09×03: Bringing Agentic AI Applications to Market with Brad Shimmin of The Futurum Group

From the very first episode of this podcast back in 2020, we've been focused on practical applications for AI technology, and we're starting to see these come to market with Ag Agentic tools. This episode of utilizing tech features Brad Shiman, VP and Practice Lead for data and analytics at The Future Group, discussing the ways AI is gaining autonomy. Welcome to Utilizing Tech, the podcast about emerging technology from Tech Field Day, part of the future in group.

This brand new season focuses on practical applications for ag agentic, ai, and other related innovations in artificial intelligence. I'm your host, Stephen Foskett, organizer of the Tech Field Day events series, including AI Field Day. And joining me this week as my co-host is Mr.

Frederick Van Herrin. Frederick, welcome to the show. Well, thanks for having me again.

So, I'm Frederick Van Herrin, the founder of ens, an HPC and AI Consulting and Services company. com. And, uh, uh, if you've been listening to utilizing tech or the previous utilizing AI seasons, you definitely recognize Frederick.

Uh, you know, he and I have been talking about AI since, well before, before, uh, all this generative AI and chat GPT, um, hit the, hit the market. And one of the things, you know, that I wanna call attention to is the reason we called this utilizing AI way back in, I don't even remember what year that was, uh, was because I was very interested in practical applications. How do we utilize this?

How do we make this technology productive and useful in the enterprise? And Frederick, you've been working on that way longer than I have. Yeah, indeed.

I mean, uh, if we had a crystal ball, it would be, uh, a lot easier. I mean, I think the holy grail here is to have the machines do a little of the lifting for us, you know, like the, the mundane items and speech is the way we communicate with machines, right? And so I've seen the whole evolution going from CPU centric to data centric, and, and nowadays it has gone so far and so fast that agen AI is opening a door to new applications.

And that's what we're really hoping for. And in fact, uh, at Futurum Group, uh, one of the big focuses of the company is figuring out what's practical and what makes sense and what really has legs. And that's why I wanted to invite on the VP of the, um, data and analytics team at Futurum Group to talk about some of those practical applications, some of the, the ways in which we're seeing AI coming to the enterprise.

So let me introduce, uh, Brad Shiman, uh, our guest this week. Brad, welcome to the show. Yeah, thank you Steven.

And, uh, it's great to be on the show. I appreciate it. So, um, as Steven mentioned, I'm VP and practice lead for, uh, our concern, uh, that focuses on data intelligence, analytics, and infrastructure.

So basically everything that goes into building in insight and gaining insight and taking action on insight within the enterprise. I've been an an industry analyst for quite some time, um, uh, but I've been a technology practitioner for far longer, and as you can tell for quite some time, um, uh, going back to 1990 when I first started, uh, working, uh, with, uh, Fox Pro Databases in Novell network, if that gives you any, any, uh, insight into the, the depths of my suffering that I'm willing to, to endure with technology, uh, because I, I adore it so much. Uh, but, uh, at any rate, uh, I'm very glad to be here today to talk to you guys both as, uh, an industry analyst watching this market and as a practitioner that is building, uh, ag agentic solutions within Futura.

Yeah, so it's, it's an interesting conversation. I mean, can you talk a little bit, what is an ag agentic system for our Audience? I love that because I recall it was, it was about a year and a half ago.

It was at a conference, and the, the vendor will shall rename name nameless, uh, but they like the color red. And on one of their slides was, uh, these are the age agentic processes that we support as a company, and we have built for you, our, our buyers. And it was a massive list line by line by line.

And when I looked at it closely, uh, I noted that pretty much 95% of those were all a single transaction. Like, you know, open the fridge door, check the weather, things like that. And, and I don't believe that's age agentic.

I, I think that's transactional. That is something that, you know, anyone who's built software or works with software knows is you ask for something, it gives you something. Um, and so when I think about age agentic systems, I, I, as an analyst, define them as something that, uh, has a number of capacities and characteristics.

And those are, um, autonomy, first and foremost, the ability to act on its own without me saying, now, shut the fridge door. Um, the second would be the ability to, to reason and plan, uh, which leads to said autonomy. Um, so to be able to say, okay, the user has asked me to, uh, do something for them.

Well, what does that entail? Um, what will I need to know? And what will I need to do to achieve that?

Make that plan to, you know, disambiguate sometimes what the user's actually asking for. Turn that into some sort of actionable plan, actionable plan, and then make it happen. And, and that comes to the third aspect, which is, uh, the ability to, um, make use of tools and information to, to take action on its own.

And that is where I think there's been a lot of, uh, a, a lot of leeway made, I I should say, across all three in terms of, for the first models have gotten much better at reasoning. And as we see, many models now are just built in with, with inbuilt reasoning capabilities, where they will go into think mode. Uh, second models will be built with the ability to, to basically, uh, make a plan and to think about how they could execute it.

And third, they will be able to make use of tools and information. And that last one is where we start to see all the technologies like MCP that I just knew we'd talk, talk about today, uh, come into play, and how popular that is right now in supporting Ag agent solutions. But to, to summarize very quickly about that, you know, I see in ag agentic process is anything that a machine can do to, to basically, as Frederick mentioned earlier, to, to do some of, take some of that lifting off the shoulders of a human, to do that autonomously and to make that something that wasn't automateable automated.

Um, whether that is basically getting the weather and then booking a, a different seat, um, for, you know, a stadium, let's say, if it's going to rain for you, or if it's to basically to put a hold on a stock that you, you know, know is going to respond to something happening in the market, doesn't matter. That's all, you know, just a matter of scale and, and complexity. But at the end of the day, it's just autonomous action taken by AI on our behalf.

Right. It seems like, uh, a gentech AI is, is kind of an evolution of generative ai. Now, from, from a practical standpoint, I mean, can you buy a generator, a a Advent AI system, you know, how does that work?

I mean, you talked a little bit about MCP is how, how does MCP kind of is, is an, how does it play an important role for people to build applications? Yeah, so to answer the first part of your question, yes, you can. Um, it, we're seeing increasingly productized agentic solutions, and this is, you know, how the market evolves.

It always starts with horizontal use cases that, you know, basically you have a set of tools, like if you're a developer, you might have frameworks and libraries that would take help you get to the end of that, you know, EU agentic process. So, um, over the last couple years, I would've likely used Lang Chain and, and within that lang graph to spec out how I wanted my ENT system to work and code that to work. But as time goes on, um, and as the marketplace always does it, it leans toward building out tools that, and solutions, I should say, not just tools and not just resources.

So that I can basically, as a consumer, whether I am, uh, a consumer, consumer or a business consumer, uh, you know, turn on or open up a browser, let's say, and have at my disposal a complete agentic solution to do something, whatever that is. And right now, I, I think the market is, is predominantly, um, delivering, what I would say is, is reasonably consumable, um, agent agentic processes, not so much in the specific, get something done for, you know, everybody who's trying to book a, uh, a dentist appointment, let's say, but instead about how they might do common tasks. So if you, if you go look at the vendors that I I cover and look at quite a bit, you'll see those that are focusing on horizontal use cases like data integration, um, they are right now building out ag agentic solutions that are productized that go toward helping you the, you know, data professional, basically stand up, uh, or find, and then bring in data in a way that you can use for whatever use case you want.

So if that means like, you know, authenticating to get the data, cleaning that data, making sure that it's not rep, uh, replicated with something else, making sure that it's harmonized, et cetera, and then standing it up for you to use, or if you're a business user and you're trying to answer a simple question like, you know, what is the close gonna be for sales this quarter in Chicago? Well, an agen process can be built and is being built by a lot of these vendors that will walk you through that little, basically without you having to write code or even build anything with a wizzywig or drag and drop. Just set you up to do that.

And what's making that possible is, uh, to your second part of your question, this, this introduction of, of several protocols and tools, um, that enable agentic ai and, um, this model context protocol with anthro, which anthropic a frontier model maker rolled out about a year and a half ago, uh, is part and parcel to that or key to that, because what it does is creates a sort of lingua franca for how a, an age agentic based, uh, or a agentic system that utilizes large language models, which is predominantly what we associate with, with ag agentic systems, uh, allows a large language model to basically find out what is sitting behind this MCP server and what can I do with it, you know, is it an MCP server that exposes capabilities? Like what can GitHub, for instance, do for me? Uh, what, what do I have access to?

What can I see and do there? Or is it just a data source itself? Like if, uh, back to what I was talking about with having an age agentic solution, basically stand up a sort of what happened at the end of the quarter, what is gonna happen at the end of the quarter that can be an MCP, uh, experience, if you will, for an age agent solution.

So the LLM basically works with that data through an MCP server. So it's becoming increasingly productized, increasingly abstract in a way, which we all know in this industry is, is there, there are only two ways forward. One, one is, you know, if you want more performance, you, you basically, um, cache everything.

If you want more simplicity, you create another layer of abstraction until you basically don't have to worry about the performance or the complexity of what's underneath you. So, Brad, I, I'm, I'm trying to get my head around the market a little bit here, and maybe you can help me with that. It seems like there's a bunch of different solutions that could all be labeled as agentic AI solutions, and I heard you mention here just now, um, basically, uh, tools, uh, that are used for, uh, categorizing and, and harmonizing and massaging data for data professionals.

I heard you talk about tools that would be used as part of a, an overall enterprise application stack. And I heard you talk about as well, tools that serve the, really, the, the needs of end users and business people. You know, essentially answer my question.

Um, you know, for example, your, your, your mention there of GitHub. Uh, another one that I know a lot of people are using is, um, being able to query financial market data, you know, just public financial market data, uh, being able to query, um, the weather, um, you know, being able to query, uh, all sorts of data sources like that. Um, e even down to, you know, the sort of things that people use assistance, like, you know, the, the s word or the a word from, you know, or the, or the, you know, the Google, uh, assistant, that kind of thing.

Um, and, and all of these, to me, they seem like they're agentic solutions, but they're all very, very different. You know, how would you break up the market? How would you categorize the world of applications in cer in terms of what buckets would you put things in?

Yeah, it's becoming much more complicated. Um, and, and I think that's fine, honestly, because if I'm a vendor and I'm serving a, a constituency in the enterprise, let's say, or I'm a vendor serving a, a consumer constituency, and each of those have, you know, tasks that they're trying to, to do. So back to the, you know, um, buying a ticket for a concert on a rainy day, you know, if I'm a consumer, um, and I log into Ticketmaster and I say, I wanna buy a ticket for a concert, um, and I've got three nights available to, to me, uh, and I want to only pay this much, and I want, I don't want an occluded view, and I wanna make sure it's on a night that has the best chance of not reigning, let's say, that would be an agentic process that I would expect Ticketmaster to build for me, such that my experience with Ticketmaster, um, would, would not, you know, be, I wouldn't be opening up another agentic process.

I would basically just be logging into the Ticketmaster interface and saying, I want a ticket. And I might do that either by typing it out, but increasingly I would just have my phone in my hand and I would be talking to my phone like, I'm talking to you guys right now, and I just, as I just said, I, I want a ticket that's on a night that's not gonna rain, and I wanna be, have a good seat, and I don't wanna pay more than X. Those are the things that, you know, you as, as a consumer would, would do, you know, on the phone, let's say, or in person, you know, in the last many decades to get something done.

And a Agent X software is, I think, you know, the best route that we have forward right now to, to at least approximate some of that. And the reason why it works is because the, um, nature of AIX software, as I mentioned at the outset, something that has autonomy, something that can plan, something that can interact with and make use of tools and information. What that gives you is, um, flexibility and, um, the, the ability, most importantly, to respond to, uh, changes and unanticipated changes in situations.

So, um, if I was using like a system that had a pull down menu that said, you know, what night would you, like, how much are you willing to pay? And I hit the button to go, um, if something happens, um, within that, that, you know, system, that workflow, let's say, um, that would, that would basically kick that out and say, that's not gonna happen. Sorry, I would've to start over with an ag agentic process and with, you know, the tools that we have at our disposal for asynchronous computing, um, that we use in the consumer software space right now.

So predominantly it doesn't matter, this system could basically just sit there and wait for the tickets to open up that I want and then make the transaction for me. And it's, it, the tool sets are, are becoming such that I would expect every vendor, whether they're selling to consumers or to the business, to then sell to consumers, that would be building agentic processes into any use case and any workflow in which that sort of flexibility and adaptability to a what would normally be a, a complex, hard-coded, you know, sort of problem. I, I, I think is going to be turned into agentic software and productized as such, even though to me, the consumer, I I might not ever know that that's really what's going on.

So, I'm sorry, that's a bit of a long answer to your question, Steven, but, um, it, it, it really to me, you know, says that we're gonna see a market that looks like this, how I would describe it, you, you're going to have the, uh, underlying tools. So in the scenario we just laid out, you would have, as we've been talking about, a, our wonderful MCP, you know, protocol to, to allow the models to understand what tickets are available. I would also have a, what's a, an A two A, uh, which is another protocol that, uh, Google developed that works with MCP quite nicely to allow disparate agents.

So the weather, um, service might have its own agent system with its own MP MCP servers that would deliver weather information. And Ticketmaster would have its own MCP servers and a two, and they would use a two A to talk to one another so that the models could basically say, so what's the weather gonna be? Is it changed?

What's it look like now? Um, and so you'll have these tools, these underlying technologies and tools. You'll also have the model makers and providers, which are increasingly building more of a platform than just a model.

So, you know, it used to be what we cared about were, you know, what can a model do for me when it's what responding to a query? But increasingly what we as consumers are paying for are the attentive services that go along with that model. So models look a lot more like a platform.

So if you look at Anthropic, you look at, um, uh, Google is a, is a great example with Gemini. You look at Mytral, all of these frontier model makers are building a, a very rich ecosystem of APIs, of supportive services for developers of software. So if I'm Ticketmaster, I, I'm gonna probably take advantage of these growing platforms to speed my time to market in building an agentic system.

And strangely, likewise, if I am a consumer and I'm just trying to do something for myself, like doing some research on, you know, what's the best night to go to a concert in my area this year and who's playing, uh, I, I could use the same tool set, the same tool set that, you know, Ticketmasters using to build this, you know, scalable, highly scalable solution. I would at a, as a user, as a consumer, use that the same way, and it wouldn't look any different to the backend, but it would look different to me. 'cause as I build it out, so, so Brad, that it's, it's a lot of information you provided.

So who are the companies we should, we should keep an eye on around Ag Agent ai? Yeah. Um, as, as I was, uh, mentioning a minute ago about, uh, you know, the, the marketplace itself and how complex it is and how almost every, you know, company you interact with is going to be building and using Ag agent software.

The same goes for, um, those who you might buy agentic technology from. So if I am, you know, uh, an AI practitioner and I'm building out AI solutions and I'm using a DataRobot or a data coup, or you know, any kind of, you know, AI platform, um, like that, I'm going to have agentic tooling coming from those guys. They're building it right now into everything they have.

If I am consuming models from the frontier model makers via, you know, OpenAI from Microsoft on Azure, if I'm using Gemini from Google, uh, or, uh, cohere, uh, from Oracle and OCI or, uh, any, any of their own models. And the same goes, for example, from with IBM and, uh, their Granite family of models on top of the IBM Watsonx platform. Uh, they're all building ag agentic tooling.

They're all building a agentic use cases. They're all, they're horizontal use cases, and they're also working toward building actual productized solutions, as we touched on briefly. So all of those folks, um, are, are the ones I, I guess I would watch out for first because the ones who are making the, the underlying infrastructure that lets me run ai, they matter in this.

The, um, manufacturers of the models themselves really matter in this. And as I mentioned a minute ago, those models look more and more like platforms themselves than just a model that you download the weights for and run on your local machine. So, uh, I would say that to start with those, so start with the, you, the AI platform vendors.

Start with the model makers and then branch out from there, depending upon what market you're in. If for instance, you're, uh, doing, you know, sales enablement managements, e you know, ERP, et cetera, obviously you can look to Salesforce with what they're building on top of Data Cloud, um, and Einstein, and you can, if you're a customer of SAP, you can look to what they're doing with Juul on top of their business technology platform. And if you are a customer of Oracle, you can see what, what they're doing with their own stack of, of line of business software.

All of that is, you know, seeing ag agentic processes bubble up through those, those software. And it's, it's actually getting such that, uh, and this is something I've had a little bit of hard time with because if, I think everyone who listens to this podcast has heard, um, Satya na Dala from Microsoft mention, uh, three or four weeks ago that he felt that software was itself gonna collapse, and that we would no longer or soon no longer, like, want to log into Microsoft Excel to use that, but instead might have a natural language interface that would be to Angen process, which would see Excel as a tool to use to get me the consumer what I wanted, instead of me having to open up a spreadsheet type, put stuff in columns, et cetera. It would basically, you know, take my question disambiguate, turn it into a plan, and go get the data and use Excel to do whatever calculations it might need to gimme what I want.

So you're gonna get it from whatever vendor that you, you interact with. So if you're, uh, an office user, as I just mentioned, if you are a, uh, Google, um, workplace user, you're gonna get it from them. If you're a Salesforce user, you're gonna get it from them.

It's, it's, it's everywhere. In other words. Yeah, that was, uh, what I was gonna say is, you know, Microsoft, I mean, you know, that they're, they're a primary provider of, um, of these tools for a lot of us.

And, and, and I I'm sure that many of us, you know, you mentioned Salesforce, um, Oracle, there are a lot of companies out there that are really trying to be the, the business CRM agentic provider. Um, yeah. You know, how does, how does somebody know who's got the best vision and who they should be talking to, Uh, us, uh, who they should talk to us.

Sorry, That that wasn't supposed to be a, a a, an ad, but, you know, I mean, basically like, you know, Well, If you're an end user and you've got, you know, an Office 365 subscription and a Google, uh, you know, work space and a, and a and a Salesforce and so on, I mean, everybody's trying to show you their vision, um, who's got the good vision. Yeah, I, I think those that, um, understand AI from a very pragmatic perspective, instead of just trying to chase, you know, benchmarks and, and, you know, being looking popular and, and focusing on how cool the videos are, you can, you can create, I think those that instead understand the necessities of performance, cost, security and governability and transparency, uh, and accountability, most importantly, that's the vendor you want to go with. So any vendor that that emphasizes those aspects, what I call, um, responsible ai, um, which is something we seem to have forgotten a little bit over the last year or so, but before that was, was very important in the enterprise.

Uh, but anyway, that, that's how I would separate the wheat from the chaff, honestly. And as I was mentioning, you should come to us because, um, we're, we're consumers and users of, you know, these agentic solutions and building out, um, what we call a living comparative reviews of these spaces. And one of them just happens to be a gentech, uh, platforms for, um, sales and, and customer experience.

And Keith Kirkpatrick, my, my colleague here, uh, just finished, uh, what we call a signal report, which is one of these living comparative reviews on those very products. We have another one coming out by, by my colleagues, uh, Diane Hinchcliffe and, and Nick Patients, that's, that's gonna look at agen AI platforms themselves. So I, I would say to anyone listening to this podcast, come, come back in, I think two weeks time, you should see a, a signal report specific to those.

Um, and these, these signal signal reports, uh, are, are actually built using a gentech processes. We've built a mechanism that takes all of the data that we as a, as a, an analyst firm aggregate and collect over time. So every conversation we have with vendors in the marketplace, uh, the briefings we take, the notes we make, uh, all of the information that we gather, it couples that with the information that is out there that the vendors are giving us and that they're publishing, it takes into account the, uh, voice of the customer and the actual experience of the customer through a partnership we have with G two, if you guys are familiar with them, and combines all of that into a, an automated living system that at any point, I, I can hit a button and it will generate a, a very detailed forward-looking, uh, analysis and assessment of that comparative competitive marketplace using all of these resources.

And so if you see an acquisition, for instance, like the one we like to use as a, as a, a good example is if Salesforce buys Informatica, uh, what will that do to the marketplace? That certainly would shift the power balance, shift the direction of the market itself. Um, and so we would, as an analyst firm, want to be able to have our living report reflect those immediate impactful events.

And that's why we built this as an agentic, self-correcting, self-assessing, you know, it, it, it basically just improves upon itself till it gets to the point where, you know, we as the builders of the system say, yep, that's it. You got it, and then we publish it. So it's, I'm excited about it, and it's, it's built using this technology that we're talking about today.

So what I think, what I find challenging today is agent ai and the innovation goes so fast, you know, how do you, how do you keep your report fresh, right? How, and, and, and maybe a recommendation for people who wanna learn more about agen AI and maybe protocols like MCP, I mean, MCP, what is it, like a year old or something like that? I mean, it's, it's, it's it, right?

It's already all over the place, but it's only a year old. So I think one of the challenges is that it's interesting technology, but it goes so fast. Is that something you address with report to, I mean, you, you talked a little about it being a living report.

Does that mean you, you kind of absorb information and as it becomes available and the report will spit out the right, the right data? Yeah, exactly. So it, it could, we could run it 24 7 if we wanted to, and it, it could just drop 20, I'm sorry.

They're, they're actually, um, ex extremely long. They're like really big reports. So this isn't like a one page report that we're building here.

This is like a deep assessment of 10 or 15 vendors, and we have five different metrics that we score for those vendors. Everything from the business value index of them, like how their, their finances go and all that down to the capabilities they're building into those solutions. So looking at the re release notes for a given product on a given day, looking at the, um, financials that were published that same day or the day before, taking those both into account and then building out the report based on that, on that information.

And so that's why I say they're, they're living entities and that, um, you know, instead of, what typically happens with we analysts when we build a comparative report is you, you gather, gather, gather, and then spend months sometimes writing a report and, and working with the vendor to, to finalize that report. And in the meantime, the market has moved on to something. Uh, you know, it's, it's like, okay, uh, just the other day we, we had, uh, a new protocol for a agentic systems that lets you purchase.

So lets, the agents themselves make financial transactions. So you have the A two A, and now you have A to P, which is agent to purchase, um, by the same company. So Google set this up and, um, that's, you know, that the, the market changed overnight because of that and how we build these systems out.

And so if I'm doing an agentic signal on H or sorry, if I'm doing a signal on agen AI platforms, I want a to p to be reflected in that, you know, who's adopting it, what are they doing with it, what's the outlook look like for vendors who are adopting that A to p you know, standard in their, into their technology stack. And I guess the only way to make that happen is to use, uh, these tools to help, uh, coalesce and, and sort and, and analyze that data because it is moving so quickly. As Frederick said, it's just an incredible area.

And, um, you know, Brad, we'll definitely be keeping an eye on the, uh, the whole, uh, the whole space here on this podcast, uh, utilizing tech focused on Agentic ai. Also, uh, we're gonna be doing a new, uh, podcast, uh, utilizing ai, which will be a weekly futurum Group podcast. Um, and, uh, of course we've got our AI Field Day event coming up.

So thank you so much for joining us, uh, today, Brad. Um, before we go, uh, where can people continue the conversation? Because clearly you've got a lot to say on this topic.

Where can they find you? Oh, they, they can find me on LinkedIn, uh, Brad Shiman, all one word. Um, it, you can find me on the Rums Groups platform itself.

com, uh, we publish a lot of material actually outside of our, our, you know, CU customer, you know, we have a, we have a customer area where we publish a lot of the deep research, like our forecasts and surveys. But a, a lot of data goes outside of that. And I would, I would encourage you guys to, to check that out because we do publish quite a bit, uh, at this company.

We, you know, our, our analysts are, are very fast. We, uh, we run, uh, quite, quite quickly. So, uh, at any rate that, that would be my recommendation.

Find me on LinkedIn and find me on, on Rums, uh, platform itself. Excellent. And, um, Frederick, uh, looking forward to seeing you at, uh, AI Field Day.

Where else can we find you? Well, you can find me on LinkedIn as Frederick v Herron. com.

Excellent. And, uh, as I mentioned, you know, you'll see me on Techron Gang, uh, most Tuesdays, uh, here on the Utilizing Tech Post podcast, as well as the forthcoming, uh, podcasts as well. So, um, thank you so much both of you for joining us for this episode of Utilizing Tech.

And, uh, thank you, uh, audience for listening. Uh, we're very glad to have you here. Uh, you can find this podcast in your favorite podcast application as well as on YouTube.

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