In this interview from Appian World 2026, Bob LeBaron, head of the Workflow Center of Excellence at Neuberger Berman, joins theCUBE's Dave Vellante to discuss how AI-powered process automation is reshaping private markets operations. LeBaron explains how Neuberger Berman uses Appian for deal closing and investor onboarding across its private equity and private debt businesses, giving fund administrators and outside counsel real-time visibility into deal and fund status. He details how converging pressures — an influx of retail and high-net-worth investors, accelerating reporting cycles and more flexible fund structures — are driving demand for operational scale that traditional headcount models cannot support alone.
The conversation explores how LeBaron sees AI expanding the carrying capacity of each employee — potentially allowing the same team to support double or triple the firm's current assets under management. He describes document extraction as one of the most immediate opportunities, noting that AI can standardize data across dozens of outside counsel firms that each use slightly different document formats, unlocking structured insight from data that previously sat inert. The discussion also tackles the tension between agentic and deterministic approaches: LeBaron recounts how tasks that initially seemed ideal for AI agents — such as reviewing 200-page subscription documents — often prove more tractable as well-defined deterministic processes once properly analyzed. Looking ahead, he envisions Appian evolving from a standalone front-end application into a universal workflow orchestration engine, integrating seamlessly with any AI ecosystem to orchestrate CRM tools, document ingestion and AI agents across the full enterprise stack.
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Bob LeBaron, Neuberger
Dave Vellante hosts a conversation withBob LeBaron, Head of Workflow, Neuberger, at Appian World 2026 from the JW Marriott Orlando, Grande Lakes in
Orlando, FL.
Head of Workflow Center of ExcellenceNeuberger Berman
In this interview from Appian World 2026, Bob LeBaron, head of the Workflow Center of Excellence at Neuberger Berman, joins theCUBE's Dave Vellante to discuss how AI-powered process automation is reshaping private markets operations. LeBaron explains how Neuberger Berman uses Appian for deal closing and investor onboarding across its private equity and private debt businesses, giving fund administrators and outside counsel real-time visibility into deal and fund status. He details how converging pressures — an influx of retail and high-net-worth investors, ac...Read more
>> Hi everybody. Welcome back to theCUBE's live coverage of Appian World 2026. We're here in Orlando, Florida, the JW Marriott, winding down day two of our three-day coverage. We did CUBE After Dark on Monday. Today was the big day, keynotes all day long, multiple interviews. Check them all out. SiliconAngle.com has all the news, thecube.net. All these videos are on replay. Bob LeBaron is here. He's the head of Workflow Center of Excellence at Neuberger. From Chicago, welcome to Orlando. Good to see you. Thanks for coming on theCUBE.
Bob LeBaron
>> Yeah, thanks for having me. Appreciate it.
Dave Vellante
>> You're very welcome. What is the Workflow Center of Excellence? How did it get started? What's your role there?
Bob LeBaron
>> So that's the title that they had for it, but to be perfectly frank, my area is very focused in the private market space. So I do consult with a lot of others at Neuberger who are not in private markets as they're looking to implement workflow solutions. They'll come to me. We have an inventory of the various workflow tools that we have available. We might use ServiceNow for something like our IT servicing and ticketing. But for our more standard workflows, business process workflows, that's where we utilize Appian. And I'm specifically aligned to the private markets business at Neuberger.
Dave Vellante
>> Okay. And so how would you describe your mission in that context?
Bob LeBaron
>> So the mission there is really to work closely with the business to understand the ins and outs. As you know, private equity, private markets is very, very bespoke. There's no off the shelf software that you can just use to say this is going to be how we're going to run our private markets business. And so what we strive to do is to make sure that we have the proper data governance over the data, that we are understanding the inputs, the outputs, what constitutes a process success, what constitutes a failure. And then we collaborate with the business. There's also a big change management component with a lot of these workflows and making sure that we're getting consensus across the various heads of business as we're implementing these various solutions.
Dave Vellante
>> Bob, how would you describe the state of the market right now? I mean, private equity is, I mean, obviously a very interesting and dynamic business. What's the climate like? There's been fewer IPOs, that seems to be picking up. Now with this geopolitical stuff, we'll see what happens. M&A and the previous administration was tough. So there's sort of longer exit cycles, longer liquidity cycles. Is that putting pressure on the business? How does that, or does that affect you at all? What are the drivers, I guess, is the real question of your business?
Bob LeBaron
>> We're fortunate to have a really wide array of investment strategies that we have. And so we have some more traditional primary, secondary co-investment funds that we have, but we have some other strategies as well. Some of the things that we're noticing a lot is that the... There's all these things kind of converging at once. There's the advent of AI, AI agents, and the capabilities that those unlock. At the same time, from an operational standpoint, we see there's a lot of opportunities coming in from retail investors, some high net worth individuals, but also maybe some intermediary partners platforms who are looking for places for their clients to put their money. And so what used to be something that was maybe more reserved for institutional investors only, now suddenly there could be several thousand investors who are interested in a product, and it presents a whole slew of operational challenges too that is from a private equity business, we're having to come to grips with that we have increased scale that we need to be able to handle. And then also, I think generally what we're seeing is the reporting cycles of performance data where you would typically report on a three month, six month lag, people are wanting data sooner rather than later. Also, going along with that, there's certain people that don't want to just be able to park their money and have it be locked up. People are wanting to have greater access to their funds to be able to redeem and to invest. And so you sometimes will also see some of these hybrid funds where they're essentially open, they're still closed end funds technically, but there are opportunities to be able to exit. So maybe you could come in on a monthly basis and exit on a quarterly basis, things like that.
Dave Vellante
>> Interesting. So you're scaling, obviously. You got to have more optionality for your client base. You got to be more flexible. You've got to have product agility, what you just described, close in funds that can actually get liquid for whatever conditions you sign up for. And then you've got... Well, some of it's compliance. Others might just be market edicts, competitive realities where you have to be able to report more frequently or they're going to go to the competition with the client's demand. So all of that is sort of pretty dynamic. How does Appian help you address those challenges?
Bob LeBaron
>> So for us, we utilize Appian in a couple of different key areas. We utilize it as a deal closing workflow for our private equity and private debt businesses. We also use it for our funded investor onboarding. And so that also includes whenever we're deciding to launch a new product or a new fund, that is going to come with a whole bunch of legal entities. There's going to be master funds, feeder funds, there's going to be blockers, AIVs, potentially some aggregators, things like that. There's also going to be investors that are coming in through various closes. And so each of those things, there's certain things that you just set up at the fund level. There's certain things that you set up at the entity level and you have to set up the entity, but you also may need to enrich it with some additional detail. And then same thing with investors and we also have a bunch of third parties who we do business with, such as our fund administrators and our outside counsel firms. So it also allows us for a platform to be able to give them secure logins so that they can come in and they can see real time the status of our deals, the status of the funds that we've set up and those types of things.
Dave Vellante
>> How about things like meeting a fund's objectives? I've always been fascinated by that. I mean, kind of the old days, fund manager had to make sure that whatever he or she was investing in mapped to the objectives, they weren't violating that. Every now and then, there was some gray areas and they might get a little slap in the hand. But how is automation? Is automation helping that whole process with aligning to those objectives?
Bob LeBaron
>> So those are some things where we've looked into some solutions and looked into where we can potentially utilize some AI. That's not something that's currently done inside of Appian, but certainly we're looking at investment guidelines, portfolio construction and management, and how do we manage the risk? If you were to execute on a specific deal, how does that change the risk profile of the fund? Are you now overweight or underweight on a specific industry or a specific issuer or things like that?
Dave Vellante
>> So you're saying Appian's more risk management and compliance, is that correct or?
Bob LeBaron
>> It's the deal closing. So once we've decided to go ahead and do the deal, and so all of the portfolio construction stuff has happened already at that point, but we know that we're going to do this deal and so it's making sure that we have done all of the risk compliance and that we've done all the operational checks that need to be done. If the deal needs to be allocated, like maybe we know that we're in for it, but we don't know exactly who is going to fill that demand. And so we send it out to the various funds and separate accounts that we manage and people put in for their different bids. And if it's a good fit for that fund or for that investor, then we may allocate a portion of that deal to that fund.
Dave Vellante
>> I see. And I mean, ultimately you're measured, I guess, on alpha relative to a benchmark. That's kind of the ultimate success how your clients are going to evaluate you. I'm curious as to how AI, specifically Appian generally is helping you achieve that. Is there a linkage between your ability to achieve those funds objectives?
Bob LeBaron
>> Yeah, there's a couple of different things kind of packaged in there, I would say. So for our firm, we've always had a historical link between our employee FTE headcount needed to support the business and the AUM. So that's our assets under management. So if we have 500 billion in AUM, then we need a specific number of employees to be able to support that. Kind of going along with that though is, can we utilize AI to increase the carrying capacity of each employee so that now instead of... Could the same number of employees support double or triple or quadruple the AUM if we build it in a way to scale? Kind of buried within that. I would say that normally when we think about AI, we're thinking about how can I automate some existing processes? I think it's also important to take a step back to say, are we doing the existing processes in the right way to begin with? Or do we understand our processes enough to be able to actually get the most bang for your buck on everything that we're doing? And we have a lot of examples where we might have a horizontal function that is supporting all of the business. So for example, we have like a finance team. That finance team supports the various investment verticals, but how are you handling the marketing of your funds? Do you have a centralized marketing team or do you have somebody on the investment team who kind of knows a little bit about marketing and they're doing that? And so there's several examples of those that I know about within our firm where we're trying to say, "Hey, let's let the investment team invest. Let's let the deal team source deals and really seek the alpha within their own organizations as opposed to just doing something that's not really their strong suit."
Dave Vellante
>> Interesting. So I think what I'm hearing is you can take your assets under management divided by labor that equals X and that X has potential to grow.
Bob LeBaron
>> Yes.
Dave Vellante
>> Have you seen growth there thus far or is it sort of early days? As a direct result of AI and?
Bob LeBaron
>> Yeah, for sure. We've seen some of that. Yeah.
Dave Vellante
>> So, okay. So that's scaling with less labor.
Bob LeBaron
>> Yeah. Yeah. And there's also a combination of things that AI is able to do. AI is able to do some of the things that a human would normally do and only report back the exceptions that somebody should need to care about. It also in some cases is doing things that just weren't done before that maybe should have been done or they were kind of like nice to have, but now that we have AI, we can actually do those things. So there's a lot of great opportunities with AI.
Dave Vellante
>> I don't want to put you on the spot, even though I do, because of the nature of your business, but that ratio of AUM over labor cost, there's so much hype in this business. So let me ask the question this way. And you would expect the hype would imply an order of magnitude or more of leverage on that type of ratio. Can you see that type of potential? Is it too early? Is it just a bunch of technology industry mumbo jumbo and it's more about your ability to run your business? How would you respond to that?
Bob LeBaron
>> I think it's crazy when you actually start getting into the weeds to see what AI can do. Just for me personally, as I've utilized AI and embarked on doing simple things, like I'm in charge of an Appian team and I am in charge of the development. I know how long it takes to build certain things. For me, the first time I built a prototype with AI, my mind was completely blown. First of all, that's not going to be production grade. There's no database that's connecting the data that I'm seeing on the screen, but just the fact that it could create it, I knew for myself that I was underestimating what AI could do. And I think that so many times too, we think we're sometimes limiting ourselves in terms of what we think AI can do just because it couldn't do it six months ago or it couldn't do it a year ago. And really, I think it's imperative for all of us to really test the limits and to check ourselves because I think that even the way that I build something right now is likely going to change in three years.
Dave Vellante
>> And so you manage developers?
Bob LeBaron
>> Yeah.
Dave Vellante
>> Do you give them a token budget?
Bob LeBaron
>> Not right now. My developers that I manage are all Appian developers. And so I don't think I really... I'm not even close to hitting my token limit on the Appian side, but we do a lot of development as well using CloudCode, other things like that on the non-Appian side.
Dave Vellante
>> Help me understand that. So on the Appian side, as they start to roll out agents, you'll be consuming presumably more tokens, correct?
Bob LeBaron
>> Yeah. Yeah, we will be.
Dave Vellante
>> Okay. Okay. So it's a maturity thing. It's not a factor?
Bob LeBaron
>> Yeah. It's a maturity thing. Right now, it's kind of one of those things where I couldn't tell you exactly how much I have in my bank account, but I know that I'm fine to buy stuff at the grocery store. It's going to be fine. I know I could go buy a TV and I would be fin., but as things get tighter, like as we start building more and more, I think we'll start to hit and brush up against some of those things. As we're going through and planning too, it's good for us to think about and kind of be budgeting for the future so we don't waste all of our tokens on stupid stuff that we don't want to be bothered with. But right now, my understanding of our limits is we're not close to hitting them on the Appian side. Even on the things that we're not building in Appian, nobody's hitting up against these thresholds that like we're getting a warning from leadership saying, "Hey, you're using a lot of tokens." The only time I get those types of warnings is on my own personal accounts when Claude's like, "Hey, you've done enough. Wait an hour and you can use it some more."
Dave Vellante
>> Yeah, you're getting limited, rate limited. But ideally you want to get to the point where you're consuming a lot of tokens and it's generating a lot of value, revenue producing value. It sounds like for the Appian stuff, it's more right now anyway on the compliance side. Do you see bringing more of the sort of revenue generating capabilities into the Appian platform or?
Bob LeBaron
>> Yeah. Yeah, absolutely. Well, utilizing AI, I don't know if it'll be on Appian or if it will not be on Appian.
Dave Vellante
>> You'll make that decision as .
Bob LeBaron
>> We'll make that decision. But some of the areas where we see a lot of potential, which a lot of people I think are using it for document extraction, it's really good at document extraction, which is a great thing for us. You would think that we control our own destiny in terms of data structure and standardization, but you could have a dozen, I don't know how many outside counsel firms we use, but it's more than I thought that we would have before I started. And so each outside counsel firm has their slight variation of our own internal docs that we have and as we're investing in GP funds, they all have their own types of docs. And so it's kind of one of those things where generally the information is the same, but that format is a big barrier of entry to give to any analyst, to have them to be able to gain any sort of economies of scale. Now that you have AI, it's much easier to be able to standardize, to structure that data and to be able to really start to unlock the wealth that you were just sitting on, which is just a whole bunch of data, but nobody could structure it and harness it before.
Dave Vellante
>> So Matt in his keynote today, CEO of Appian, kind of laid out a scenario where you've got to bring the probabilistic world and the deterministic worlds together in order to have confidence in governance. They made the case that they are quite expert at doing that. Has that been your experience? Does that message resonate with you? What are your thoughts?
Bob LeBaron
>> I think what's tricky is lots of times when you... I think lots of times if you actually go through and solve a problem the way it ought to be solved and put the actual analyst power on it that it probably deserves, you're going to find that a lot of the problems are actually more deterministic and less variable. That's one of the problems that we've seen before where we're trying to implement a solution and we're super excited about using Agent Studio, something like that. And say I wanted to just read my sub docs. That's currently, I would say, a non-deterministic task for a lot of my coworkers where they're reading a 200-page sub doc and I'm giving them general guidelines on what to look for, but that's because we're constrained because somebody can't pull out all the stuff. It's not reasonable for them to pull it out. And so we just kind of give them some general guidelines and that's like more of like the art of it. I just want to give an agent instructions like I would give a junior associate and have them go and do.
Dave Vellante
>> Yeah.
Bob LeBaron
>> But the right thing to do is go to page 7 or to page 11, depending on which one it is, and pull out this exact term and pull out this exact thing. And so it's kind of tricky because I want to use agents more, but now with AI, you kind of think, "Well, actually, should I just go all the way and do it deterministic and do a better job with it?"
Dave Vellante
>> Interesting. Wow, that's really... Do you think that your thought process on that will change as the harnessing and the scaffolding matures or is that a kind of a non-obvious insight that you've just uncovered?
Bob LeBaron
>> I don't know. At the same time, I never would have thought that AI could do coding. I never thought that AI could do song generation or like animate videos or do any of these types of things-
Dave Vellante
>> Tell good jokes even.
Bob LeBaron
>> Tell good jokes. And it's doing those things. And so I usually, what I always try to do is I'm about to go out and do a really good build. I'll usually just try to just one shot it with a prompt and just say, "Hey, could it do it?" Because you just don't know until you try and sometimes you're surprised at what it can do. I wouldn't recommend to somebody on the street or as a former consultant, I wouldn't go in and be like, "You should use AI to reconcile all your spreadsheets, but maybe we're going to get to that point." And so that's where it's a little bit tricky.
Dave Vellante
>> Last question. So thinking about your relationship with Appian, what you kind of like to get from them, if you're here... Usually I ask this question 12 months out, but let's say you're here 24 months, Appian World 2028, I don't know if they've announced the location yet, but let's assume we're 24 months out. What do you want to be able to say with this company today or then that you cannot say today?
Bob LeBaron
>> That's a good question. I want to be able to see a natural path to be able to integrate the ecosystem with really any type of AI ecosystem. I think what I mean by that is I want to see it be very plug and play with various things. Even within my own business, we do deal closing, but not for every strategy, just in the private markets, but then there's also the whole public markets where we're not even utilizing it. So if I'm an organization, I need to be able to have my own AI governance strategy and my own agentic strategy, and then how do I make sure that I have conformity within there, but then I still have some of those things going on in Appian? And frankly, honestly, every SaaS provider out there who has their own version of agents, their own version of AI. And so it's just really making sure that you have a robust governance model and how do you make sure that it fits and plays nicely and seamlessly with all that stuff.
Dave Vellante
>> Interesting. Is data fabric kind of a path to that? Are you using data fabric today or?
Bob LeBaron
>> We use data fabric, but it's still kind of within the Appian ecosystem, right? What I would like to also see in the future is that I'd like us to be using Appian as our workflow engine for everything. Right now, Appian is kind of confined to standalone applications where the whole user interface and backend and everything is built within Appian. But I would actually like to see it where we're just orchestrating a whole bunch of different applications, like be it the CRM tool and the deal management tool and all these different things. I should be agnostic to whatever tool we're building. If I've got a better front end somewhere, who cares? You use that, but I would like to have standardization and I hate to see when some of my coworkers are building out like part of a workflow engine on their own, probably good learning experience for them, but like I would love to just take the years of experience that we have and just have a standardized workflow engine that we're utilizing for all of our .
Dave Vellante
>> That Appian-like capability across all your state.
Bob LeBaron
>> Yeah, yeah. And stop thinking of it as just a front end and just really think like, how can I utilize the workflow engine and how can I utilize the AI and the agents as individual process models? I shouldn't need to care about the front end.
Dave Vellante
>> Kind of a headless, happy in.
Bob LeBaron
>> Yeah. Or potentially how can I, anytime I need to intake documents and extract data, because they have a really great user interface and experience for basically like geo tagging the fields in a document. I can extract it and I can say, "Oh, where's the social security number?" And I click on the button and it pulls over to there and you see everything. So it's start thinking of it almost as like micro apps too, where anytime you intake it, maybe it goes in and goes through that process and then it feeds to whatever system it needs to feed to.
Dave Vellante
>> Yeah, good little... Good challenge you just put forth for the company.
Bob LeBaron
>> Yeah.
Dave Vellante
>> Well Bob, thanks so much for coming on to help us close out day two. Really appreciate it.
Bob LeBaron
>> Yeah, thank you. Appreciate it. You bet.
Dave Vellante
>> All right, this is Dave Vellante for Alison Kosik and the entire CUBE team where we're wrapping up day two here at Appian World 2026. We're back tomorrow morning, first thing, we are starting early East Coast time. We kick off at 8:00AM again. So tune in siliconangle.com, thecube.net, thecuberesearch.com for all a deep dive. I'm just about to post something on the keynote this evening, so check that out. We'll see you tomorrow. Thanks for watching.
Dave Vellante