Julie Bilodeau:
Hello and welcome to BDO Digital Digest, where we are your go-to for the latest of innovation in technology. From us here at BDO Canada, I'm Julie Bilodeau and I'm joined here by many more than you're used to seeing. So we have Hamed here as our co-host. We have-
Hamed Faghfoury:
Hello.
Julie Bilodeau:
We have Matt Glenen here, and we have Ahmad Ovais here. We're here to help you explore how evolving technology propels businesses forward and reshapes our work. And specifically, in today's theme, we're talking about private equity and how it is quietly rewiring the tech world. And tech is reshaping how private equity wins as well. So from billion dollar take privates, to cyber security, to once in a generation build out of AI infrastructure, the playbook is changing. So today we're going to unpack how capital, tech and Canada fit all together.
And so as I mentioned, we have Matt Glenen here. He's one of our technology consulting partners. He heads our business applications area of BDO Digital, and he's our private equity leader for technology. So he's leading transformations across our Canadian enterprise businesses. So happy to have you here, Matt. I also want to mention for our audience, that he also contributed to our BDO 2025 Private Equity in Canada Report. So please check that out later and unpack more of what tech's value is as a leader for our PE companies. You also see on the screen, Ahmad Ovais is here, again, part of our BDO Canada Digital and AI Strategy and Transformation, one of our partners here. Who advises clients across industries like private equity on how to effectively plan for and adopt AI in the organization, to get measurable value. So I think we got the right people here today. I think we're all set for our talk in private equity.
So just to frame us off and kick us off a bit from a few statistical standpoints, to really hone in on why we're here today. Turns out, in 2025, 27% of private equity spend went into 10,000 billion, 10,000, $10 billion deals. I wish you could say tens of $10,000 billion deals. So scale is back as per an EY report, and also we have a landmark of $50 billion of private capital partnership being deployed to build AI data center capacity, that's on a KKRV report. So just a couple of statistics around PE, how things are happening and why we're here to talk about it today. So thank you all and let's dive into this amazing topic.
Ahmad Ovais:
Thanks for having us.
Julie Bilodeau:
No problem. So first, I think we could kick us off with really around how private equity firms seem to constantly be looking for ways to streamline their operations and maximize the value creation. So where do you guys see the biggest opportunities for efficiency gains through AI in the PE life cycle? From like deal sourcing to portfolio management, what do you say?
Matt Glenen:
Well, Julie, thanks. I'll start. And thank you again, very much, for having us. Super exciting topic. I feel like we almost have to date and time stamp this, because everything we say could change, AI is moving at lightning speed. So I'll maybe talk, we mentioned private equity a few times, I'm going to define just quickly private capital. So private capital is broadly a term that covers infrastructure firms, real estate firms and private equity firms, but for our purposes today, we'll talk about PE or private equity. And so like any industry, we see PEs at all different stages of maturity when it comes to their AI adoption. So we've got some early adopters, they've got clear plans, they've got teams assigned, they've allocated funding, they're making a deliberate choice, really, to be a progressive AI leader, and seeing AI as a strategic advantage.
And often, we see these as some of the larger, more well-funded funds that can really allocate the resources to be an early adopter. We see some firms in maybe more of a wait and see, or a wait and watch approach. They watch what others are doing. They're making a deliberate choice to be enabled by AI. We see this kind of at all sizes and shapes, but certainly some private equities, if we think of mid-market PEs, some of them are fairly lean shops. They don't have a huge bench of people to redeploy on thinking through how to maximize AI.
But if you step back, really probably three kind of typical patterns that we've seen emerging in AI adoption and private equity. So the first is, using AI to really help and facilitate good, better decision-making. So PEs sit on a ton of information, they have a lot of data, in some cases they have complex models they use to evaluate a deal thesis. Often their data is fragmented, information's kind of scattered and they need to move fast. They need to make rapid decisions on acquiring a business or divesting. And so the tendency has, in the past, been, make these decisions on instinct or maybe gut feel, with a little bit of data informing it. And so now AI can be used and is being used to make that a much more repeatable, reliable, just passionate process. So you can make a quick decision using consistency, being data-driven and waiting and risk, and then you can train your AI to help you find better data and use that information more effective. So really decision making. Decision making, I think is kind of number one area we see adopted.
Number two is, running their operations. So whether that's their deal teams doing research on a potential target or maybe assisting with the diligence process, maybe reading a CIM, Confidential Information Memorandum and summarizing it quickly, or communicating with their investors, their LPs in legal, writing a contract or analyzing a contract. Now this can be done really, really efficiently with generative AI. In finance, doing a journal entry, looking at expense reports, all sorts of use cases to run their core operations more efficiently.
And then the third way, and this kind of really varies private equity by private equity, but it's helping their portfolio companies adopt AI. So helping their portcos implement an AI strategy to get a lot of value and streamline their operations. So lots of ways. Again, date stamp, because it's changing all the time. Capabilities of AI, they're growing exponentially.
Julie Bilodeau:
Yeah, the use cases sound endless, Matt, the way you're talking. Okay.
Hamed Faghfoury:
Yeah. And sort of piggybacking on that, and I know, Ahmad you've been living and breathing the AI to ROI motion here at BDO Canada, you're leading the way on this narrative. And when you think about ROI in terms of private equity, one would generally say traditionally it's measured in financial outcomes. It's sort of what would make sense. Now, do you see AI shifting that conversation in what you're seeing on the ground? Are you seeing, not just in terms of faster returns, but are you seeing it unlock new possibilities and new value drivers as a whole?
Ahmad Ovais:
Yeah. I think August 2025, Matt, let's time stamp it. I think when we think about ROI, when it comes to AI, it has to be looked a bit differently. As we have drove market impact just to see our, I would say ROI for AI should be across three different layers. Layer one is what I think Matt talked about a lot, which is the measurable ROI in terms of inward and outward value, whether that is top line or bottom line or general value savings, efficiency, productivity. And there is ton of it. And some of it is quick wins and some of it is strategic value, but I think it's measurable. The other part is truly value differentiating layer, which is, how can we use AI to be a different type of organization? Where we can go to market differently, where we can partner differently, where we have a different strength than a typical private equity firm. And then third would be a capability ROI, which is, how can we help our people to improve their curiosity and competency-
Hamed Faghfoury:
Right.
Ahmad Ovais:
Drive more agility and flexibility in the changing marketplace? Because what's happening is, the cross broader challenges, complexities will continue and the market is continuing to have interesting ups and downs and ebbs and flows. And so what that really means is for a normal business staff member or a leader, world is complex, it changes on a daily basis. And so within that you have curious people and traditional people, AI will supercharge the curious folks. And so those folks will drive more value in the future. So it's the measurable outcomes through quantifiable ROI, it's strategic ROI through value differentiating moments and measures, and then it's capability ROI through skill building and curiosity outlets. I would say all three are powerful. If we look at time horizons, I think in the short term it's more measurable, but I think in terms of sustainable competitive advantage, as Porter says it, right, is going to be more about capability and strategic.
Hamed Faghfoury:
Got it.
Julie Bilodeau:
So Ahmad, how does a firm go from experimenting with AI, which a lot of them are kind of dabbling, right, what does this do and what could we expect out of it? To having it, obviously connect to their investments to make these measurable business outcomes? Are there keys that you see that will move them from just being these AI experimenters, to those that actually truly deliver on that AI ROI?
Ahmad Ovais:
I mean, I think most of us have gone through the Gartner Report that came out two months ago and the MIT report that came out last week, which talks about the challenges organizations face in experimentation. I think Gartner frames it as POC purgatory, MIT report has some other term. But end of the day, what a lot of research and analysis, and I mean from an AI to ROI perspective as we interacted with a ton of clients and are delivering projects, the key reasons why organizations struggle with getting ROI are fundamentally three. One, is the absence of good old technology planning.
Julie Bilodeau:
Yeah.
Ahmad Ovais:
I think a lot of organizations thought, "Oh, we can just get to AI without any level of-"
Matt Glenen:
They wanted a shortcut. They wanted a short-
Ahmad Ovais:
Right.
Hamed Faghfoury:
It's magic.
Ahmad Ovais:
It's magic. And I think we don't do that when it comes to any other digital transformation. And so some of that was suspended as a hope and optimistic way of doing it. A lot of companies are recognizing that without a clear intention, vision, purpose approach to it, you are basically attempting a lot of different things without cause. And-
Julie Bilodeau:
Ahmad, this reminds me of if you plan, what is it? If you fail to plan, you plan to fail. That's what this reminds me of.
Ahmad Ovais:
Yes.
Julie Bilodeau:
Yeah.
Ahmad Ovais:
Correct. And one of my leaders in the firm, Sam, he talks about it, like sometimes you have to go slow to go fast, and I think everybody tried to go fast on day one and it just doesn't work when something as complex as AI at play. So I would say that's one element and it's very fixable element, it is like let's build a good plan in a business case to move forward. The other element is not enough readiness when it comes to technical and data. A lot of organizations have extensive historical data and private equity firms have a lot. Think about the portco reports they get on a quarter basis, all the thousands of contracts and legal documents, extensive amount of data. But you can't crawl through it, you can't really use it all the time, you have to clean it, organize it, classify it before you can make it usable. So that talks about a bit of a data readiness, and we are not even touching the data within systems, which could be petabytes of data.
And then you have technical readiness, which is, step doesn't talk to each other. So if I want a perspective using AI, that brings together multiple data sources together, well, if those sources are not connected, it's going to be a very hard pull. And so having a bit of a technical readiness, a data readiness will supercharge the complex AI use cases where a lot of value resides, where a lot of personalized outcomes reside. And I would say the third thing is, responsible adoption. And so a lot of organizations think about AI as something that we will train during business testing. Just before they go live, we're going to train the business users on how to use it and all will go well. It never goes well, especially for something as vague as AI. Staff has massive concerns about their life, their job, their outcomes, and so you have to bring them along early on, you have to articulate what's in it for them and you have to do it responsibly.
So from a PE perspective, one of the things that we are observing quite a bit is sensitivity to precision and quality, that's paramount to their business. And so there is extreme aversion to bias and hallucination and poor quality and lack of referencing. So we have to make sure responsibility is not lost. A lot of organizations are getting to adopting ISO 42001, which is one of the more leading standards. But again, any standard is better than no standard. We need to figure out a way that we can have proper observability, grounding, objectivity, traceability when AI responds and we need to bring our people along things.
So I would say those are the three things. And if an organization does those three things, while they are experimenting, or as part of their experimentation, they will get a much higher probability of value and outcomes than, let's just try five different use cases and somehow one of them will work.
Julie Bilodeau:
Yeah.
Hamed Faghfoury:
It's actually a good segue to one of the questions I had for Matt, in terms of talent and culture generally and that change management. We're all using AI in different ways as part of our daily lives, and we've talked about that on other episodes as well. And it's really interesting when you start looking at how that presents a challenge in some ways, which you have with organizations that see how that adapts to their team and their culture. So how are PE firms handling this? How are they coping with that change, this idea that there's implications with using AI and challenging ways to change the way they're working, ultimately?
Matt Glenen:
I think, yeah, Ahmad covered some of this, but again, I'll come back to maybe the concept of this being a journey or a spectrum. If you think of the first interaction most of us had with AI, was logging into Chat GPT and asking it a goofy question and getting it to write a story for us. So that first interaction, and really, the first way organizations are adopting AI, is what we call the layer of individual empowerment. So people are using AI individually to do simple things, like maybe I use Copilot to take my meeting notes, or I use something to help me write an email, so it makes my job a little bit easier.
And at organizational wide, it's hard to necessarily see those outputs as you move up the maturity scale, so kind of stage two, is team enablement. So teams are using AI together to perform particular functions. So perhaps we've implemented a decision making model or we're using it to, again, back to the idea of a deal team using an AI-
Hamed Faghfoury:
Right.
Matt Glenen:
To make part of their process better. Moving farther up the curve, is team augmentation. So now you've built an AI agent who is a team member, who kind of behaves in their own right, does a particular function on the team, fulfills a job. And then kind of the latest stage of maturity or the last stage of the journey, is when you're operating a full AI operating model. You have multiple agents taking over parts of a business process. You can imagine the organizational impacts really change depending on where you're at in the spectrum. At the beginning, you need to teach people the basics, you need to find use cases, you need to do experimentation, you need to train people how to prompt, how to validate results, so you can avoid that hallucination trap that Ahmad talked about.
As you move up the curve and you get into having agents actually perform functions, you need policies and procedures that will put guardrails around that. You need capabilities, you need to stay on top of trends, you need teams that can focus on the data. And then as you move to the end state, AI is a team member. You need HR to think about how AI agents are part of your organization. So the training and the impact on people really is multifaceted and kind of depends where you're at in that journey.
Hamed Faghfoury:
Yeah, makes sense.
Ahmad Ovais:
If I just add one thing to that response, is, I think Jensen said it very well, "The best way to use AI is to use it aggressively." We'll have to keep trying different ways of working with it, and over time we'll all figure out what is the best way it works for us. And with the memory features, I think it starts to get to know us better and it will just become exponentially better over time.
Hamed Faghfoury:
Yeah. And learn from each other, right. That's a big takeaway too, is that we're going to learn, I think that's part of the talent, is that it's one of the few technologies where as people are using it daily, they'll also be able to bring that back in a positive way as well. I don't know if you-
Julie Bilodeau:
I got to cut you off Hamed, 'cause we're at time.
Hamed Faghfoury:
Oh, wow.
Julie Bilodeau:
I know.
Hamed Faghfoury:
We could be talking about this [inaudible 00:19:01].
Ahmad Ovais:
Oh yeah.
Hamed Faghfoury:
And Jenny didn't even get a chance to ask her question yet.
Julie Bilodeau:
I know. This will be like one of those Easter eggs for our audience, like which episode was it that we missed Jenny asking a question.
Hamed Faghfoury:
So if you don't know, Jenny is our co-host and they've told me in the past, don't anger the AI agents. And I don't know, maybe I've watched too many sci-fi movies, but this is one of those cases where hopefully she won't-
Matt Glenen:
So, on the AI podcast, we did not allow AI to ask a question on the AI podcast.
Julie Bilodeau:
Yeah, this will be the one.
Hamed Faghfoury:
This is-
Julie Bilodeau:
This will be the one. So-
Hamed Faghfoury:
We'll see if we anger the AI agent.
Julie Bilodeau:
Yeah.
Hamed Faghfoury:
But we'll let Jenny ask two on the next episode.
Julie Bilodeau:
There you go. We'll make it up to, we'll make it up. I think this is a great place to end, though. I mean, talking about people and how this is impacting the way we work, right. This is all about what this podcast is about. So thank you all so much for being here. Thank you for those listening. I thank you for tuning into us again on BDO Digital Digest. The next time, we'll have Jenny ask a couple more questions. And in the meantime, for those of you listening, please continue to explore the future of technology in your world and stay innovative and bend the arc of possibility in your world. So thank you so much everyone for being here.
Ahmad Ovais:
Thank you for having us.