Futurum Signal – Agentic AI Platforms for Enterprise

So as, as many of you know, uh, tech Field a is part of the Futurum group now as of about a year and a half ago, uh, Futurum is interesting because it is an analyst firm that was designed from the ground up to challenge the preconceived notions of what an analyst firm is. That's why I thought that it would be a good home for Tech Field Day, along with the tech strong media sites, along with so many other elements of it, because the whole point of futurum is let's not do it the way it's been done before. Let's do it by rethinking it in the context of the current business climate and the current technical capabilities.

And that's really what the, what I'm gonna show you next is all about. So, signal, Futurum signal, when the time came to come up with a vendor evaluation survey, sort of that thing that analyst firms like to do, where they like to have kind of a, you know, a, a, a, a shootout of all the different products in a space and tell customers, uh, you know, uh, which one's better, which one's worth which, which one's more mature, which one's a, a leader, a follower, whatever you want to call it, those reports. Well, Tums is new.

It's called Signal. And Signal, just like the rest of the company was designed in a way that basically throws out a lot of the preexisting conventions about how we should do this and takes a new approach. And the approach that they used surprise is agentic ai.

And so that's why I wanted to share it with this group and also with the audience here, to really kind of show where Agentic AI could be used in other ways. How it can help a company to rethink the, the basics of how they approach technology, how they approach creating content. And also to share with you the results of this signal report.

So the, um, signal report that I'm going to be sharing with you is Agen AI platforms for the enterprise. So this is not intended to be, uh, the coding assistance that we've talked about. It's not intended to be anything same.

This is intended to be a strategic decision from an enterprise buyer of which major partner they want to bet on to transform their business using Ag agentic ai. And so, uh, what we did was the analysts within the future and research group got together and decided which major companies would be appropriate to evaluate based on their knowledge of the market, based on the companies that they had, uh, been briefed by, based on, you know, their own feelings, uh, who of this report. Then they basically set a fleet of agents loose to collect the data, collate the data, and pull in the data and develop a report.

This recommendation of what, what companies should be looking at. So what I've just described is very different. If you guys know a little bit about the analyst game, typically these reports that come out are the result of a long process, a manual process of collecting data, often with the direct involvement of the vendors that are involved.

You send them surveys, you have them brief you, it takes a long time. I only did it once, and that was enough for me because it literally took me six months to pull in the data. And as soon as I was done, it was already outta date.

And the, uh, vendors that participated didn't like the way that they, that the, the way that they looked and that they felt like I had ignored this information or looked over this, and the vendors that didn't participate, well, they were even more mad because I didn't, uh, properly judge them, even though they didn't participate in it. So, the problem is that this whole model of evaluation could use a reboot. You know, you look at the way that things have traditionally been done, you think, man, how could this be accelerated with ai?

How could this be accelerated with better data, with fresher data, with more data? And so what I'm gonna show you now is, is a, a report on enterprise AI that was generated using a combination of human intelligence data and, um, information from, from the, uh, that was pulled using this AI model. So the goal here is to combine the nuance that an intelligent and informed analyst brings with the speed of an AI powered data pipeline.

And so, Deepak runs this for futurum, and he was kind enough to let me basically share this with you and share the final report with you. And those of you who are watching, I would, I will share the report with you too. Just reach out and I'll send it to you in email.

So here we go. So how does this work? Well, the model pulls in essentially four kinds of data.

It is proprietary data from the analysts. Essentially, they're taking briefings and attending events and doing meetings and talking among themselves and building the same kind of proprietary data that all analyst research firms have. Um, they're analyzing it, they're talking about it, they're producing their own reports.

It's also data from industry, from partners in this case. Uh, G two is a, a primary source of data for Signal, along with other things, including, um, the media content that we do with techron, including Tech Field Day presentations, company presents at Tech Field Day. Well, there's an hour and a half of in-depth product description, everything that can be used to drive these reports in the future.

So in the future, when somebody presents at Tech Field Day, it's gonna be reflected in the signal. Also, AI powered data, essentially ai, you know, the same way that we would use like perplexity to go do research. They're using this system that they've built, and it will go to the vendor's websites and it will download things and it will process what it finds.

It'll process the product, uh, the use cases, all that sort of thing from all those public sources of information, pulls it all together, um, and then produces analysis that is then curated by the analyst in charge of that area in order to produce a recommendation. The cool thing about this is that it can be done extremely quickly. It's got a methodology here where you can see there's, there's these five there, assessment areas.

Each of those are ranked ba with, with, with primary data that comes from different places, including, you know, the, the vendor's own website. Um, each of those things is then kind of collated together. They, they're ranked on a numerical score and it produces essentially the classic spider chart of, uh, who's the more mature and who's the less mature.

So, do you guys wanna see the results? Yeah. Yeah.

Alright, so here we go. So, when it comes to the ENT platforms for Enterprise, essentially what they were looking for was companies that could offer a true, uh, suite of tools that would be appealing to the largest enterprise customers, to the companies that basically want a, a real partner to do Agentic ai. And so they pulled in some familiar names and they collated these things.

Now, first off, there are some trends that they noticed when they did this evaluation. Um, I think that a lot of these things are things that you all are recognizing as well. So for example, we, we talked about the fact that companies want there to be collaboration among multiple agents from different sources, different models, et cetera.

They talked about the fact that data is absolutely critical. And so these agents need to have the ability to pull in information from a variety of sources that it needs to be appropriate for the enterprise, that it needs to have the proper controls, governance, that sort of thing included with it. Um, there's a lot of interest as well that we're hearing from customers in, um, non, you know, foundational models in having either open source, proprietary, internal controlled models being involved instead of just, you know, throwing it out to, you know, open AI or philanthropic or somebody like that.

Uh, these are all things I think that we've all been talking about. Do, do these trends resonate with you? Do you agree with these four trends?

Yep. Nothing like when you see them, They all make sense. Yes.

Right. And same with the success differentiators too. Yeah.

And, and, exactly. So when we're evaluating these, what are we looking for? We're looking for companies that are mature, that have, you know, good data governance, that have a, an ecosystem.

And this turned out to be incredibly important in this particular evaluation, simply because companies want to have a variety of data sources. They wanted there to be an open ecosystem. And also critically, this is a report intended to be consumed by the Fortune 500.

You know, you need a, a somebody who can be a, a real enterprise business partner. So, here we go. Ready?

There it is. Who are the best partners? So essentially, according to the analysis done by the Butum AI researchers, uh, Microsoft and Salesforce are in the elite zone.

They're the ones best suited to be the enterprise agent AI partners. Um, then you've got Google, I-B-M-S-A-P, ServiceNow. Interestingly, AWS and Oracle are down in the established zone.

And, and Glean and UiPath, who you may not know, uh, were deemed, uh, you know, worthy of being included in this assessment. And I think that that's important because there are 500 companies that could have been included in this assessment, but they didn't make the cut and these guys did. And so if you aren't familiar with Glean and UiPath, I think it's worth taking a look at them.

And we actually have some more data in here. Now, I can't go through all of this, but I'm gonna try to go through a little bit of it. So, so this is the result.

This is the magic, um, you know, target circle, you know, uh, that says which ones people should look at. This is the heat map. So across those five areas that I talked about, who's got the best solution?

And it immediately stands out why Salesforce and Microsoft were chosen as the leaders in this pack, because they've got the most heat in the most squares. I mean, it's, it's right there. Um, I do think that it's interesting, you know, you glance at this and you say, you know, ServiceNow, S-A-P-I-B-M, Google, you know, they all come out looking pretty good.

So let's look at them a little bit in according to these five criteria. So when it comes to business value, again, Microsoft and SER and Salesforce were the leaders. Um, you know, I'm not gonna read slides to you, but clearly they represented the best, uh, value for the enterprises.

They, you know, Microsoft and ServiceNow on the, on the other hand, were the ones leading in terms of like what the product actually has and, and does. Um, Salesforce and Microsoft, were in the lead with the vision, Salesforce and Microsoft again, in terms of, of execution and finally Salesforce and Microsoft with ecosystems. So essentially what we've seen here is that they have, you know, taken most of the field when it came to this evaluation.

Can I make a comment? Absolutely. Everybody hates Salesforce.

Hold on, hold on. There's, there's a follow up when one of the reasons for that Salesforce hate is you can't do anything out of the box. It always requires customization.

Salesforce Really is a platform. It really is a platform. At least I can do stuff with Microsoft products out of the box.

Yeah. Not denying your hate. I, who am I to tell you, Ray?

I Think, I think the point is valid, but I think the target audience for this Yeah. Is the enterprise. Yeah.

It's not us. I get it. The, Yeah.

And the biggest enterprise customer, The biggest enterprise. So if we see, with the exception of UiPath and even UiPath is in some of the biggest companies in the world, I, I've not heard of Glen. I am curious, Steven, on the input of, so as you look at the RUM brand and all of the components of it, uh, and specifically field day, 'cause we're at field day.

What happens when a sales force hears this and they present at, at a, at a field day? Does that change the, how, how much of an impact does that input have on? 'cause it's real practitioner feedback, et cetera, et cetera, or even UiPath, To be honest, UiPath Presented a long time ago.

Well, here's the thing. I hope that the answer is a lot. Mm-hmm.

Because what's happens around this table is the kind of feedback that companies don't get. Yeah. Like, too often the only feedback they get is Competi competitors complaining about their product champions championing their product, customers trying to justify the purchase.

And I don't know, maybe haters, and that's not any of you. You guys are a different category because you're here to actually say, sort of tell it like it is and give honest feedback. So my answer is, I really hope the answer is a lot of impact, but I will be honest, tech Field Day had zero impact in this report because we have not had these presentations at Tech Field Day.

So there's no way this could impact, you know, what we do here could impact this. But in the future, I will tell you already, there's a signal report that is gonna be managed by Mr. Tom Hollingsworth.

And that one will leverage Tech Field Day presentations and your comments very heavily because it's, you know, it is a hundred percent in our wheelhouse. And literally every customer, every company that's gonna be considered for that report is gonna be somebody who presented at Field Day. And I'm hoping that in the future, that's going to be the norm, that what you say will heavily impact the result that comes out of this.

And the cool thing about this report too, is that it is up to date. So I'm gonna show you guys something. So this is Microsoft, uh, this is Salesforce.

I can go back to those and talk to you, but I wanna show you the AWS page, Despite a significant outage in October, 2025. Okay. That's in the report.

When would that get into a report if it was being done by hand? I tell you what, not until next year at the earliest Right. Would it even be remembered?

But that was such an impact, and this report was prepared, and basically all this data was collated automatically. You know how it is with a, with AgTech ai, basically you're sitting there and you're watching it and it's churning through all this stuff. Yeah.

Well, it took into consideration, uh, Q3, 2025 financial results. It took into consideration, you know, what just happened. It took into consideration the fact that, um, you know, they just had a new, um, you know, executive leaders, um, in, in one of these, that was another one of these, uh, mentions in here.

It was, it was talking about a new newly appointed, uh, CTO for one of these companies and what his background is and what he understands about the market. And that to me is kind of cool. The thing that, the idea that it'll take into consideration, like announcements, news, you know, I mean, if we were gonna do one of these things with Yeah.

Typically if you're an analyst firm, you might update this once a quarter or once a year, Once every single, yeah. Once a year. Probably a year.

Uh, are you gonna be updating this like on a weekly basis based on the fact that it's all genic generated? Well, it's not all genic generated. Um, it is important that it uses the data, but it's also important that people are involved.

So I do want to clarify that this is not just something where you're gonna push the button and it's gonna turn out a fresh report tomorrow. But that being said, the goal is to build a system that can be updated very frequently. Heavy Lifting that can, can be done, right?

Yeah. As long as you've got the human in the loop, whether we like that term or not anymore. Yeah.

But you could identify exactly what was created by the genic AI and how has changed when the Human review Yep. And then go back and just update the genic AI stuff. Right.

And they're a hundred percent doing that. Like Yesterday, Azure went down so Azure could appear in the report. Right?

Right. So that's, I think that's the goal, is that it would be updated very frequently. So The, I I've always had this question about RUM content, who is this for?

Like, what's the target audience for this report? I, I can't read mines. Mm-hmm.

But I will tell you that reading the report and talking to the folks, this is intended for buyers in the enterprise. Um, I think that's who it's written. If you read the, if you read the text, that's who it's written to.

Um, If they can, if you guys can do this with this fee consistently, it is absolutely a market leading Yeah. Asset. Because I think, and I think buyers really appreciate transparency as Ivan just mis mentioned.

If it's just noted this, this via ai, this update is via ID ai and we're trying to get you the freshest content possible. And once we, you know, get more humans taking a look at it, I think people will appreciate that. Yeah.

And, and I think so too, and especially if, if, if, if it can be approached with the proper level of humility and the proper level of respect for the, for what is AI and what isn't ai, what is human, what is not human. I mean, it, it, it really is pretty remarkable to see some of this analysis. And it's also remarkable, if I can jump down here to those, uh, those, uh, lower tiered vendors.

I don't wanna say that, you know, followers or something because they're not, they're aspiring, that's the word, aspiring vendors. Um, they rose to the surface as part of this report. Like I said, the, the goal was basically we need to figure out who should be included here.

Obviously, Oracle, Salesforce, you know, AWS Microsoft, they're gonna be in this report, but who else? And I think that what happened was they did the, they did the number crunching and they realized that Glean and UiPath had compelling products that deserved attention as well. And frankly, I've been, I've had a briefing with Glean and I've talked to the folks over there and they haven't been investing.

And I feel like Glean absolutely deserves a place at this table. And I have not yet. Well have, you're right, I have talked to them in the past, but I haven't talked to them anytime recently about, certainly not in the agentic era, but I really look forward to talking to them as well.

And I would love to get some of these people in front of us for field day. So essentially, you know, these are companies that are making the, the move to build something that challenges the established companies. So that second bullet from the bottom expanding beyond RPA, like mm-hmm.

Clients I know that have tried them, were underwhelmed by them in the RPA context. So that trying to go beyond that explains hitting the aspiring category. It would be interesting to hear what they have to say.

Well, and it, it's interesting to me too, because that's, my use of Agen AI is basically as a replacement for RPA and my frustration with those limited tools. Yeah. And my seeing the PO potential that LLMs and, and, and, and generative AI brings to making RPA processes more durable.

Yeah. Um, and flexible. And so yeah, this is, this is, it is interesting.

So the intelligence platform, I want, the reason I wanna call this attention to is because this one absolutely a hundred percent is updated in real time. And so this is basically a, a portal, uh, an online platform that you can log into as a futurum customer, and you can see the graphics on your favorite thing, and it is up updated in real time, and it is updated with the latest information. And, you know, it's, it's actually kind of cool because you can see, for example, if somebody takes a briefing, it all like trickles right through.

And suddenly the, that vendor's, um, information is much more, you know, is, is bulked up here and here and here, the same with, uh, you know, new financial data that comes out and so on. There's a lot of really infras interesting stuff in there. So, um, unfortunately I can't really share that with you.

Um, so that's basically what we've got, you know, um, what do you, what do you guys think, uh, transforming the way that these analyst leaderboard reports are generated? That's great. Always a good thing.

Yeah. It's, it's interesting. It would be also interesting to have a way to assess what those updates look like, the fidelity of the updates.

And I don't even know what that would look like at this pointif Yeah, I'd love to see a diff Yeah. Yeah, yeah. Diff um, would be the first step for sure.

Change Log. Yeah. So this, so this is the report, um, by the way, um, if you wanna know what it really looks like, this is the actual report.

And so, um, and, and what I've just presented is actually just, uh, a summary of what the report says. And so if you recog, if you look in here, you'll know, and actually I'll, I'll give you a little Hi, uh, little twist. I actually generated all these slides using the future of intelligence platform.

I actually generated all the text. You just saw you using the platform's AI capabilities to summarize and consolidate data from its own report. And that's pretty neat, right?

So I was able to do this, um, in just a couple hours, and I was able to summarize like, everything that, that is in this entire report in a, in a very short time. So, again, it's really cool. I know that we've all been using AI in various ways.

Um, you know, Futurum is very, very committed to using AI as sort of a superpower for people who are working there. Um, you know, the message is very strong that, um, they want us to leverage AI as much as possible. And so we are, and so when the challenge came, you know, hey, if you'd like to share this with the tech Field day community, you can, uh, but we're gonna, we're gonna need some slides.

My answer was, okay, let's, let's warm up the, let's warm up the AI agent and, uh, and make it happen. And that's what we did. So, um, yeah.

Do You have, do you have visibility into the ones that just didn't make the aspirational tier? I don't, but I know that the analyst that was responsible for this, uh, Dion Hinchcliffe is the one who led this particular report. And I know that Dion has a very exhaustive list of who would have been in here, Would be interesting.

Yeah, I, I agree with you. That would actually be a really cool idea to, uh, to include. So anyway, so this is, uh, this is the Futurum signal.

This is their new report. I am sharing this with the delegates. I would love your feedback.

Um, I warning, if you give me feedback on the con on the format structure, content of the report, I'm gonna give it to Deepak and the, and the team, and hopefully they will change and update and improve it. And the same thing with anybody who's watching here. If you th if you say, that looks really cool, uh, just drop me a line.

Uh, they have authorized me to send you a copy. Uh, so I will, and, um, if you are interested in, uh, being part of this in the future, you know, let us know. Um, and I'm really looking forward to the first tech field day influenced signal report.

And that's coming probably in the next couple of months, because that's how quickly they're able to go from zero to report in hand. Uh, so one major, um, impression, and it goes to Keith's, who is this for? And I know we, we think it's for enterprise buyers.

Look, the slides read like a report. They're so text heavy. I wonder if, Well, those are my slides.

Okay. Well, I wonder if the platform will let you do a condensed, you know, like are, um, even more summarized, like, you, You wanna know something incredible. I had that conversation this afternoon.

Okay. And I said, yeah, I put together the slides and I used the, uh, intelligence platform to summarize the, and the, and the feedback that I got is, well, we should just have the signal platform generate those slides. Sure.

And so the next version is gonna do, it's gonna basically make this slide deck as another deliverable. Yeah, no, I think that, that With less text, I, I think that's perfect. Like I'm very much visual display of information and you know, the report should be very, you know, all the dets, right.

Um, So it's a hundred percent gonna do that. Yeah. Cool.

Yeah. Yeah, because I couldn't, I actually couldn't, I couldn't get the, uh, AI to be, um, concise enough for my taste. It was always too verbose and I didn't want to cut out, I didn't wanna not have the text, and I also didn't wanna oversimplify it.

And so I said, fine, that'll fit 14 point font, let's do it. But yeah, we're absolutely gonna have the gonna make it do that because it generated this, it did the layout and it did the No, that's awesome. Automatically.

Yeah. And so my snarky response to Keith is, it, it's actually for other bots crawling the web, LLMs can have a tendency to be verbose. Yeah.

I'm Not reading this. I'm gonna put it into LM. Right, exactly.

You know, hey, summarize this for me. Right? That's the first thing I did.

Did. Yeah. But, But it's like in any of us who do any kind of content generation and get paid for it, it's like, we just had this conversation earlier today, or, you know, who's the content for?

Well, more and more it's for the bots to make sure that we get the eyeballs so that people will actually pay attention. Well, that's what we're doing with like benchmark data. That's what we're having to have to rethink.

'cause like our consumer is no longer humans first, it's something programmatic or agentic or whatever first. And humans are a secondary function to that. Well, another thing that occurs to me, and I, I want to kind of run this by y'all.

When I first heard of this project, my first reaction was, I don't want it to just be a web interface that you can use in real time. I want there to be a report. I want there to be like basically a stake in the ground, a bookmark that says, this is the October agentic AI for the enterprise report.

Right. Instead of having it be the October 30th Agentic ai, you know, as opposed to the October 31st one, which has different con conclusions, because I felt like we need to make, you know, you need to make a stand. You need to say, I feel that as of today, if a gonna implement AG agentic AI in a big enterprise environment, they should be talking to Microsoft and Salesforce.

Yeah. You know, I think that's important. Well, And this is the thing that we're noticing, especially in academia, right?

Where now if you're gonna be citing anything that you've pulled outta research, you must cite the model, the version. And today you pulled it. Yeah.

Because longitudinally, that could be changed next week by next training epoch or the next, you know, kind of iteration of whatever. And because these cycles change so fast, yeah. Having a demarcation point or a point in time in which you're drawing from, it helps show maturity curve.

I mean, you're ultimately gonna get into the place where you're gonna be doing maturity curves. You're gonna be doing co bless not another magic quadrant, but you know, nested pipe or whatever the hell you're gonna call your diagram. Um, but, you know, spider, spider, spider, spider everywhere.

But you know, you're gonna clean Spider. Yeah. You're gonna want that over time, because this becomes, you know, where you see people either falling off or succeeding, right?

Microsoft today is the lead, but they might be an established, and it could be, you know, AI platform. Next is gonna be the, uh, And to the point here, it would be, it would be really cool to see the diff Yeah. And if the diff is on a quarterly basis instead of on an annual basis, I think that's a lot more valuable.

And it reflects the speed of everything. And so, I wanna leave you with one thing. Think about the things in your space and think about the way that AI can challenge the assumptions, the basic assumptions.

Because the basic assumption was you can't do this and update it in real time. You can't have fresh data, tough luck. It's gonna take six months.

And if you throw that out the window, then what? And in all the other areas that we're working, how can we throw all that out the window? And how can we use this, a AI technology to have much quicker time to market, much broader reference, you know, I mean, this thing is looking at every page of the vendor's website, right?

I I, I don't think an analyst would do that. I certainly didn't do it when I was doing my eval. It's, it read every customer use case.

Yeah. I didn't do that. So, I mean, how can we use AI to accelerate things?

So that's what I've got. So if you're interested again, um, you know, reach out on LinkedIn to Futurum or to me, I know that they would be willing to share this with you.