Boomi World 2025 | Chris Hallenbeck, Boomi & Shawn Rogers, CEO, BARC US

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Chris Hallenbeck

SVP and GM of AI & Platform

Boomi

Shawn Rogers

CEO

BARC US

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Chris Hallenbeck, Boomi & Shawn Rogers, CEO, BARC US

Chris Hallenbeck

SVP and GM of AI & Platform Boomi

Shawn Rogers

CEO BARC US

Chris Hallenbeck, SVP and GM for AI and platform at Boomi, and Shawn Rogers, CEO of BARC US, sit down with Savannah Peterson and Paul Nashawaty at Boomi World 2025 for a high-energy conversation on the accelerating shift toward smarter, data-driven operations. They dig into the urgency and opportunity AI presents across today’s evolving enterprise environments.

Hallenbeck breaks down the dual paths organizations are taking, some racing ahead with AI, others cautiously testing the waters. Rogers offers a seasoned view from the analytics space, pointin... Read more

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What are the different stages companies are in their AI journey, according to the speaker? add

What are some key considerations for effectively implementing AI technology in a business setting? add

What are some challenges related to data quality and how can the Boomi platform help address them? add

What are some examples of practical use cases that people deploy successfully first? add

What is the opinion on the use of AI in the field of customer service management (CSM)? add

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Chris Hallenbeck, Boomi & Shawn Rogers, CEO, BARC US

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Savannah Peterson

>> Good afternoon, Boomi fans and welcome back to Dallas, Texas. We're here at Boomi World 2025. My name's Savannah Peterson. Here with Paul today bringing you all the fun stories. We're talking platform, we're talking to AI, we're talking agents, we're talking about everything.

Paul Nashawaty

>> We certainly are. The platform is really where it's at. That unified view.

Savannah Peterson

>> Yes it is, baby. And even better to talk to us about that then, Chris and Shawn. Thank you so much for joining the show today.

Shawn Rogers

>> Good to be here.

Chris Hallenbeck

>> Great to be here.

Savannah Peterson

>> Good to have you back on, Chris, after it's been a few.

Chris Hallenbeck

>> It has. I'm super-glad to be back.

Savannah Peterson

>> Yeah, this is going to be super-great. And I'm really excited about this conversation because I feel like we're going to distill a little bit of myth from reality. And obviously, there's a lot of hype around a lot of what we're doing in AI and agentic. But I'm curious since you all get a pretty frontline view, where are companies actually in their AI journey? Chris, I'm going to start with you because you've got a grin on your face.

Chris Hallenbeck

>> I think they go to two. You have a very small percentage, less than 10%. They're like all in. They're making way. They hire the talent, and they're moving, and they're the ones who are like, "Oh my God, I need a platform." When you talk about agent sprawl, they're piled up at the AWS Summit in our booth going, "I hear you guys actually are the first ones with something there." And then you have everyone else who's like, "Okay, we don't have an AI budget. How do we do this? I get agent, but how does that really meet the road in terms of use cases and how do I do this and how I solve it and directly how do I create business value? I have to do that otherwise it's not cool." This whole thing of AI party trick to tell the board we're doing stuff doesn't fly anymore, and that's this moment. And they're like, "Show me the use cases by vertical, by domain. I can go have that. I can have impacts along with my business partner. And if you can do that and do that in a way that works within governance, I'm all in." And that's how the two groups settle out. Now we can hear. Shawn, tell me I'm wrong.

Shawn Rogers

>> No, I-

Savannah Peterson

>> .

Shawn Rogers

>> We do a lot of research at my firm on the industry and what we're seeing is an 80/20 split, 79/21 split. 21% of the people that are doing AI right now really laid down a very fantastic technology and policy foundation. So they're ready to play. And so those folks are the ones that Chris is talking about. They're out there, they're asking the right questions, they're trying to get in the game. The other folks are trying to figure out what to prioritize. They're answering to boards of directors who are like, "Do we have AI?" And they're like, "Yes, we do." And when you asked the question at first, I thought I've seen a lot of good slides. And I don't think that holistically across the vendor landscape that all of the vendors are ready. And a lot of them are over-rotating on talking about we have 10,000 agents.

Savannah Peterson

>> What does that mean? What's the business outcome?

Shawn Rogers

>> How are you going to deploy them? How are you going to manage them? How are you going to help your customers stay out of trouble? How are you going to make sure they're performing the way they do? I'm a much bigger fan of the adult version of doing agentic and AI in general, being the adult in the classroom saying, "Look, we can de-risk your innovation." I think that's valuable.

Chris Hallenbeck

>> Yeah, absolutely.

Shawn Rogers

>> Hundreds.

Paul Nashawaty

>> Absolutely. There's also this element of vibe coding and trying to experimenting and trying things, and then there's practical execution. And as an analyst, I back everything with data. That's what I do. That's my world. That's what I live in. So about nine months ago, I ran a study. And I said that 18% of the respondents indicated that they use AI in production workloads. We ran that study nine months later, jumped up to 54%. So a significant jump. And then now if I ran it again, I'm sure it's going to be much higher. So I agree with you. I agree that there's experimenting going on. I think that there's also, the pendulum is moving. Right? It's shifting. It's moving more towards, moving from experimental to production applications. And we're seeing that in the data.

Chris Hallenbeck

>> I think there is, but I still think there's one more dimension that you need to look at maybe and ask. I'd be super interested in what the data said, but what I've seen is a lot of people come out and they're like, "Yeah, we now have RAG on our knowledge base, internal document. And we got that stuff out. We're helping our service folks get answers faster." All good. And we also have stuff in HR, answer a question or do something like flight trip planner. That's all interesting. No auditor actually cares. And I don't mean that in a bad way. I have worked with companies where we did manual journal entries, brought into production and doing literally, tens of thousands a month being automated. You have to have audit logs. The auditors come in and they're like, "Show me those logs. Show me the transparent AI about why. Show me that the data that was brought in was fresh at that moment and it wasn't stale from this system." That's the adult stuff. That's the boring, but that's what has to happen. And if you want to do AI at scale, when you talk to the companies that are doing that and saying, "Are you using it in a regulated process?" Which is where the real gains come, I think that's going to drop down to a very small percentage.

Paul Nashawaty

>> I just have a quick comment on that and I think that there's an interesting piece here is pivoting from the tech stack to the leadership qualities and what's happening.

Chris Hallenbeck

>> Absolutely.

Paul Nashawaty

>> And when we look at it from that lens, the reason why I think a lot of what you're saying resonates, because the reason why a lot of organizations are saying, "Hey, we need to do these things. We're going to execute," is because there's a separate AI budget now. There's a budget that's over here and that's not the IT budget.

Shawn Rogers

>> I just shared this with the group. I gave a talk an hour ago. We've got a research piece that was interesting. 37% of boards are telling the ELT, "We have money. What do you want to do?" I talked to end users about where you're at in the process and now I'm starting to hear from end users going, "We put the word AI on all of our projects now because-

Paul Nashawaty

>> The money....

Shawn Rogers

>> they just get green lit." And I said, "Well, if you're smart."

Paul Nashawaty

>> I hadn't heard that.

Savannah Peterson

>> It's that simple. You've heard it here, folks. All you got to do is just put a little AI on it and the money comes out of nowhere.

Shawn Rogers

>> Well, it's not like back in the day when everybody updated their LinkedIn profile and said they were a data scientist. It was the same theory. But if you're smart, you're tagging AI to things like data quality projects, data governance projects.

Chris Hallenbeck

>> But I think that's valid.

Paul Nashawaty

>> I think so. I think it's a great idea.

Chris Hallenbeck

>> Because I don't think people are separating out like you did, all the projects they did over the last three years. We would do the whole studies of value capture and all the different use cases by business process. And then we came out and you would do, less than 10% could actually move forward due to data quality.

Savannah Peterson

>> Honestly, that doesn't surprise me at all because I think that's one of the biggest challenges that everyone has right now.

Chris Hallenbeck

>> We're looking for an in 12-month payback due to the huge budgetary crisis. Now, can I do that? And I think one of the great things we have about the Boomi platform is I can do really lightweight MDM using agents to clean. I can then go out web services, look at my Moody's ID, I can fill in missing values. I can do it data imputation, create the golden records and bring those to bear in that project in a really agile, lightweight way as I'm building out my agents. So data quality is up 10%. Good enough for that use case. Another 10, oh my god, two more use cases. And I can build quickly. I don't have to wait for this data products on data mesh that's promised in two years. It's always two years. It's like fusion. And you're like, "Okay, when can we do that?" And Boomi's platform enables you to do this in a crazy agile way. And I think that data quality aspect is going to be as big as these issues we're doing on governance for AI.

Savannah Peterson

>> So first of all, that's a wild way to narrow down what you're going to be able to work on and what projects are going to be able to execute. If it is a data quality problem, how can people solve that? How would you advise? What do you do in that situation?

Shawn Rogers

>> I think perhaps, for the first time, and I've seen it come and go before. But AI might actually be the forcing factor required for people to slam on the brakes, go backwards two steps, and solve some of their data quality and data access and orchestration issues. Many analysts have said that before that BI was going to be the forcing factor and this and that. But because of the autonomous nature of AI, you can't get away with being mostly right. Whereas we can in a BI analytics arena, if you have most of the data and you're pretty accurate-

Savannah Peterson

>> Plus or minus a little bit....

Shawn Rogers

>> you can make a business decision. But if you're going to let autonomous or agentic AI go running and doing really cool stuff with your customers or with your inventory, whatever, you better have your act together.

Paul Nashawaty

>> Well, BI gave you a lens into the business. It showed you what was happening, but it really wasn't ... You can take actionable insights from it, but what's happening with AI and engines now, it's not a tech problem, it's a business problem. And because it's a business problem, it's touching every element and we're automating along the way. And having a flexible backend in order to adapt dynamically, that bridges the gap between the old and the new. And that's where the Boomi platform comes in and goes, "Okay, well we can have those connectors, we can have that integration, if that's our core foundation for Boomi." But now you have some actionable insights that you can take and it's not just a visual representation of the data, it's actually taking action against the data.

Chris Hallenbeck

>> Yeah, no, and I think we make it so that you can accelerate your processes, reinvent them and do that, but it's not about using AI. Because people have said, "Can I go talk" ... In one of my interviews with customers. And I was like, "Don't go ask the business how they want to use AI. That's not it. Talk to them what their key problems are and then you on the tech side, if you can make that run better with AI, that's the use case and then just do it."

Savannah Peterson

>> You make it sound so simple, Chris.

Shawn Rogers

>> He and I had a conversation about this iterative step-by-step motion that you can do with AI, and I paraphrased you today during my speech.

Chris Hallenbeck

>> I'm honored.

Shawn Rogers

>> And I told the room, I said, "Don't boil the ocean, man. If you try to over innovate with this technology, you're going to end up chasing your tail around in circles, and you're not going to come out with where you need to be. Start small, conquer this, conquer that, and build upon it." And I think that's especially interesting as we move from traditional AI, which we all know has been around for a really long time, towards gen AI, which has some issues. And agenetic AI. And I love what we're doing, I just want people to do it in a safe, smart way and prosper from it.

Chris Hallenbeck

>> Because you can start small and maybe the value capture is not there, but now I'm building trust with the business that this is going to solve it. Then you do the incremental one, then you're like, "Wow, a little bit more trust built, higher value capture." And then you can move that up to then get to the crazy ones where I'm stopping margin leakage, I'm doing this. But you're not going to do that with your first project.

Savannah Peterson

>> What are some of those practical use cases that you see people deploy successfully first?

Shawn Rogers

>> Chatbots.

Savannah Peterson

>> Yeah.

Shawn Rogers

>> I think everybody starts with the simple. I think we're moving towards the complex. I see really cool use cases in using unstructured data in environments. I think that's pretty interesting and very easy to get instant ROI from. I think that there's an awful lot of augmenting already nice processes that are rule-based with agent technology in the flow.

Savannah Peterson

>> I think there's going be some really interesting stuff there.

Shawn Rogers

>> And I think that's one of the first places you ought to be looking. What are you already doing in your enterprise that would benefit from being amplified with this technology?

Paul Nashawaty

>> Sorry, that's a great response because I was thinking of a question I had to ask you. And I think you really just nailed it, which is looking at the audience that's watching this. They're asking themselves, "Where do I get started? There's so much out there, there's so many directions to go." And you just nailed it. Start with the things that you're already doing and make that better. That's what I heard from you.

Shawn Rogers

>> Yeah, optimizing your business is the number one place to start. Sales and customer interactions is the second place that we see most people going. The third category that people are focusing on is outright innovating. Doing stuff we've always wanted to do but didn't have the right tech. And the fourth bit is still around privacy, security, and mitigating risk. And so those are the four I see in my research segments. What do you see?

Chris Hallenbeck

>> I see that. I've seen a lot over time with matching, where I'm doing, whether it's trades, you're doing goods receipt, invoice reconciliation, I'm doing manual journal entries. These are things they stop you from closing the books on time. I have to have a number of accountants based on my high watermark. It slows me from closing the books. And you're like, "Look, the technology's there. And with an explainable AI, I can actually make those things run really smoothly and auditable."

Paul Nashawaty

>> We all need that.

Chris Hallenbeck

>> I know. Seriously. So a lot of these manual processes that are building up, they're kind of low value, but you're like, "I can now do those in real time continuously with human in loop for the exceptions. And then they can do that continuously and save time." Other class would be some of these systems like in sales. We saw that amazing demo where we automatically, a sales rep could listen to a call. It automatically transcribed the call, built it into a repeat, a value sheet, looked up ... This one we can look at the history of the customers, what they've bought. If they haven't, what would be incremental, and then have all what the products are and that. And then send it out. What that's doing? My best two reps are the only ones that ever do that, and they only have time to do it about four times a year. So talent and time. This enables your bottom core tile at the end of every call to behaving like your top two. And now, I've taken time and talent, and I've just condensed it. And so when you talk to people, whether it's contracts, RFI responses, sales, these are all amazing use cases. And marketing and sales, if they see money, they move and you can go. And I've seen a lot of uptake in those areas, in addition to the one Shawn was talking.

Paul Nashawaty

>> All right. I have to ask one question. This is off what you just said. I really like what you said, but one of the things I hear quite a bit about AI in general and where it's going with regards to skill and talent and skill gap issues. What I'm seeing and what I'm hearing organizations doing is they're not hiring new college hires. They're using AI to augment the entry level positions or the new college hire positions. What I see as an analyst, what I see as a risk here, is not so much now, but in the next five years, we all had a rite of passage where we learned on the job as a new college hire.

Savannah Peterson

>> And weren't paid a lot.

Paul Nashawaty

>> And then we learned all that. And then so at the end of the day, what happens in five years when those organizations are going to look for that skilled talent that now, they haven't had that rite of passage? What's going to happen?

Chris Hallenbeck

>> I think that's a challenge. I think it's a fair question, and I think it's where people need to take a look at the future. AI isn't strategic. I can have something that writes code, let's say early Copilots. They're amazing. What they don't know is to think about what does this look like from a maintenance whole cycle? What about upgrades? What about minor upgrades, huge ones, HA, DR, all of these advanced things, it's not doing it, writes that code. That requires an architect to gen code to accelerate and make them better, but they actually have to think that way. And so I think people coming out really need to focus on critical thinking and how do I do that to separate them out. And then using AI as that thing that makes them work faster and makes them work superhuman, but to bring what AI doesn't. And I think that's where the opening is, but the folks aren't doing that. I worry. I have the same concerns to be honest.

Savannah Peterson

>> Yeah, well I think it's interesting. So the Shopify CEO recently came out and said, "No new hires unless you can prove that AI can't do the job." Which was an interesting approach. The idea being that use the tools to augment your human capacity before you're going for more budget on the other side, which is kind of interesting. But the reality is I think that the jobs of today are not the same as the jobs of five years from now. I think that there's a lot of things that will change. And I don't know that almost looks at human potential as linear. These tools are going to allow us to learn and solve problems in very different ways. Even Luke, who we just had on the set a second ago, his job title didn't exist a year and a half ago. And there's a lot of people in that boat.

Shawn Rogers

>> So true.

Savannah Peterson

>> So I think, yeah.

Shawn Rogers

>> and you circle, you didn't say the statement, but you and I both know the quote quite well, "AI is not going to take your job, so getting back to the core of your" ... I'm coming out of college and I might not be able to get a job. It's people that know how to apply and utilize AI.

Paul Nashawaty

>> I like that response.

Shawn Rogers

>> So I know it's being overused right now, but it's also accurate.

Savannah Peterson

>> It is, yeah.

Shawn Rogers

>> You look at, I heard a fun example about a CSM agent recently, you know this story. And the CSM team within the company got a little panicky and thought they were being replaced. And I said, "No, this is a resource for you to leverage to do your jobs faster, better, smarter." So shying away from AI because you're afraid of it is not a good idea. If you're young and you're coming out of college and you're having a hard time breaking in, you need to show up with an AI skillset.

Paul Nashawaty

>> I like that.

Chris Hallenbeck

>> Do think it's a little overblown. We started out writing assembly and then all of a sudden you had, "Okay. I can actually write imperative code. No one said jobs are being lost." And then we went to imperative code and they're like, "Okay, yeah, that's great. Now I'm not writing in C and doing this. I don't have to understand the computer's architecture to write good stuff." No one was like, "Jobs are being lost." And now we're saying all of a sudden Copilot and everyone's screaming. And you're like, "Well no. It's just the continual march and everyone's going to be adapting moving forward."

Savannah Peterson

>> Absolutely. All right, final question for you both and I'm actually really curious to hear what you both have to say to this. When we're at Boomi World 2026, because we're not letting you go as long as you went last time without being on the show. What do you all hope to be able to say then that you can't yet say today?

Shawn Rogers

>> I want to talk about the maturity that we're going to see in the market. So I want to go from everybody's got great slides to everybody's doing really cool work. I told the room I was in an hour ago, "The next time I'm here, I'm going to talk to you about multi-agent chains of logic. I'm going to talk to you about agent swarms that are doing all kinds of really cool work." We're not there yet, but that's what makes it fun. The final bit I'll give you is I'm upset that I'm as old as I am at this point with this cool tech. This should have come around when I was 25. What the heck? I'm closer to the end of my analyst career than the beginning and I just find this so fascinating. So I think it's a great topic.

Savannah Peterson

>> It is fascinating.

Chris Hallenbeck

>> I'm looking right now, everyone's looking at how do I take my existing processes and improve on them? And what I'm looking for is to say, "Why are we still doing it that way?" And to start looking at the use cases, I want to see them doing it Boomi and say, "We thought about this. And we thought about what were we really trying to solve for and solving it in new ways." And then talking about that on stage with them. That's what, if I could do that next year, I'd be happiest guy ever.

Savannah Peterson

>> Well, we look forward to helping amplify that message when you can do that next year.

Chris Hallenbeck

>> Thank you.

Savannah Peterson

>> That's the goal. Shawn and Chris, thank you so much for being here.

Shawn Rogers

>> Thank you.

Savannah Peterson

>> This has been a joy. And thank you Paul as always, and thank all of you for tuning in to our two days of coverage here at Boomi World 2025 in Dallas, Texas. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.