wise, you know, why rename and come out with Aptify and try to start something new when the Golden Egg right is right there in the nest. So why not? Yeah, No doubt about it.

I mean, look, I, I was, I was thrilled to hear that it was being named Black Duck, right? That was, that was a good, I mean, I don't know, for me, it just brought back good memories and, and good feelings of, of a quality company, right? And, and yeah, those are hard To come by.

So all of hard to come by. Yeah, it's all official. As of October one, um, we, uh, we opened the Egg if people were watching on LinkedIn, you know, and we, we hatched in terms of, uh, becoming a, uh, black duck as a, as a business entity.

And so we're now in sort of the, that short term transition, right? Where we're working through all the entities set up and making sure we have the right businesses set up in the different regions, making sure our PO systems are working and all those things. And then really, um, you know, driving that assurance with our customers.

Um, and unlike a lot of private equity scenarios, one of our benefits we think is right outta the gate, we're profitable. So, um, 500 million roughly in revenue or, or profitable. So we can, we can focus on growth versus sort of the cost controls that often come with, with pd, uh, that I think a number of folks in the space and our competitor base have gone through.

And then the, the third element of sort of our short term efforts is really then from that assurance with customers. And we just had a user event in London and spent some time with some of the largest enterprises in the world and really just centering around our execution. So the things that we said we were going to do six months ago, nine months ago in our roadmaps, continuing to execute on those, we're working now on rust coverage, for example, with Verity.

We talked a little bit about the supply chain, and we're continuing to add new capabilities there for SBO m management and doing that at scale. So it's, it's proving to our customers that, um, yeah, now that we're our own entity, um, we're making that transition. And then longer term, when we really look at what that means to drive more agility and really focus, right, um, in a larger enterprise, right?

There are, there are times when the, the mothership might say, Hey, we really need you to do this deal with Customer X. That may or may not really fit for us, but you go do it. 'cause the mothership wants you to.

So in our new world, we're really gonna focus in on our base customers with, uh, the, the embedded space, uh, and truly just expanding that. There's a lot more coverage we can add. And the, the various hyperscalers out there.

So you mentioned the, the large tech companies, um, our bread and butter, and then the regulated enterprises. So transportation, automotive, and really getting closer to those customers. Um, and maybe not quite as much in maybe the commercial side that we will, we'll have some options there, but really doubling into those customers that truly have complex and regulated needs that we feel we're best positioned to, to meet the needs of.

And so that's sort of our longer term approach around agility focus, and then building that trust with our quality results. Because for those customers, um, quality still matters, right? We just, I just saw a win wire come across.

And, um, the interesting thing is it was against one of our competitors, and the primary takeaway for that customer was the quality results were the difference. And so we wanna continue to build on that while we evolve the ease of use with our SaaS platforms so we can play that side of it, but the quality can't waiver, and we're gonna continue to build on that. And again, that's part of the Black Tuck brand is the quality.

Absolutely. Hey, Scott, I got a silly question. Sure.

com now? com? It's Black Duck.

Black Duck. Yeah. We, we, uh, we maintained the URL, um, In, I thought you did Throughout.

Yeah, very. So, so we were fortunate enough, somebody was smart enough. com was a bed and breakfast way back in like 2013.

So Really? Yeah. So maybe we'll make some with a, with our, with our duck.

Maybe we'll make some, some eggs at some point. But, uh, the irony is that Sounds like a breakfast at RSA or something to me. You know, we're on, I got you.

We're on. Yes. Hey, Scott, thank you so much for coming on.

Congratulations to you and Phil. Thank you. And the whole, the whole Black Duck team there, man.

We're, we're expecting big things from you. It's good to have you back. And, uh, you know, I, I think we're doing some work here at Techron with the Black Duck team already, so we'll have some stuff with that, but man, I'm excited and happy.

It's good, it's good for you guys to have that name back and it's excited to see where you guys go with it. Yeah, we're looking forward to it. As always, Alan, really appreciate your time and, uh, Thank Blake.

Thank you so much. All right. All righty.

Take care. Hey, Scott Johnson, VP product Management at Black Duck here on Tech Drug tv. com.

We're gonna take a break. We'll be back in a moment. This is Textron tv.

Hey, everyone, we're back here live in San Diego with more coverage from the Qualys QSC. I've got two speakers here to introduce you to. First of all, to my immediate right is Beatrice Circus Circus, and to my far right is Asman Zube.

Darn. I got it right. Okay.

Beatrice, why don't we start with you first. Tell people a little bit about kind of your journey and what brings you here today. Okay.

So, um, I work for IDB Bank in New York. Uh, I am the head of application security and vulnerability management. Um, I started in security 20 years ago, uh, with a lot of experience in, uh, networking applications, um, if all that is related to this.

Um, so today we are going to speak about Quas web applications can, um, and how is this platform allowing us to keep the bank safe, um, to, to keep the bank within, uh, a, the risk appetite that is defined, uh, and to provide our customers, um, good and safe service. 20 years ago, security was all about network security. Definitely.

It's come a long way. Yeah, right. It was all network security.

We had the moat and castle. It was such a different, different place. But thank you for sharing that.

Swan. How about you? What's your journey been like?

Yeah, so currently I work at Qualys. I'm leading web application and API security product line. I have been in application security space for more than 10 years, and I have had the opportunity work to work on dynamic application security, interactive application security, little bit of sast.

So I absolutely love application security space, and I'm happy to be part of this conversation here today. So, I remember when I first became exposed to AppSec, as we call it, right? Application securities.

I had a friend, Jeremiah Grossman, who started a company called White Hat. Yes, I Know him. Sure.

And that was my first exposure to AppSec. And of course then we saw, uh, uh, oasp and, and all of these things and application security became, if it wasn't AppSec, it didn't mean anything. It no more network security.

It was AppSec. And then, you know, funny thing happened and we saw the rise of WAFs web application firewalls, and I got into DevOps and DevSecOps and that exposed the different kind of web application security. So was no longer just about scanning, whether it was a static scan, a dynamic scan, software, composition analysis, SCA and so forth.

But it was also, it became about software supply chain security. 'cause that's part of your app. And all of a sudden everybody started putting, and another thing happened, you mentioned APIs.

Yep. All of a sudden, APIs, now, 57% of all the traffic on the internet is just APIs, talking to APIs. And you know what, for the most part, they fly underneath the waf.

So WAFs, which where all the rage quickly became useless, almost useless. And we had to have a new, a new way of looking at security. API web application security through it all has been Qualys, right?

I'm, I'm in this, as I said, 28, 29 years through it all has been Qualys. How is this evolution, this story impacted? It's obviously impacted you in your career, but how does it impact how you do web app security and scanning at the bank?

So, uh, that's, that's exactly the question. So, uh, most of the, uh, applications today are web based even. So we see the migration towards mobile, uh, and that is happening.

Um, we still have most of our applications on the web, and of course, those applications are using APIs because there are so many financial platforms that need to interconnect and interchange customer information through the APIs. So the API security became the critical topic of this year, and the top priority from a security perspective. But I want to go to the basis first.

So the web applications, the web applications, as we know from the SDLC, they are starting from a non-pro environment. And most of those applications are that critical, that have multiple non-pro environment. And we scan those using Qualys web applications, can in non-prod, and then make sure that we, uh, work with the application team, uh, to remediate the critical and high risk, uh, vulnerabilities in non-prod.

That is, um, the, the threshold is zero critical and high risk vulnerabilities when moving into production, otherwise it'll not be approved. Right? Um, and we are doing so using, uh, the Qualys, uh, infrastructure, meaning the cloud agent, uh, that is in the cloud for the applications that are in the cloud.

And for the internal ones, we are using, uh, the, the scanners that we deployed into the bank infrastructure. So it's all based on Qualys infrastructure, the scanners, um, and those scanners are used for, uh, more than only the application, but we are speaking about the applications today. Sure.

Um, so, uh, having the ability to scan from the non-pro, it means that in the production, the applications will be clear from a security perspective, they will still be scanned. But we see that, uh, critical and high risk vulnerabilities are already remediating, and it give us the peace of mind to allow the application to go live. So, and just to be clear, when we say non-prod, right?

It could be in a test environment, a dev environment, it it, you know, if we look at the left to right timeline with the middle being deployment, it's to the left of deployment. So yes. If you will shift left as we call it, right?

Right. Doing, doing that, these scans there, um, now that's a big change, right? It's a Huge Change.

When I was doing security, we were lucky if we scanned it once a year after. But we also live in a dynamic world, and what, what seems to be secure today is not secure tomorrow. It's even more than that.

What is secure today? It might not be secure tomorrow because a new vulnerability will be found out. And then continuously scanning with the Qualys, uh, infrastructure, we discover those vulnerabilities in a timely manner and be able to remediate those because, you know, anything that is not remediating, it's, it doesn't worse, right?

So we, we keep a very close communication with the application teams so that they get the right details, what is the problem, how to solve that. And then we re-scan and validate that is remediating Zuma. I want to ask you, we, we mentioned there's different kinds of scans today, right?

And I apologize, I can't make these people stop talking, but our mics are good. They'll hear us. But we have dynamic scanning, static scanning, software composition analysis, API testing, it's, is it really called API scanning or API testing?

Depends who you talk to. Qualys has all of these, So definitely, uh, for dynamic security software composition analysis, basically whatever is needed for, to secure your applications or to perform security testing, to be precise, Qualys offers that. And our web application security testing is very unique for many different reasons.

We scan for not only UVA top 10 vulnerabilities, we also look for PII exposure. We also look for, uh, we like using Qualys VAs, you can detect and monitor for the presence of malware as well. So it is super comprehensive, uh, application security testing.

And what about API specifically anything? Absolutely. So for API security testing, we have, um, like OVAs top 10, uh, for APIs.

Yes, Yes. They have their own API list Especially. Yeah.

Those checks. And not only that, uh, Qualys was also looks for compliance to open API I specifications because that has pretty significant impact on how APIs are adopted. So we look for those weaknesses as well, or nonconformance, to open API specifications.

So, you know, when you look at the banking industry today, the banking industry today is a very different place than it was before C or 25 years ago. There aren't a lot of banks putting a lot of money into opening up new brick and mortar branches. Everything is done via app, everything.

Right? I, I actually had to stop in my bank this week to get some paperwork done. It's the first time I set foot in a bank.

I don't remem, I don't remember the last time I was in a bank. Now, this puts a lot of pressure though, that these apps, they have to run flawlessly because you know how customers are te if if your internet on the airplane doesn't work, they're ready never to fly your airplane again. Right?

They need those bank banking's. Mission critical. How does Qualys help make sure that your mission critical apps are not only secure?

'cause sometimes I think people, they talk about being secure, but it's more important they work for them, right? Yeah. How does it help make sure that they're up and running all the time for them?

Yeah. So, um, this is, this is really important. Um, we are able to use, uh, qua tags, uh, to tag the critical applications, um, also to tag the environments and also to tag the exposure.

So being able to tag, we will prioritize, uh, the scanning and the remediation for the ones that are more critical to the bank. And why we do that, to make sure that there won't be any financial loss. Sure.

Okay. So this is all we want. We want to keep our customers safe, we want to keep the bank safe, and we know that, uh, the financial sector is the first one that is being attacked because there's money there.

So Always, Yes. So there's no, uh, there's no expectation that those applications will not be attacked. Uh, the only expectation is that when it's this attack may be impacting.

So we are constantly scanning, constantly reviewing, constantly looking at the, uh, risk level of the vulnerabilities that we found. Uh, we are using also the, the quality true risk. Uh, so only speaking about the severity, not enough, right?

Because it might be like a medium severity vulnerability, but Well, it could be medium severity on other risk. Exactly. Risk.

The risk might be very high. So we do take that into account Sure. And work with the application development teams to remediate those before, uh, before they are going on with an upgraded application or before they play a pitch.

Um, and there's another aspect as well. net, um, Apache and those third party software are coming with other vulnerabilities that are continuously discovered. So we use the software composition analysis from qualities to discover those and to remediate those as well.

And that saves us efforts to, um, implement another platform, because otherwise we should need to buy another platform that, that's software composition analysis. We have it in Quas with the very same agents that we are already deployed and, uh, installed. Uh, so this allows us to efficiently address and have one source of tools for all those vulnerabilities.

Obviously we use another platform auto to scan. We use belt. Uh, so we, uh, import the XML files from Burp into Qualis so that we have one source of truth, right?

And we have a complete report to our application development team. This is what you need to remediate. Uh, and it's very good that, uh, the report includes not only what they need to remediate, but also how they should do that.

And that, that, that's an important part of this, right? Because I, you know, the old days you would get a, a vulnerability reporter, it looked like a telephone book, and many of the people young out out here don't remember what a telephone book looked like Exactly. But, but they were very thick.

Right? But if you didn't know what to do to fix it, what good was it? You had to sit and look up every one.

It could take forever. The nice thing about the Qualys, uh, uh, web app portal is it tells you right there how to, how to fix these. I haven't heard someone talk about bourbon in a long time.

Um, yeah. This, this is a very, very good platform, uh, very, uh, customizable. Yeah.

Um, and, um, uh, it's, it's always a good way to, uh, test an application from different angles. Absolutely. But the better way is to have one source of tools You have to, 'cause we won't be able to manage otherwise.

It's hard enough managing with one source. It gets exponentially hard from there on. It does.

It does. And we are also aware that, uh, developers that they need to remediate. They are not security specialists.

No. So the more information we are giving them from Qualis, the more they are able to do their what And, and the more they understand. com, so we speak to developers all the time.

Oftentimes they feel like the security people tell them stuff. Oh, there's a, there's a vulnerability here. Why, why?

What's vulnerable about it? What, what, exactly. And so, and 'cause they tend to think those security people just make stuff up to make our life hard.

They say, no, why do I gotta do this? What's the policy? What, so when you could give them that kind of background, no one wants to make bad code.

No one wants to develop insecure code, but they wanna know that this is not just being done arbitrarily. So, so having one source of truth makes us cybersecurity to be an enabler for the business. Uh, and not say not A burden.

Exactly. Yeah. Agreed.

We're about outta time. Zuma. Beecher, thank you for being here.

Thank you for presenting here at uh, QSC this year. And good luck. Thank you.

Alright, we're gonna take a break. We are still live in San Diego. We've got some more content, we've got a lot more guests to come.

Stay with us. You're watching Tech Drunk tv. This is Textron tv.

Hey everybody. Mitch Ashley here in Barcelona at Atlassian, team 24 Europe, talking to more great people. I'm joined by Dave Meyer, who is head of product with Jira.

Yes, I am. Okay. I really put emphasis on it, JIRA.

We'll talk about why I'm doing that in a minute. So tell us a little bit about yourself. You've been at Atlassian for a little while.

Maybe a little bit of your history with Atlassian, what you do as head of Jira. Yeah. I guess at this point, I'm a Atlassian lifer.

I about 12 and a half years, uh, in, I joined, uh, as a product marketing manager for our marketplace the week we launched it. Oh, really? Wow.

And spent several years working on the marketplace and developer relations. Moved to the Jira team, uh, spent four years as a PM on Jira. I took the opportunity to work on, uh, what is now Atlassian guard and a lot of our enterprise cloud readiness, uh, over the last few years as more and more of our customers have adopted cloud.

And then took an opportunity, uh, at the beginning of calendar year 2024 to, uh, rejoin Jira and, uh, take it into its, its next phase, which is a tremendous opportunity that I am feel very blessed to have. Well, it, it is an interesting time to, to be at the helm of Jira. You know, I was mentioning to you, I started using it pretty early on when Jira came out and, and just observation.

Jira has gone through so many transformations, you know, started as a ticketing system, started to do more development, uh, process work more, uh, Kanban, agile, DevOps, you know, kind of evolving then, then to be more of a workflow system. And now, you know, you're taking it cross organizational, so non-technical users. What does, what does that mean?

What, what, what, how does Jira need to evolve to be able to do that? Yeah. I love that you, uh, were able to trace the history like that, because I think one of the things that makes Jira unique and has allowed it to have such a long lifespan is that it was always a little bit more flexible, a little bit more accessible to different types of teams than whatever it was competing with at various things In its, uh, various points in its, uh, evolution.

Today we really see an opportunity to connect two different threads. On the one hand, uh, Atlassian is when we talk to our customers, they're looking for our help to solve bigger business problems for their organization. So it's not just, oh, we need to make our software development teams a little bit more efficient.

We need to get our operational processes a little stronger. It's, we need to deliver more business outcomes to our companies more efficiently, uh, and more effectively. And so we're looking at how can we solve that problem?

And we've realized, and this is, uh, underpinning a lot of our system of work philosophy, is bringing everybody into, uh, a single set of tools where we can share knowledge and, uh, collaborate on goals and plan and track work in one place. And then exogenous from Atlassian, there's, uh, trends among business teams become more sophisticated in how they track their work. And, uh, whether it's, uh, Trello or Monday or Asana, uh, you see more and more business teams getting away from spreadsheets and email as their project management finally.

Yes. Yes. And so, you know, those two things are coming together and there's a really, uh, we'd be crazy not to take this opportunity for, for Jira.

Well, you know, it is interesting. I, I think I heard, heard from Matt Schwimmer, uh, that, uh, you, you don't have to call it, uh, tickets anymore or issues. You can kind of call whatever that object, that work object is that fits your organization.

Sounds like a small thing, but people don't, it doesn't make sense to somebody who's not, it's not a ticket or it's not an issue if that's not the kind of work you do. Yeah, exactly. And we did, we do a lot of customer research, user experience, uh, investigation.

And at the end of the day, we know that there's a lot of folks that, you know, if they don't come from a software, technical product development, background issues, sound like you've had a problem. There's an old Atlassian t-shirt, uh, from the mid two thousands at Jira because you've got issues. And I think developers think that's funny.

But the average marketer in 2024, who's, Yeah, not much Doesn't get the joke. Yeah. And so we need to make sure that, and I don't think it's really gonna cost, uh, our developer audience anything if we start talking about work rather than issues.

Yeah. Interesting. So it, it's remarkable.

And I don't, I'm not just being nice and saying this, I mean, this, it's remarkable for a product to go through so many evolutions like Jira has, and still, you know, not only be standing but really well positioned for the next evolution of work. Uh, sometimes flexibility can be a curse, right? Because it can cause complexity, make it hard, make things more har harder to use 'cause of just too many things.

Why do you think JIRA's been able to kind of carve that balance to be able to continue to evolve? I guess I'll take that as a compliment. It's string a, That's a massive No, you've threaded the needle really well.

Why do you think JIRA's been able to do that? Uh, I ultimately, when I think about the fundamental problem that Jira is solving for our customers, it's work is complicated. Work is this, especially collaborative work among multiple people and organizations.

It's, and it's this ethereal concept of like, you're doing this because, uh, and those are the steps to do it. And this is the information we need to know about it. And what Jira solves for a customer is, uh, digital manifestation of that ethereal chaos of, of work.

And the thing we ultimately got right back in the early days of Jira was providing a core set of objects, uh, issue types, work types, uh, workflows, fields, screens, uh, and the collaborative, uh, elements on top of that that allow people to represent lots of different types of work in lots of different types of ways. And I think that people underestimate at their payroll the importance of not just saying, oh yeah, we've got, uh, tasks and we've got marketing blog posts launches, and we've got it operation incidents. Uh, but also being able to say, and we need to know different things about those different pieces of work.

And, uh, the definition of done for those things is wildly different. And so, uh, giving users the ability to kind of model those processes has been ultimately the key to our success. Interesting.

And the willingness to change And the willingness to change, right? Yeah. Not being stuck, no.

Has to be issues, you know, or whatever. Um, so there's been some changes to Jira. You've, uh, done some work on the user interface.

I don't know if a refresh is a good way to describe it. I think it's, you've done more work than Just, oh, we call it a refresh internally. Okay.

Alrighty. I didn't wanna underestimate what, but tell us what you said. Oh, we Actually called it make Jira pretty internally.

Okay. Well, I was, I was teasing somebody. I said, is it a facelift or a makeover?

Which, which did we do here? So tell us about what's changed in the user interface. Yeah.

Uh, so we started with probably our most fundamental usability problem in Jira, which is as we get more and more, uh, users into the system, uh, and there's more and more work being tracked there, it gets harder and harder to find where the work that you need to do is, uh, is located. And so, uh, na navigation was a, a natural starting point for us. Uh, and so we started with, uh, navigation across software projects and business projects and rolling it out to, uh, it projects and making sure that it's consistent not just internally within Jira, but across all of Atlassian.

And bring in some new functionality that helps users not just, uh, find their work faster, but also, uh, manage the complexity of Jira. So the ability to star the boards or projects that I work on most frequently. Uh, the ability to reorder and hide or remove, uh, elements in the product that you're, uh, not using or that you are using.

Um, and then we need to make sure as we try to reach a new generation of, of Jira users, uh, hopefully not literally, uh, we're only, we're only, uh, getting users under 25. Just keep it rolling in. Rolling In, right.

It's actually the opposite. The, there's a lot of folks out there that have a, a certain picture of Jira in their mind, and they, maybe the last time they looked at Jira was a decade ago. And so we need to make sure that Jira makes a great first impression.

And so there really is a genuine focus on can we make it more visually appealing? Can we make the colors better, the page facing layout better the fonts, uh, uh, feel different than what somebody might have thought of, of Jira and then make, uh, users feel more at home. And so there's little tweaks, like the ability to set a, a custom image as your board background.

Our Jira marketing team, uh, you know, we are working on our big marketing project over leading up to, uh, team Europe here today. And, uh, it was the middle of a Charlie XCX bra brat summer. And so the Jira marketing team's, uh, board is brat, summer green, brat green, which is tough on the eyes at first, but, uh, its like an adjusted over Apple green now.

No, Exactly. Um, but especially, you know, when you think about the Atlassian portfolio, we have a lot of folks coming over from Trello as they, uh, become more sophisticated in their project management. And so these are some of the most loved features in Trello.

And so we can bring them over to Jira, along with a great Trello to Jira importer, uh, to help make that transition more seamless. Interesting. Yeah, there's a lot of personalization that's been added to, i I label it that you can customize, right?

The nav in the left hand side and take things out. It doesn't apply to you, or it's almost like kind of your favorites or starred type things. So your work is right there as opposed to the mountain of things that might be happening across all the J users.

The sum total is we wanna make Jira more, more visual, more friendly, and ultimately, uh, more fun. If you move a, a task to done in a business project, you get, uh, confetti, uh, that drop Down, oh, there, now We have, uh, dropped 780 million pieces. You'd be Surprised how fat you wouldn't, We have a dance jacket.

Confetti is, people love it. Confetti flies, we can get something done. Work, Work should be fun.

Jira doesn't, JIRA can be serious about work without making people feel like J work is only serious. And, uh, and it's part of what we ultimately believe makes healthy collaborative teams. Let me ask you, uh, I don't know how to, how you, uh, how you would answer this question, but how do you add AI into, uh, the capabilities of agents and, and, uh, being able to create agents, uh, summarizing information?

How do you add AI into the interface so it doesn't look like, yeah, they kind slapped on AI into this and, but it's not really ai it's not that helpful. We'll wait till the real AI shows up someday. How do you, how do you build AI into a product like Jira One day at a time?

Yeah, One day, very carefully. Uh, yeah, we, we thought really hard about, uh, how can we layer progressively and more, uh, powerful use cases on top of it. So, you know, the, the first, uh, implementations were probably the low, what we'd consider the lowest hanging fruit.

So, alright, can we help you generate, uh, a better issue description? Uh, can we, uh, make some of the features of Jira that are harder for less power users to, uh, access like JQL, uh, for searching and creating automation rules and let users use natural language to, uh, generate those types of strongly typed, uh, information. And then we can say, okay, let's go to the next level.

Uh, can we break down work intelligently into smaller, uh, bite-sized pieces? Because we know from our teamwork research that work is more likely to get done if issues are well described, if they have all of the context and if they're broken down into, uh, uh, the right size, uh, issue to rationalize in your, your head. And then we can say, okay, how can we go farther?

Well, if we have all of that and we have well-described issues and we, uh, have them at the right size, then we're able to generate high quality code. If, if the, uh, story just says, build this new thing because it's important, uh, without any information on what the thing is, you're not gonna, the code generation is not gonna work particularly preach parent. Well, so if we can, uh, help connect the dots between, you know, if you have the spec for the, um, for the feature in Confluence or in, uh, Google Docs, and you have the information about historically, here's how we've broken down other epics of this level of, uh, complexity.

All those things can feed into a, a more intelligent, uh, JIRA that can ultimately solve the really hard problems like co-generation, uh, at a higher level of quality than other confi competitors in the market. Yeah, it seems like you've really picked specific thing, let's find something it can do for the user, not shiny objects to, you know, look, look compressive, but not really be that helpful. Yeah.

Uh, you know, we're talking a lot about goals, uh, this week. I can tell you that the, uh, Atlassian, the Jira AI team's goal is measured in amount of actual users, uh, using our AI features. So if they're, if people aren't actually using the AI capabilities in Jira, then they're not doing their job.

So click, click a lot on those AI features. Um, We're a little bit more sophisticated than, yeah, I'm Sorry. I am sure you are.

Um, one of the things I found interesting, um, I was in a session, we were talking with your CEO and president and, uh, found out that agents are actually users in the system. They're not scripts or some other odd thing kind of behind the, but they're actually agents that can be signed work, can assign work to other people. Because I was wondering about this idea of, um, you know, you have human teammates and AI teammates and how would that, what would that look like?

How are pe how do people receive that idea? The one of the users is, I don't know what the, what the name was in the demo this afternoon, but the name of some, uh, some agent Issue organizer, agent Dave, or you probably need to give them better brands. Uh, the, oh, the Guardian, uh, guardian brand.

Guardian Guardian, yes. Brand guardian. That, that said a lot right there, but yes.

Uh, well, even in a, uh, phrasing like brand guardian, uh, there's an implicit, uh, connotation with guardian around safety, right? And so I think we'll be pretty intentional, at least in the Atlassian, uh, provided agents around kind of using branding to help, uh, our customers feel like it's a more natural experience. I think it'll take, uh, time for it to feel normal.

And then as you know, you saw the customer quote during the keynote today, it's hard to imagine you can't living without it. So, uh, you start to use it and then you can't imagine doing it any other way. And that's true.

Whether it's a agents in, uh, VO or, you know, I've worked at Atlassian for 12 years. We've had Loom at Atlassian for two and a half. I cannot remember what life was like before Loom.

And honestly now I can't remember what it was like to have Loom without Loom AI to give me a transcript to edit all of my, uh, ums. As I say, uh, I, I had a funny experience. Um, our who's now our CEO, when she first started, she was working on Trello and uh, and Slack and some other things.

And we had a system account, and we called it Sbot. A lot of people didn't know it was her. They thought it was an automated something, and then they found out Sbot was Kim.

They were shocked. They didn't know there was a real person. So as long as you don't name her agents, you know, Bob's your uncle or something, some human name, you could, Could build your agent, name it whatever you want, you can name it whatever You want.

Be careful. You might confuse people. We'll see how it goes and see if we need to put in place some restrictions.

There You go. So one of the things that can happen is as you evolve a product going after new customers, new market, whatever it might be, the current, the people who help you get there, the developers, the ops folks, ITSM, you don't want them to say, Hey, what happened to us? You know, you've changed things and you forgot about us.

How do you keep, how do you keep the generations of of users that you know, know, love, working Jira every day happy, as well as being able to support the new, new kind of users? Yeah, that's a great question. We, the message that I, uh, preach to the Jira team is we are building Jira.

We haven't moved from building Jira from technical teams to business teams. We are building Jira for all teams. And ultimately that's in Atlassian's DNA from day one.

Confluence was built for all teams. Loom is built for all teams. Robo is built for all teams.

So Atlassian knows how to build project products for every type of, uh, team and a product and feature set that's universally, uh, appealing. And so our goal is not to only build for a non-technical, uh, user, but to build for everybody. Uh, and the features that we're investing in most heavily are things that we do think are going to, uh, benefit not just the non-technical audience, but that the technical audience is gonna love too.

Like technical users want Jira to feel fresh and modern and easy to use and be able to find their work faster. And, uh, we rolled out, uh, our list view, uh, first in our Jira work management product, but then in Jira for software projects as well. And we're seeing tremendous adoption of the list view, uh, among software teams as well.

So, uh, I think technical teams are benefiting just as much from these investments as the business teams are. Well, it's a heavy responsibility being in charge of Jira, but I think they got the right person. It's a tremendous privilege.

Oh yeah. It's a big for, it is. There aren't a lot of software products that are still kicking after 22 years.

I know. It's, it's, uh, pretty awesome. Well, wish you all the best and, uh, as we continue to move Jira into the future and doing new things with new users as well as old things with old users, all give above.

Right? So thank you. It's been great talking with Dave Meyer, who's the head of product for Jira.

Like I said, JIRA. Now you know why I said Jira because it's, it's, it's, it is kind of the center of the universe that in Confluence within Atlassian. So thanks Dave.

Take care. We'll be back with another great, great interview with another great person. Hello and welcome.

My name's Steven Dickens and you join us here for a six five Media at BMC Connect. I'm joined by Josh and Ryan today. Hey guys, welcome to the show.

Nice. Thanks for having us. So Let's dive straight in.

Tell us a little bit about what you do for BMC and what you do for C Coors Tech. Alright, yeah. Uh, well for Coors Tech, uh, senior manager of corporate applications, so oversee our, our AI program and, um, other applications as well.

And that kind of plays into what we're trying to do. Yeah, fantastic. Ryan Manning, uh, VP of product Management at BMCI support our Helix products, so service management and also our Helix GPT product.

So everything's gotta have a GPT. That's right. It's 2024.

It's compulsory. Right. So Ryan, let's go to you first.

How are you seeing AG agentic AI transform? What's going on in enterprise? I, it just coming from the keynote lots going on there, hearing about it from clients, from the industry as a whole.

What are you hearing? Yeah, I mean I think it's all about user experience. I think that that's probably what sets AI apart from previous platform shifts.

Mm-Hmm, right? Like cloud and mobile. I mean, I think it's open season on UX paradigms.

We feel that this technology is gonna change the way we interact with software in our personal and our professional lives. And when it comes to professional lives and enterprise software, we sort of believe that oftentimes the best UI is no ui. And so we're, we're really, we're, we have an internal goal of eliminating about 60% of our U user interface in favor of these conversational experiences.

'cause we want to give these personal digital assistance to all of our power users. So, I mean, that's the upside I think everybody can see that vision. What are some of those challenges that you are hearing as you are chatting to customers?

What are those adoption challenges? What are some of the barriers that you are seeing? Yeah, yeah.

The two we're two we're focused on two right now. And the first is data. Uh, you can't have an AI strategy without a data strategy.

A lot of our customers are thinking about data governance, data security, data centricity, data assurance. And so we're doing a variety of things to help them on their journey. Partnering with data cloud vendors like Snowflake is one.

And we're also building agents to help this, like the Helix GPT Knowledge curator will help knowledge managers keep the knowledge base up to date and relevant. The other trend we're looking at are a challenge here is the deployment model for ai. 'cause we don't know what that's gonna look like yet.

And you know, we do know a few things. We know existing regulations are heightened new regulations around the corner and data. A lot of our customers are think about monetizing their own data in the next 12 to 24 months and we're gonna be ready for that.

'cause the Helix platform is more portable than any other enterprise software platform out there on premise, SaaS, public cloud, private cloud, and soon the Snowflake data cloud. So what are you seeing as some of those competitive advantages? We joked a little earlier, it's a busy noisy market.

Lots of people are talking about agen ai. Yeah. What's that BMC competitive advantage in this market?

Yeah, Our deployability is definitely at the forefront, but i I kind of, it's akin to the Apple and Microsoft wars of years and years ago. Our, our competitors taking a very command and control closed approach to ai. So they want you in their data centers using their Gen AI services and they want you to move all your data into their data cloud, right?

Our customers are, are, are, are liking our open approach. We're almost taking an anti platform approach here where we wanna work with the tools you're either already using or just about to start using. And Josh is gonna talk to you about that in a moment here.

Yeah. Bring you in Josh. Sure.

What obviously BM C'S making some great strides with the technology, but how are course tech deploying that? What are you seeing? Tell us a little bit about some of the deployment models that you are seeing and how you are leveraging it within your business.

Yeah, so our actual idea is a, an a chat bot for not just, um, interacting with BMC but interacting with all of our corporate applications. So we want a one stop AI interface where our end users can chat with one AI chat bot and interact with whatever application they need to. And the chat bot can make that decision for them.

Even so, they may need to record a new lead in Salesforce or they want schedule a meeting or they want to take time off. Um, they just ask the chat bot and we really think that's gonna change the world. I, you, you took my talking point.

Uh, I think software, uh, interaction and ux, um, and GUIs are, are gonna become far less important over the next few years because Agent X is gonna take over and really I think it's gonna change os um, if you don't have an agent in your OS that can work with other agents, you're not gonna be competitive. So how would you quickly summarize some of those benefits? We talked about the technology there.

What are those key advantages and how's the BMC Helix software impacting where course tech is going? Um, well, the openness, uh, and the, the data. So one of our ideas is the, the, the security should sit within the software.

It's already in. So my Workday security is already in Workday. I don't need to move that data and rebuild that security somewhere else.

I'm just going to access it via a chatbot. BMCs approach is, uh, in line with that as opposed to he mentioned someone else, uh, that wants to take your data. We're not interested in putting our data anywhere else.

We want to keep it where it is. I think that's a great story as we start to bring it home here. What are you seeing as that future view?

If you were looking ahead, maybe we're taking a 1218 month, 24 month view. What are you seeing as that future for Agent ai? Yeah, I mean, it's, for us, it's a fleet of AI agents across the service ops lifecycle.

I talked about Knowledge curator, CMDB auditor is another agent we're building. We have, uh, we're building Helix GPT Insight Finder. So no more kinda waiting in line to see your local data scientists because executives and managers can converse with that agent to get meaningful analytics without knowing how to, how to write SQL queries.

So every single role in the IT operations landscape we want to have an agent for, um, that's our, that's our vision. I Think that's a fantastically broad vision. It's been fantastic getting that view of how it's impacting where you are going with course tech.

Thank you for sharing. You've been watching another episode of the six five Media coming to you live from BMC Connect. Please click and subscribe and check out all those other episodes and we'll see you again next time.

Thank you very much for watching. Hello and welcome to another episode of six five on the road at BMC Connect. I'm your host Steven Dickens, and I'm joined by Mark and Anthony from BMC.

Hey guys, welcome to the show. Thanks for having us, Steve. Thank you.

So tell the listeners and viewers what you do for BMC. Let's get started. Let's start with you an Anthony.

So I'm a DevOps architect and evangelist for BMC. So my role is I go and talk to companies about what's the art of the possible, what can they do with the BMC suite of tools and how can they, how can they make working a delight? That's, that's really what I do.

I guess I'm a, a seller of happiness, Steve, a seller of happiness. I love that. And you mo you've got a lot to follow up with.

I I know you're A seller of happiness. Come on. You gotta tell That.

Yeah. Well, I'm the lead, uh, product manager and I work with Code Pipeline, um, code Insights and abate, and I'm focused on the developer experience. Anything I can do, well, a seller of happiness, but to work towards making their lives better and things easier for those developers.

So, topic of the session today, we're talking about it, a little about it off camera. These are systems of record. Mm-Hmm.

They're vital to the business. They typically are the business for most of the shops that we're talking about. So what challenges do you see those teams having?

The fear of change? Mm-Hmm. We are talking about it off camera.

A lot of this code's critical, mission critical running the business. How are you seeing that manifested? Is it cobalt's fault?

Is that just the way the system structure? We'll go to you first, Anthony. Yeah, I I, I think well, so to start on the COBOLT part, I think COBOL gets a bad rap.

I'm with you there. I think that COBOL is a fine language. I think everybody wants to put all the sins of the past into cobol.

For me it's French versus German versus English. Right? It's a language, right?

Yeah. You learn if as long as you understand your patterns and how to basically engineer a system, whether I'm in go or I'm in COBOL or I'm in Node or I'm in whatever, it, it, you're, you're creating something, right? Yeah.

The choice of clay shouldn't matter. So I like that. I've not heard that one.

I'm gonna steal that one. The, uh, you, you, I give you rights to it. The, uh, but I think the fear of change thing, you know, a a lot of companies, these are their critical systems.

These are the core of their business. This is where the money's at. And they're scared to make changes because the people that used to understand these systems backwards and forwards are leaving.

And frankly, it's no way to live it. You can't be scared of your system and your technology. You need to dictate to your technology, don't have the technology dictate to you how you do things.

And I think that's, you know, just frankly, I think that's where BMC comes in and helps companies bridge that skills gap that they have between the next generation of people coming in and the ones that left. So, you know, what I'm hearing a lot is, you know, how do I manage this because companies can't stay pat because these systems are too important. Mm-Hmm.

But they can't just blindly make changes and, you know, hope it works. They have to be, uh, cognizant of what they're doing. So coming to you, mark, if that's the vision and that's where customers are, how are you engineering the BMC products to kind of address that vision?

Right. As I said, I look at that developer experience and I live with that and that fear they have, and I understand it because I was a developer. Yeah.

So we look at ways that I could basically do magic. Could I run the application and have it magically just chart it out and it showed exactly all the database calls and program calls, all that. Could I have the code charted out so I can take a complex section of code and chart it out and say, oh, this does this and this does this.

It's taking what could be hundreds of thousands of lines of code for millions and making it clear and concrete in a visual way. And now with text, because some people are visual like myself and other people have to have it described. So you've gotta have a UI that adapts, Right.

And we do it many different ways to work with how that developer likes to understand the code. So guys, it's 2024, we're four minutes in and we've not talked about ai. It's, yeah, I mean this is shocked gonna be a record somewhere.

I'm shocked. It's gotta be. So all joking aside, we're starting to see chain AI come across the entire developer landscape code assistance.

We're seeing that come holistically across. How are you seeing that coming particularly into the mainframe? I'll go to you first, Anthony.

How are you seeing that with some of the customers? And then we'll go to you, mark, around how you're embedding it into some of the solutions. Sure.

So I think the mainframe is leaning into AI even more. Um, uh, even more than you're seeing on some of your distributed and cloud systems. I think the mainframe is really looking at AI as a tool that can be used in the future to help with what Mark was just describing, which is, I have millions of lines of code that I need to understand, that I need to parse.

And even if the guy who wrote it back in the day is still there, it's now become so complex and had so many layers put on top of it that even they probably don't understand. And Mark, that's the key point. We've seen the B-M-C-A-E assistant announcements while we're here at the show.

I think from a product point of view, and Anthony touched on it, that explanation of code, maybe that original developer's not there, maybe it's coming to a news team. Talk to me a little bit about how you've seen that from a product point of view. Right.

And that's something where, like I said, we've had the charting and we would have tables and we'd have ways to explain it, which we've had great success, but for the last like 20 years, I've had developers say, could you explain to me what it does? Yeah. And I would kind of laugh at that because that's impossible.

How could you go in and explain it? But in the last, In the answer lines of cobol, pull apart that and understand And tell me the business Yeah. You know, logic, what it is and how it works.

Impossible. And those people have typically left the organization. Yeah.

Yeah. The people who wrote the code 30 years ago have gone, I need that quick summary of what it does, and then it's over the last two years, last year, it was like AI was the solution. So we didn't take AI and say, oh, what can it do?

We started with that problem and that was the one remaining problem I had. And AI magically can deliver what they've asked for. Well, I think, mark, that's a fantastic way for us to wrap up AI magically doing what customers asked for.

I've not got a better way to wrap up this. Guys, it's been great having you on the show as always. As always, you very much thank you.

You've been watching another episode of six five on the Road, coming to you from BMC Connect. Please click and subscribe and do all those things for the algorithm. Check out the additional content from the show and we'll see you next time.

Thank you very much for watching. Hello everybody. I'm Mike Biard, and we're back at six five on the road, and we're at the BMC Connect event in Las Vegas at the lovely Fountain Blue Hotel.

And we're here with my old friend Rah, and we're gonna talk about DataOps Rahm, welcome to the show. Thanks, Mike. Always a pleasure to, uh, have a chat with you.

There was this thing that happened a couple last year. It was this AI was everywhere. It's a bright new shiny object.

I think this year we kind of figured out it doesn't really work without data. Absolutely. So, is DataOps kind of gonna be the new place where the cool kids are hanging out because the AI model isn't worth much without data?

I, I think it's a fair statement. I mean, I'll go back to what I said in last year's Connect events. AI and data are in a cosmic dance where one without the other does not make any sense.

One enables the other, they feed off of each other and jointly create value. So you cannot talk about one or the other in isolation. So specifically your question about data ops.

Data ops is all about operationalizing your data management and data analytics use cases. It's great to use technology, but unless you can operationalize those use cases, you're not gonna be able to get value from them. That's the premise behind data ops.

So it's a must have for ai. You and I have been talking about data management as long as I can remember What guilty as charged. What is it about data ops that's different than what we used to think of as data management in the first place?

Great question. I mean, if you look at traditional data management, it's the collection of data, uh, ingestion, integration, storage, analytics, visualization, all those things. What it doesn't account for is how do you get a handle from source to insights and keep everything in place such that if there is any break or flaw in the continuum from source to insights, how do you manage that?

How do you mitigate the risk? And that's the key to operationalizing those use cases. So when you deploy in a production environment, you want the rigor and discipline and enterprise scale resilience.

And that's where Data Pipeline Orchestration comes in as an enabler of data ops. And with Control M, which is our, uh, industry leading solution in that space. And Helix Control M, which is its SaaS counterpart, we have the best data pipeline, orchestration solutions, bar none, uh, for Enterprise Gate.

Great customers. I also saw this new kind of data assurance tool that you guys are showing. And what is that exactly?

Yeah. So we, it had its genesis in the BNC innovation labs, which I established and I oversee. And as what we do in the labs is we solicit ideas from customers.

We, uh, look for their pain points, their evolving needs, future strategy and direction. And we take a subset of those, synthesize them into ideas. And this was one that came from such conversations.

So data assurance, think of it as a compliment to data pipeline orchestration builds on the no so notion of business data observability. So think about applying observability concepts to analytics pipelines that through which data traverses. So if you can get a handle on the health and performance of data as it traverses complex pipelines and institute course corrections before the proverbial, uh, uh, things go hit the ceiling, then you're in a good place.

So that's the premise behind, uh, data assurance. Managing all that data is complex endeavor. Mm-Hmm.

Are we gonna get to the point where I'm gonna have something that feels like an AI agent to help me manage the data that I'm using to go build AI models? Uh, that's a great question, Mike. I mean, I think the short of it is AI and generative AI can be applied to pretty much every use case.

But the trade off is what is the value? What is the burden of implementation? Are you gonna get a return on investment?

What are the associated risks? Needless to say, uh, control M is ripe for, uh, hard ripe for augmentation with, uh, generative AI and ai. And that's definitely a key focus area for us, uh, to build on top of our existing automation capabilities with generative AI based, uh, workflows.

Uh, we are working on another cool one, which, uh, exploits metadata using generative AI to provide previously unavailable insights to business services. So those are many of the things that we are looking at with the power of AI and gen ai. Again, it's AI feeds data, data feeds the AI together.

They are in a cosmic dance. And that's kind of how we're gonna operationalize all this. 'cause one of the things you hear is that we don't have enough data engineers, but then the next logical question is, is, well, do we need a data engineer for everything or can we democratize this to the point where mere mortals can do this?

I absolutely think it can be democratized. Uh, if we can institute, uh, code generation capabilities, uh, on traditional workflows for data specific workflows, you start with 50, 60% of the code or 70% of the code, you, your dependence on data engineers suddenly goes down. Don't get me wrong, we are never going to eliminate those people because the insights and the experience that they bring, the human touch is gonna be continue to be fundamentally important.

But you can alleviate your own talent shortage, uh, uh, challenges with augmenting, uh, by augmenting the Gen ai. Do you think maybe the way it teams are organized is gonna change in the future? Because right now it takes a village to do anything.

Yeah, and we don't have a village. So, uh, I, I mean, uh, change is inevitable as the cliche goes, right? So it's absolutely gonna happen.

In fact, it's already happening, uh, even within our own organization, uh, in our IT organization run by our CIO there, there re marshaling and pivoting resources and augmenting with gen AI so that the productivity gains are faster and the time to bring to market solutions stick, uh, solutions is that much fast, uh, that much shorter. So there are lots of folks out there and they're trying to figure out what their job is gonna be and how it's gonna evolve and what is their role gonna be. I don't think anybody's gonna be replaced, per se, but what I was doing yesterday is not what I'm gonna be doing tomorrow.

Yeah. The, the nature of our work is going to change. The work itself is not gonna go away.

We are going to evolve and we are evolving towards doing higher value added work as opposed to a traditional, uh, types of work that we've become accustomed to over the last few years. So I think it, in some ways, it is, uh, the techies version of an Iron Man coder. That's how I would look at it.

Are we gonna have a better understanding of what data is valuable? And I ask this question because a lot of times it people, they manage data, but it's often all the same to them. We now have data that is unstructured, semi-structured, structured.

Are we gonna be able to kinda ascertain which data has more value to drive into these AI models? That, That's a great question. So there's multiple, okay.

There's multiple dimensions to your question. Number one, you need to know that you're using the right data and the authorized data for each use case. Even something as simple as is this data, uh, authorized for public use in a or some other kind of, uh, broader use in training and AI model?

That's one set of considerations. But beyond that, specific to your question is it's not just about the business data. It's about the data about the data, which is otherwise called the metadata.

This is data about code, about configuration, lineage, versioning, and so much more. This is pretty complex and dispersed across the technology layers, but you can use generative AI to link it to business services, understand interdependencies between your business, uh, data, your business services and data about the data, and that can provide killer insights and give you outcomes that were previously not possible. So absolutely doable.

Where is this going? All right, folks. You heard it here.

It's all about the data at the end of the day. And if you really want a job in ai, think about being in the data management side of the equation. You've been watching six five on the road here in Las Vegas.

We'll be back with some more episodes. By all means, check them out rom thanks for being on the Show, Mike. Always a pleasure.

I'll leave you with this. Data is the fuel that powers ai. Alright, thank you.

Back in a minute. Hello. Welcome.

I'm Stephen Dickens and I'm joined by my host Mike Ard. Yep. We are coming to you from BMC Connect and this is a six five on the road.

We're joined by Anthony and my dear friend, Dave Jeffries. Hey guys, welcome to the show. So let's get started.

Tell the viewers and listeners a little bit about what you do for BMC. So I'm, uh, responsible for really all the aspects of r and d. Um, whether it's the, the products that we've had for a number of years or whether it's late and breaking Amy platform, Amy assistant.

It's a fantastic place to be. So we, we just drive innovation and that's, uh, it's my team worldwide that do that. That's not a bad way to describe your job, Dave driving innovation.

Anthony, you So I'm a architect for Amy platform and Amy assistant and an AI evangelist. Uh, go out and talk to many customers about what We're, is that a cool job title for This year? Yes.

That just appended right onto my, uh, title. Fantastic. So you took us there, guys in your introductions with generative ai.

We're here at Connect this week, just come off the keynotes, lots of focus on generative AI assistance. Kinda where are you seeing that kind of fit within the DevOps and the sort of space? I'll go to you first day, Right.

So I I we've done some fantastic announcements today. Mm-Hmm. Alright.

And, and if you haven't seen them, it's all, all about amu assistant team platform and bringing generative AI to where we think it's needed the most. Mm-Hmm. And obviously a lot of people talk about skills gaps and skills attritions on, on the mainframe.

Um, but you have to think, you know, why, why is that an issue? Why is, is the skills challenge an issue on the mainframe? Because surely you know, it's going the way the dodo maybe or something like that?

Well, in reality, it's completely the opposite. The mainframe's got an entirely new lease alive. Um, those skills are probably an inhibitor, or the lack of those skills are an inhibitor to transformation.

People want the mainframe to do some new cool stuff because it's got fantastic new technology in there. And we think generative AI is, is providing really that key to unlock what applications do. So therefore, you know, reducing the risk of changing those applications, what your systems do, how your systems into operate, et cetera.

And it's allowing you to, to unleash transformation, which is bringing, you know, a whole new realm of possibilities to the platform and, and how the platform can support business. Bringing New people into the platform. Yeah.

Enabling them to get started faster. Yeah. And it's making, I think the guys who are already there, guys and gals are already there helping them as well to, to innovate because sometimes, you know, you might be the last one there and you might be struggling in terms of, you know, the, the scale of the, of the challenge in front of you.

You need some help to go do it. And I think it's not just unleashing the next generation of talent, it's unleashing the talent that already exists, which is important. Exactly.

You touched the customers a lot. Yes. What are some of the examples that people are actually using here?

Because I think we talk a lot of theory with ai, but, you know, what are we actually seeing? Where is, where is the manual effort in the scut work disappearing? Yeah, so, um, it, that's a really good, interesting question.

So from different customers, it means different things to them. So a lot of them, it's, uh, capturing that tribal knowledge. Let, let's just start there.

So before they even go down the road of, you know, AI or generative ai, they have to take that step back and see what does it mean to their business. And if you just look at some of the tooling that's out there today, currently, when it comes to generative ai, it's very agnostic, right? It really doesn't mean anything specifically to a customer and their wants and their needs.

So the first thing that we had to do is take a step back and say, well, how do we infuse our generative AI with the knowledge from a customer's environment, first of all to make that AI relevant to them to address their wants, their needs, their business direction. 'cause that's where, you know, uh, I think you use work skunkworks. What, what it, when it comes to that, well, how do we make it a reality?

How do we make it relevant for the customers? So we built our platform of generative AI services in a way that it's open and customers can infuse it with knowledge that they need and then start applying it to things in the DevOps space, in the, uh, AI ops space specifically around, uh, you know, what they're trying to get out of, uh, improvements in their, in their business with generative ai, making it relevant in context for them. So I can customize it and it meets me where I am versus me being exactly.

Being forced to do something. That's Right. So you don't want to ever leave your experience, your environment where you tooling to jump out into another, you know, platform or another tooling.

'cause that's where you lose what we call context and the relevance of what it means to you. So we like to say we meet the customers where they are in our product experiences with that context, with the understanding, and you get far more at generative AI and much better results that are, again, relevant for you and your business as you move forward. So Dave, key word there from Anthony was spec specific, making this specific for the particular shop, the operators Yeah.

The developers. Can you just kind of double click on that as a phrase and what that means from a specificity, you know, contextualize it, what am I gonna be actually doing with a assistant or some of the a, a AI ops stuff, right? And, and how that's gonna Work.

So I think one of the key aspects of all this is, and, and everybody's, you know, they've approached generative. There's, there's a world and a plethora of LLMs out there, large language models. And we're, we're starting to see some language models being really good at certain things in certain areas.

Some are good at codes, some are good at other things, et cetera. So as Anthony was talking about, you know, one of our kind of think core traits that we bring to the, to the platform and to our solution is allow you to take the right LLM for the right use and then obviously infusing it with your own information. So that's how you get in that specificity.

Oh, absolutely. And then you can apply it to the code world. Yeah.

We talk a lot about generative AI in code, in terms of understanding code, but there's more than just code that runs the business. There's the infrastructure, there's the configuration, there's the environment. And so not just applying generative AI to understanding what a COBAL application does or what a an assembler application does, but what does the recs do?

What does the JCL do? Mm-Hmm. What does, um, what does the, you know, the, the expert back at base do in terms of how he resolves a particular situation that may appear in operations.

Exactly. So bringing that subject matter expert to a wide variety of areas involves multiple kind of tribal knowledge, pockets being pulled together, multiple lms, being able to be used for the right reason and the right purpose at the right time. I think explaining code is great.

'cause a lot of folks, they didn't document it in the first place. Yeah. So they don't really know how it works.

But how do we go to the next level? Because I think what we're moving now towards is realtime insights that are gonna be surfaced as I'm trying to perform a task. And Yes.

Yeah. So we're on this journey, but it seems like there's multiple phases. What are they?

There is. So, uh, the way we are looking at the spectrum right now, and, you know, we started off in the area of what we all started to experience, like with chat GPT, right? It was the chat experience.

So we go out there, we, we dump, dump questions over, right? Uh, how to, what is, how can I type questions to chat GPT. That's how we all started.

So we also started that way with infusion within our products where it made sense. But the spectrum now, we're looking much wider, much far beyond just the chat experience moving to towards something called agent or agent AI or AI agents. And what we want to do there is really move towards more autonomy with our generative AI solutions, but also the point of hyper focusing in our particular product areas with AI agents to be super experienced, super knowledgeable, super capable within a given product area like AIOps, SecOps to do things like, uh, the automation.

Let's just take automation for, we have a lot of mundane tasks that we deal with day in and day out, right? If even if you look at us individually, uh, there's a lot we can start looking at for generative AI in our own lives to simplify things, nevermind in the business world. So we have these AI agents now that are focused in our product areas to allow customers to automate mundane tasks, to surface insights automatically.

What do we really want to do here? It's not that we want to make, uh, we want to, we want to take the cognitive load off folks with generative ai. 'cause we want them to focus on more important business issues.

We want them to focus on innovation. They wanna innovate just as much as we do. So how much can we help them with utilizing generative AI to push them to that journey?

Anthony, I think you said it well. We want them to focus on the good stuff. Exactly.

Much less on the operation stuff. What a great way to wrap. You've been watching us here on the six five coming to you live from Connect with BMC.

I've been your host, Steven Nickens, joined as always by my dear friend Mike Ard. Please click and subscribe and check out to the other episodes and we'll see you next time. Thank you very much for watching.

Hello. Welcome to another episode of the six five on the Road, coming to you from BMC Connect. My name's Steven Dickens and I'm joined by my co-host here, Mike Ard.

And we've got Dave Jeffries and Priya Doty on the show. Welcome to the show. Thank.

Thank You Steve. Thank you. Thank you, Mike.

So let's get started. Tell us a little bit about your role and what you do. We'll go to you first, Priya?

Yeah, Sure. Uh, Priya Doty. I am vice President for Solutions Marketing for BMCs Amy Portfolio.

So Dave Jefferies, I'm the Vice President of research and development for BMCs Amy portfolio. So I've gotta ask Mm-Hmm, I hear it a lot. We've heard it this week.

Amy Priya. What does that mean from the way you are structured internally within BMC, but also what does that mean as it manifests itself for your clients? Yeah.

Well, Amy is a MI, which stands for Automated Mainframe Intelligence. And actually we created that brand back in 2019 before my time here. And, but what I love about that brand is it was, it was started around mainframe software and making it, you know, more intelligent using the latest, uh, AI ML technologies at the time.

Fast forward now, five years later to 2024, and Amy still has so much relevance, right? Because it's Ask Amy, ask Amy Assistant with Gen ai. So we're continuing to talk about our mainframes, our mainframe solutions, our mainframe software as BMC Amy.

So you touched on it there, and I'll take you there first, Dave, Amy, as a platform, we've seen a lot of announcements this week, Amy, assistant, there's the stuff going on in the DevOps space, this is stuff in the AIOps space. Can you just tell me and help the viewers with platform versus assistant and what kind of the delineation points between Those are? Absolutely.

Absolutely. Steve. So Amy platform that we announced, uh, statement and direction in in July is, is a big area.

It encapsulates many different aspects of really treating the platform as kind of a cloud native way. One of those features and capabilities is our generative AI services. And generative AI services is infusing the a e assistant.

A e assistant is the gen AI capability that will assist you infused in every one of the products on your journey through operations, through security, through data, through Dev X, et cetera. But AMI platform itself is a whole set of services. genai is just one of them.

You can imagine things like discovery services, data services, feeding, restful services, et cetera, um, authentication services. There's a whole bunch more to come. This is really just the first piece of the entire platform starting to be deliver.

So If we think of platform as the overarching and then the assistance plugging into it, is that kind of a way to frame It? Yeah, absolutely. Good way of thinking about it.

Okay. So in my mind, and I like to imagine things, but there's all these retired main framers. There're sitting on a beach having a mitie right now, but it's creating a little bit of a knowledge gap in these companies.

So how do we close that with ai? Yeah, great question, Mike. And so it is true, the market reality is what is really driving this announcement of Amy platform and Amy assistant.

And the market reality is that you can no longer be a caretaker of the mainframe platform. That's not me. That's Garner.

There's stat that basically says that by 2030 you have to make a decision and the companies that don't make a decision to start to modernize in some way will actually incur higher costs, 50% higher costs. So what does that mean for that, that my tide drinking retired mainframer, um, what that means is that person is walking out onto the beach with a lot of institutional knowledge and that institutional knowledge has to be preserved by these companies. Our customers are frequently regulated.

They are financial institutions, healthcare institutions, insurance, telecom, they require continuity of service. So I think of it as a way to hand over the keys to the next generation of the people who will really be managing the platform. So Dave, picking up on Priya's point, we've got this demographic change.

You know, I've heard anecdotally takes five years to grow a cis prog, people are looking to collapse that down to two with some of these generative AI platforms. How do we, and we talked about it earlier, this path to automation, sort of looking at some of these mundane tasks, taking out the toil. If you've got these experts, you don't want them focused on the sort of minutia, you want them focused on the bigger, more architectural issues.

How's the platform and where you are going with some of the assistance as well gonna bring value There? Well, I think one of the first things to bring to that, the whole Amy platform and specifically Gen a l bring, is simplicity, is is kind of removal of the risk in people doing automation. Because automation is the ultimate goal.

One of the buzzwords faster than humanly possible has been used this week quite a lot. Um, and it really is trying to take what customers are doing with application development. It they're to allow them to go at the speed of the business to be as agile as possible, as rapid as possible in the changes they're trying to make.

And you take something like on the operations side, the AI ops side, you wanna be able to trap those problems within your system as rapidly as possible before they become, uh, maybe a, a a an issue or an inhibitor in, in production. So taking days to minutes to seconds, et cetera, and even getting proactive beyond even having noting you've had a problem automation to Take over ultimately. So back in the day when main framers were young, there was a band called Pure Prairie League, and they had a hit song called Amy, and the refrain was, Hey Amy, what you wanna do?

So there's my question to you. What does Amy wanna do? What does Amy want win the award for question the build up to that one was fantastic.

That Was brilliant. Um, yeah. So what does Amy wanna do?

Amy wants to make teams more productive, right? So, uh, Dave mentioned days to minutes, that's reality. Um, Amy assistant that we announced with within Dev X for Code Insights, which is a visualization and code modernization tool, there is code explanation now, which means if you're looking at COBOL code, maybe it's something you've never seen before, you can actually figure out what that code is almost instantaneously and then actually comment it back.

Uh, we're also thinking about the ops domain, like Dave mentioned with, uh, ops insight and how to add more explanation. But it's not just explanation. That's one piece of it that is around, uh, sort of guiding.

We're also thinking about how we can use gen AI to do creation, whether that's of code of queries, anything like that. And we're thinking about how we can use ultimately gen AI to do automation and more and deeper automation. And as Dave mentioned, we're thinking about it across the stack.

So it's all of the different pillars and and points. So what does Amy wanna do? At the end of the day, Amy wants to make the current generation of main framers be able to take their vacations and go out on the beach and hang out.

Well, on that, what a great way to wrap up. Amy has a platform trying to get us all to sit live our best lives on the beach. You've been watching another episode of six five on the road this time.

We've been coming to you from BMC Connect. Please click and subscribe and check out the other episodes and we'll see you next time. Thank you very much for watching.

Hi everyone. I'm Keith Kirkpatrick, research director with the Futurum Group. I'd like to welcome you to Enterprising Insights.

It's our weekly podcast that explores the latest developments in the enterprise software market and the technologies that underpin these platforms, applications and tools, tools. This week I'd like to talk about two events that I attended. Smartsheet Engage in Seattle and Zendesk's AI Summit in New York.

Now, while there are both very different events on the surface, they're actually a lot more similar when you really start to dig a little bit deeper terms of the underlying themes for both events. Then once I've covered that, I'll get into my rent or raise segment, which is where I pick one item in the market, and I will either champion it or criticize it. So without further ado, let's get started.

So, uh, first event that I went to this week was Smartsheets Engage, uh, which is their user conference that takes place up in beautiful Seattle, Washington. Now, at this event, uh, there's really a couple things going on. First of all, the, the biggest news that actually happened before the conference itself was that Smartsheet was actually made the announcement that it intends to go private, uh, essentially delisting from public markets and, uh, once again, being owned by private equity.

Now, initially one might think that that is a sign of trouble because, you know, generally speaking, you don't see companies deciding to go private unless there are some issues, uh, where you know, either internally there might be something going on in terms of the overall health of the company, or perhaps there is some massive problem in terms of, uh, you know, uh, strategy or product issues. I don't believe that is the case. Uh, mark Mader, the CEO, came down and talked to us at the analyst conference and at the event itself.

And, uh, for my money, I, I believe that the company is on the right trajectory. And really this move is just a way to make sure that the company has resources to continue investing heavily into the platform and into ai. Let's be honest here.

Uh, AI innovations, they're not inexpensive. They requires quite a bit of work in terms of trying to make sure that there is enough functionality in the platform to keep all of the customers happy. Uh, given the fact that, you know, if you look at most of the companies in the space, uh, they're rolling out new products and features all the time, you cannot do that if you don't have the capital to keep funding that, uh, those developments, uh, projects.

So, uh, with that said, uh, there are a few other things that were really kind of going on at this event, uh, that I think were pretty notable. Uh, of course, the, the, uh, the other sort of main sort of, uh, you know, focus is that Smartsheet is rolling out some new functional updates and new features on its platform. Uh, obviously a lot of this revolves around generative ai.

Uh, one of the main sort of features that, that was really kind of, uh, pretty cool, and I actually talked to a lot of customers about it, is this feature where they can, where the user can use generative ai, uh, to go from sort of natural language to an actual, uh, formula with NSL. And, and then the reason that's important is if you think about the way people work, particularly if you think about knowledge workers, project workers, which are the ones who really use Smartsheet, they are not generally speaking experts when it comes to putting in, uh, functions or, or formulas within sheets. That's just, you know, if anyone has used any kind of, uh, spreadsheet tool that's for, for example, they know that, you know, remembering formulas the exact format, that's a lot of work.

And more importantly, sometimes it's not about even just remembering the formula, it's remembering kind of, you know, what is it that you really want to do? And a lot of times people have it in their head. They, they clearly know they want to include these certain variables, but they don't know how to structure it.

So that AI functionality is really a great example of how generative AI can deliver a lot of benefit, uh, to really users across the spectrum, not just sort of novice users, but even more expert users that just don't have time to memorize every little, uh, formula or, or function within the platform. Uh, that was certainly a, you know, if you think about what's going on with generative ai, this is an example of, of a really smart way to deploy it within the platform to deliver benefits right away. Now, um, another sort of, uh, really sort of interesting development that, uh, that was announced is that the company is looking to kind of migrate its users to the new platform.

Well, what does that mean? Well, it means that instead of having a bunch of different disparate applications, they're moving to a single platform where different functions will be just that there'll be different functions all incorporated, uh, within the platform, and as a result, they're shifting their pricing model as well. So there, instead of, let's say saying, I need access to six different tools and I will pay seat license fees for each, which Smartsheet is starting to do, is shift folks to more of a freemium based pricing model where let's say you have a group of users who need to access a certain functionality, Smartsheet will let them use it, and not for a day, not for a week, I think, uh, made or said something like that.

He's said customers that are evaluating a function or a tool for up to 60 days. And and why is that important? Well, if you think of an enterprise, uh, who is trying to identify what features are actually valuable and what aren't, it's hard to do that without spending time with that particular tool or that particular feature.

What Smartsheet is doing is saying, look, we're confident that users are gonna get value out of the platform, and we're gonna open this up. So if users want to use a certain functionality or a group of users wanna use the functionality they can and without the any kinda limits there, and then once they get to their renewal period, they then they will actually apply the appropriate pricing to account for that usage. Now obviously the devil is always in the details in terms of how that is applied, but my sense is that they, you know, they have a very large, you know, loyal user base.

I don't see them nickel and dimming the their users based on, you know, specific usage patterns. I see sort of a fair assessment of usage and a fair return of value on both sides. Obviously revenue flowing to Smartsheet for the use of that tool, but also not creating a scenario where the user feels like, oh, you know, I had a team use something for a little bit.

And, you know, then, um, you know, they're not going to be blown outta the water. I believe the pricing will be based, uh, largely around usage or consumption trends, which kind of fits with what's going on in the larger SaaS market. Now, I think the other thing that I, uh, was pretty interesting is, uh, Smartsheet announced, uh, their Amazon Q data access tool, which will allow users to access Smartsheet data, uh, really through this tool, through any other application.

And what does that mean? Well, if we think about the modern enterprise right now, data lives in a number of different places. Uh, there are very, very few organizations that only keep data in one place, or one data lake, or one application, or one data platform.

That's just isn't the way that most organizations operate. Because of, you know, the nature of how, uh, you know, technology is acquired, it might require a system for a certain function. You generate data there, and that, of course, then you have all that data in that application and it's much, it's challenging to automatically say, well, we're gonna take all that and put it into a centralized location.

Very few customer, very few companies that have been around for any amount of time have taken that approach. So what this is, this tool allows an organization to access data, a lot of it that might be incorporated within or included within Smartsheet and access it throughout their organization. This is really huge because if you think about the way projects are done, you're not always gonna have data lying in a convenience spot.

You're gonna need to be able to access it from all over the place. Uh, if you think of, uh, other depe systems that are dependent, uh, or they want to access information held within the project management platform, they'll be able to get it, uh, through Amazon queue. And again, this is a tool that is built on, uh, you know, being able to query for this data in natural language, which again, makes it accessible to all types of users.

Again, reducing the amount of friction when it comes to actually accessing the data that that one might need. Now, uh, the other thing that I, I found pretty interesting about, uh, you know, all of these, uh, new features that were announced and, and, and there were certainly, you know, a few more, you know, specific announcements around workload, heat map, workload schedule, uh, which you know, are designed. Basically there are two features that are able to provide managers.

It's a little more insight around project resource allocation, uh, you know, labor utilization scheduling across the entire portfolio of projects. Um, there's also another feature called resource management. Uh, this allows, uh, uh, an organization to organize all of their resourcing data within a Smartsheet report and then put all those insights onto dashboards, which really is designed to help improve, uh, enterprise-wide dismiss ability and decision making.

And then of course, the other, uh, you know, sort of new announcement there is around timeline view. This allows Smartsheet customers to look at, uh, date-based, work date-based project work, uh, and allowing them to get a better sense of what's going on in terms of deadlines, what is, what work is being done when, uh, you know, looking at project milestones and where they are along that timeline in terms of completing those, uh, deliverables. It, again, this is all about visibility and accountability, which is, you know, sort of the, the, the hallmarks or, or the pillars upon which projects are completed within organizations.

Um, now of course, the other thing that you know, is going on, as I mentioned or alluded to earlier, is the use of AI within the Smartsheet platform. Um, again, uh, there are, I believe they have announced, uh, you know, a way to analyze data just via conversational prompts. Um, you know, so you could say, show me a chart with all of the projects that are due within these particular dates, um, that'll being worked upon by these teams.

Uh, it's a really cool feature. I think it's something that is ultimately, this is the way that as we move along this AI continuum, we're gonna be seeing people interact with, uh, their various applications, whether, whether we're talking project management, whether we're talking ERP, whether we're talking CRM, doesn't really matter. It's this very conversational, almost prop based way of interacting with data, because in the end, it is much easier to extract data, you know, when you just sort of have an idea of what you want to learn as opposed to going in and clicking through, you know, generating reports, that sort of thing.

That's, that can be a challenge. And particularly if you look at the way, um, organizations are structured now, they're tracking all of this information. Now, you know, this is not just, you know, most companies anymore, if they're moving to a platform like Smartsheet or whatever, or Monday or whatever, they're inputting all of this data somewhere and being able to access it quickly is really where there is value.

So I think, uh, the, the other thing to, to, to really note here is that Smartsheet is, you know, they are definitely leaning into, you know, demonstrating their value to their customers through their PR pricing model. I think this is something that is going on or will be going on with a lot of other vendors moving away from a strict seat license model, where it's sort of like, well, you know, let's figure out how many people need access to the system and we'll pay for it, realizing that, well, maybe, you know, this group of users is only utilizing the software 20%, uh, of capacity versus another group that uses 80% and trying to figure that out versus more of a consumption model where essentially you pay for what you use and then you're able to better, you know, sort of forecast what, what your usage is gonna look like over the coming years. And of course, you'll be able to kind of scale up, scale down and, you know, get a handle on where you might be moving in the future based upon that usage.

So, uh, a lot of good stuff coming outta that, uh, that particular conference. Uh, I would say that the other thing about Smartsheet, it's really was, uh, there's a few other things about Smartsheet that were really interesting. One is that I talked to a ton of customers there.

Uh, kudos to Smartsheet for being very, very open with letting us, you know, as an analyst, talk to customers without sort of being handcuffed, saying you can't talk to them. Uh, you know, they are obviously very confident that their product is telling their story and is delivering results for their customers, otherwise they wouldn't allow us to speak with them. Now, was every customer I talked to, were they all jumping up and down about every last feature or every last thing about Smartsheet?

No, of course not. Uh, if that were the case, I'd really have to wonder whether or not, uh, they were actually customers. Um, but I will say the one thing that stood out to me is that, uh, a couple of customers directly said to me, the one thing that Smartsheet has done, particularly over the last six to 12 months, is they have taken up upon themselves to take a much more active role in terms of listening to their customers, suggestions about what features they want, what features they don't want, what integrations do they need, and then actually, you know, implementing that.

Now, again, uh, it doesn't mean that every suggestion has been listened to you. That's not how you run a company. That's not how you develop a product, but taking everything from your customer's verbatim and then popping 'em into the, the roadmap, uh, there has to be strategy behind it.

But the fact is that if you think about what, uh, you know, these companies are really trying to deliver, they trying to deliver, it's a great customer experience, and it starts by doing that with their own customers. So with that, I would like to quickly shift to the other event I went to, which is Zendesk's first ever AI summit, which is held in New York. Now, this is an interesting event because Zendesk has, over the past year and a half or so, like a lot of vendors, they've really kind of leaned heavily into AI as a tool to not only improve efficiency of human workers, but also to kind of do a lot of automation.

And I think this event was really great, sort of crystallizing the messaging that the company has, uh, particularly around AI-powered agents. And here Zendesk is taking the approach that all of these AI features, they can be used to not only kind of enhance self-service, but also help human agent interactions, uh, across all channels. So the goal is anything that can't be done or anything that can be automated easily should obviously be done that that's a way to start to really essentially cut costs.

And, you know, the, the sort of dirty little secret, uh, that, that companies don't love to talk about. But yeah, a lot of customers, they are initially looking at AI as a way to reduce cost. And yes, that means cutting headcount in some cases.

Uh, sometimes it means, you know, cutting, uh, you know, cutting certain live support features because there's a better way to do it, you know, using technology. Um, now of course there's also the other aspect of which you of ai, which is essentially, okay, we're not gonna necessarily cut, uh, cut the overall number of humans working, but we're gonna make sure that we have the right people in the right roles to handle more complex interactions. And with that AI and AI agents can be used to assist them to make sure that they have the right information at the right time so they can, so they can serve customers better, you know, through live channels, whether we're talking about live text or through voice or through, uh, app interactions, whatever it might be.

But Zendesk basically saying that, look, you know, ai, uh, you know, they have their AI platform, the Zendesk AI platform combined with all of the process and workflow data that they have, having worked with a lot of customers over the year, you know, obviously in a service-based environment, you know, that will help make their AI agents work very well kind of out of the box. And then of course, they did also announce something called an AI agent builder, which is designed to allow their customers to more easily customize and deploy agents for specific functions or specific industries. And I think that's, uh, important because as we start to get past this sort of initial wave of AI agents or chat bots or what have you, taking care of some of the low hanging fruit of, you know, basic interaction like, oh, I need to return a shirt to this retailer, or I need to, you know, uh, you know, change or add a feature into my TV service.

I think where organizations will really, you know, really generate a lot of value is by attacking these more complex problems. And what that means is, generally speaking, if you think about people, people don't call up or text or engage with a company through their customer support lines because they've got nothing better to do. They don't want to be there doing that.

And the only reason that they're interacting with them is because they have a real problem that they weren't able to solve on their own. A lot of times it's because what they're doing is very complex, either in terms of having multiple steps, or perhaps they feel they have a unique scenario that is not covered by general policy of the company, or perhaps it's just something that, you know, that just generally doesn't come up very often, and they want this situation to be handled quickly and efficiently. What Zendesk and others are saying is, Hey, let's figure out a way to a start training these agents to handle these more complex requests by learning how to understand intent better.

Or if we can't help them through these digital self-service channels, let's make sure that this agent is able to work in the background alongside of a human agent to make sure that agent has all of the information they need to help address these customers issues quickly. Instead of having the agent have to put someone on hold, go talk to a supervisor, go look through a stack of, you know, knowledge base articles, which is gonna take time, perhaps it's not even something that they're able to, you know, know if they're looking in the right place. Uh, so the idea here is figuring out a way for AI to really make sure that whoever is working on that customer support request, whether, whether it's a bot or whether it's a human, has all the information they're able to gather and, and, you know, basically surface that for them in real time so that the customer can be served more effectively.

Now, all of this is great, uh, but really obviously what it requires is that the end customer or the customer needs to have their data, you know, basically prepped and segmented, properly labeled so that AI can actually search through it and understand the semantic meaning and make sure that it grabs the right data that is pulling from the right knowledge base article, make sure it understands all of the interactions, and obviously that comes down to making sure data is prepared properly. You know, and that's true whether we're talking Zendesk or any other company. And I think it's important that companies like Zendesk continually make that known to their prospective customers because ultimately no matter how, no matter how good an algorithm is, if the data isn't prepared properly, it will not deliver the results that is, that are desired.

Now for its part, Zendesk has claimed very, very high resolution rates from some of its AI agents thus far. Uh, I would love to hear a little more detail about what exactly, you know, I, I think some of the resolution rates are, were in, you know, the eighties or even 90%. Uh, I would love to hear more about, you know, exactly what types of use cases, what types of problems were solved, but certainly that is a good start in terms of, you know, demonstrating the power of AI agents.

Now, the other thing that was kind of interesting coming outta Zendesk, it really isn't even, wasn't even from the AI summit, but uh, but actually a little earlier this year, was the fact that Zendesk is moving to a new pricing model just as Smartsheet is. Zendesk has decided that, you know, this traditional seat license pricing model isn't necessarily going to reflect the way of the future. They're moving to an outcome-based pricing model, where essentially, if you think of, let's say an AI agent and you have an interaction where let's say it's a task like I'm a customer, I want to initiate a return, complete the return.

Well, if an agent is able to do that without any other sort of intervention by a human, well, that would be a completed outcome. And then of course, they would be paid on that outcome taking place as opposed to, uh, more of a seat license basis or even a consumption basis, uh, around, you know, um, the amount of, of compute required to do it. This is very interesting, and I think it is eventually the way in the future because it aligns both the customer's interest as well as the vendor's interest in terms of actually solving customer's problems and doing it efficiently.

Now, it doesn't mean that we're gonna turn to this model or, or all the customers are moving in this right away. It's going to take time. There still needs to be more sort of, uh, transparency in terms of, you know, how do you define an interaction?

What are the parameters in terms of saying, okay, this interaction was successfully completed. You know, is there, you know, a clawback provision if, let's say, you know, uh, you have an erection, you think it's closed within a, uh, the agreed upon timeframe, but then another issue comes up that's bigger and there was actually a mistake, you know, what happens there? Are you still charged for that?

Are you not? Those are all issues that I believe are gonna be worked at over time and, and maybe very much dependent upon the specific use case industry and even customer in terms of how, you know, what it actually, what an, an interaction actually is in terms of, you know, uh, you know, an outcome. But I think it's a, it, it, it really is sort of, it makes sense in this world where, you know, we probably are gonna see more interactions move away from human interactions or human led interactions and more toward that, uh, you know, self-service way of doing things through a, you know, fully digital AI agents.

Doesn't mean that we're gonna have all of them move, but certainly, you know, the goal is to take more of those interactions and make them sort of self service because realistically, most companies, you know, are already just, just drowning in customer service or customer support issues, you know, uh, or inquiries coming in. Um, it's, there are very few that actually have agents sitting around not taking calls or texts or whatnot. Um, so I think, um, this is, you know, again, I wanted to kind of circle back to what I was talking about before about how these two events seem a bit, sort of like two different things, but really there's some sort of some underlying, uh, similarities.

And really one of them is obviously looking at how they are approaching pricing of ai. Uh, both are really taking the approach that, you know, this cannot be a traditional seat license model moving forward. Uh, it just does not align what it is that AI wants to do, which is deliver more efficiency.

And you can't just do that when you're doing a static seat license. Uh, I think that it is also interesting that if you look at, you know, Zendesk, which has taken it to the ultimate, you know, conclusion of saying, we're going to hopefully charge on outcomes, if you look what Smartsheet is saying, they're saying, look, use, use our software, you know, see if it actually delivers outcomes from you, and then we'll charge you based on how much you've used. Both are somewhat similar in terms of saying, look, you know, we're not expecting the customer to trust us.

We're saying, look, use it. And, you know, we only get paid when you do well. And I think that's an interesting development in the market.

I talked about this as something that was gonna happen in 2024, uh, a little less than a year ago when I did my sort of, uh, what can I expect? What can we expect in 2024? And, and I do think this is starting to happen now.

So, um, I I can't say that, um, you know, it's not like this is going to be in the norm, you know, by the end of the year. It probably won't be until sometime in 25, maybe even 26, you know, as customers start to renew and start to look at contract terms and, and, and start to re-up again. But I do think that is on the way.

Uh, what's the other thing, the other sort of point of commonality? Well, you know, again, it's around ai, but also around the need to make sure that, you know, customers have their data, you know, prepped in a format that can be easily used, uh, and accessed. And, you know, I feel like that has been the message that not a lot of folks have really been focusing on because it's not as neat or, or, you know, it's not talking about a feature, it's talking about work that needs to be done by customers.

You know, honestly, before they start really using any sort of platform, uh, if they want to actually get the results that the vendor is promising. And, you know, some of these companies certainly offer professional services to help them. Uh, but a lot of it is also just around having an understanding of, you know, what is it, what are the business goals that my organization wants to achieve?

What data, understanding what data is required to do that? And then understanding where is that data and has it been organized in a way that, you know, if you're using ai, it can actually extract, or, or, or understand, you know, what a meaning, what the actual intent is within a, a prop, let's say. You know, making sure it's able to actually grab the different elements there because it's been properly tagged.

Uh, those are important things to really think about, uh, as companies move forward and deploy these agents. 'cause they will never, ever get the results they want if they don't address those issues first. And then I think the third one, uh, third point of commonality between all, you know, between certainly Smartsheet and Zendesk is, uh, both companies made it a point to, to underscore the fact that, look, you know, the other thing that they are very, very cognizant of is the need to sort of highlight the fact that they are safe.

They handle AI safely, they make sure that they're in compliance with all of the different AI regulations that are out there and you know that they're, uh, employing proper data security and data privacy controls, all of that kind of stuff. Making sure that there's, you know, minimal or where they minimize the possibility of data leakage. All of that, you know, we're starting at the point where I feel like sometimes it's sort of glossed over, but it's important, particularly if you talk to CIOs, if you talk to, you know, honestly, um, security offices that's still top of mind.

They are not willing to undertake risk by implementing a new system unless all of those boxes are not only checked, but also explained. So kudos to both companies for, for addressing those issues. Uh, and then finally, I guess the, the last thing of course is, uh, both companies were talking more about, again, not just features, but time to value.

How quickly will an organization, once they implement the solution, actually see results, see value from implementing the software, um, that cannot be overstated in its importance. And it's good to see both companies talking about it. You know, obviously it's highly dependent upon each particular implementation and each customer, but there's certainly, you know, that is, you know, being mentioned in their messaging.

So they understand that it's important more so than just saying, we have x, y, Z feature here, or, you know, ai this there. 'cause in the end, at a certain point, all of that technology, all of that AI is going to become commonplace common and essentially a commodity in the market. You know, not tomorrow, not next week.

But at some point we're gonna hit that period and it's gonna be these other basic business factors that are really gonna drive business. So with that in mind, I'm gonna wrap up that portion of the podcast and moves to my rant or rave segment where I pick one thing in the market and I, we will either champion it or criticize it. And today, uh, I actually have a rave.

Um, and it really is around this idea that I think customer, or I'm sorry, that, that vendors are really starting to understand that they need to listen to their customers beyond just saying, oh, we take feedback, actually creating feedback mechanisms and really, you know, accounting for customers who say, I really wish I had this feature, or I wish I had this integration. They're starting to realize, and I've had several one-on-one meetings with product leaders, uh, and strategy leaders at these companies. And they understand that, you know, customers are at a point where they understand that, you know, they have a choice in software.

Um, with the exception of a few vendors, you know, it's fairly easy to switch, um, and, and you realize that you cannot be static, you cannot not keep moving forward in terms of delivering certain features and mainly around things like usability, uh, you know, data integration, those types of things. Um, because those are how a particular package can be smoothly incorporated within an organization's daily flow work, um, that's what actually creates efficiency. That's what creates more productivity.

That's what helps reduce errors, uh, you know, increasing precision, all of that. And the way that a lot of these companies are doing it is they're starting to say, Hey, hey, customers, you know, keep giving us feedback and you know, we're not gonna put everything, you know, on the roadmap because that's impossible. You know, it's sort of like, uh, you know, you have to kind of pick and choose and see which features make the most sense for which types of customers and is that a priority.

And then of course, what's reasonable in terms of a development perspective. And then of course, you know, what features or what functionalities, you know, may be best delivered outside of the platform. And certainly, you know, a lot of these organizations, a lot of these vendors realize that the most efficient way to deliver certain experiences is to partner with another organization.

But I think that it is interesting to hear companies acknowledge that, uh, you know, they do need to listen to their customer base. And again, you know, there's certainly, you know, still a ways to go for a lot of these, uh, organizations in terms of, you know, making sure that there is that sort of free flow of, uh, suggestions to action. And even if not action, just acknowledging that, hey, you know, thank you for your feedback.

Here's how we're looking at addressing this issue, this issue. And, you know, just being transparent there because ultimately, uh, everyone using a customer experience business right now, uh, and that certainly includes their own internal customers or their own customer base in terms of making sure that the product is delivering and meeting their experiences. So I would say that that's certainly a rave right now.

I do feel like there is this, uh, definite feeling that, you know, vendors are listening to their customers more. And part of it is that now with generative ai, they're able to deliver a lot more features, uh, you know, more quickly, uh, than they ever were in the past. So I think that's a win for everybody.

Alright, well that's all the time I have today. So I want to thank everyone for joining me here on Enterprising Insights. I'll be back again with another episode focused on the happenings within the market in the next week.

So be sure to subscribe, rate, and review this podcast on your preferred platform. Thanks again and we'll see you next time.