Where AI Stands in the Middle Market: Insights from Five Private Market Leaders on Nasdaq TradeTalks

Two years ago, private equity and investment banking mostly viewed AI as a productivity tool — a way to summarize CIMs faster, automate notes, or accelerate diligence workflows.  

Today, that mindset is shifting.  

Nasdaq's recent TradeTalks panel, Integrating AI Into Private Equity & Investment Banking Operations, brought together private market leaders, including Justin Guthrie, COO of Blueflame AI, to discuss what AI adoption in the middle market looks like beyond improving operational efficiency.  

Five takeaways on where AI stands in the middle market

1. Human judgment remains the ultimate differentiator

"Alpha doesn't come from using the same tools as everyone else," said Elliot Parker, Founder and CEO of Alloy Partners. "In private markets, alpha comes from relationships, reputation, and having the willingness to take contrarian views that turn out to be right."

The panel agreed that financial institutions expecting general-purpose AI tools to serve as a "magic returns machine" will be disappointed.

Success will come to firms that treat AI as an infrastructure layer — a system that frees up time and surfaces better information for humans to make more impactful decisions. Today, AI amplifies judgment but still doesn't replace it.

2. Purpose-built AI moves AI from experimentation to execution

The investment firms seeing direct impact through AI adoption — both in attracting top talent and generating real returns — are using purpose-built AI designed for their financial workflows.

Instead of adding another point solution to an already fragmented tech stack, this approach creates an AI orchestration layer that absorbs ongoing model changes — turning the “leapfrog dynamic” of constantly re-integrating capabilities into existing workflows into a structural advantage.

Elliot Parker, Founder and CEO of Alloy Partners, expanded this idea by sharing, "Early filmmakers couldn't imagine what cinema would eventually become, so they applied the new technology to the thing they already knew.

That's exactly what PE firms are doing today — applying AI to existing workflows and calling it a strategy. The firms that will win are the ones starting to imagine entirely new business models, not just faster versions of the old ones.”

3. Data is the foundation of sustainable competitive advantage

While AI models are increasingly commoditized, a firm's accumulated knowledge is not — its deal history, portfolio performance, pattern recognition, and even its past mistakes create a valuable layer of institutional data.

Investment firms that structure and query that data effectively are building something far more durable than any single tool. This extends into the middle market, where many portfolio companies sit on largely untapped data assets.

Investors who can help surface and operationalize that data aren't just improving company performance — they're feeding a broader learning loop across the firm.

The result is a compounding flywheel: better data leads to better AI outputs, which leads to better decisions, eventually generating even more valuable proprietary data.

4. AI enablement remains a major hurdle

One of the most grounded observations from the roundtable: roughly half of private equity firms and their portfolio companies now have at least one full-time role dedicated to AI enablement or adoption.

"We see that about 50% of our customers now have at least one full-time role associated with AI enablement or implementation," shared Justin Guthrie. "That's up from 2% just 18 months ago."

These roles typically sit at the intersection of the investment firm and portfolio company operations, identifying which workflows are genuinely ready for AI augmentation and leading the change management required to implement it effectively.

Without dedicated ownership, it’s unrealistic to expect employees who are already busy, skeptical, or unfamiliar with the tools to fundamentally change how they work.  

That’s where most AI initiatives quietly stall.

Financial institutions that don’t address the enablement gap often see AI usage plateau at surface-level tasks, while the underlying workflows — where the real value sits — remain unchanged.

5. We’re only scratching the surface, with transformative potential ahead

A recent Anthropic report cited during the discussion found that even in the most AI-penetrated business functions, saturation has barely reached a fraction of what is truly possible.  

That’s why the decisions investment firms make now about data infrastructure, culture, and purpose-built AI solutions will compound for years.

"General partners that take on a culture of being AI-forward are naturally going to influence that culture down to their portfolio companies," said Justin Guthrie. "That's where the real value is going to be made — not by using AI to avoid hiring a new associate, but by driving serious value creation at the portfolio company level."

What it takes to win with AI  

So, what separates the firms that will look back on this moment as a turning point from those that will be left wondering what happened?

Several key factors emerged from the conversation:

• A culture of AI adoption comes from the top: Leaders who actively use AI tools, discuss them openly, and set expectations for real fluency, not just awareness, create conditions for meaningful adoption across the institution.  

• Purpose-built AI solutions lead the way: General-purpose AI applied broadly across workflows will produce generic outcomes. Firms adopting purpose-built AI systems trained on proprietary data and designed around specific workflows will have a real competitive edge.

• Long-term thinking is key: The talent war for people who can build sophisticated AI systems is already underway. Early investments in the right capabilities and partnerships will pay dividends as technology matures.

• Willingness to reimagine business models paves the way: The most exciting AI applications in private equity and investment banking aren't the efficiency gains; they're the entirely new ways of creating and delivering value that established players, locked into legacy processes, can't easily replicate.

Real returns for the middle market are powered by purpose-built AI

The TradeTalks conversation revealed a clear inflection point: investment firms can no longer afford to treat AI as experimental. The question isn't whether to adopt AI-powered workflows, but how quickly and strategically they can implement purpose-built AI solutions that create a real competitive advantage.

The winners will be institutions that prioritize their most critical workflows across the deal lifecycle and implement an AI system designed specifically for investment firms and dealmakers, not generic business users.

Ready to move beyond general-purpose AI tools? Request a demo to see how Blueflame AI transforms the entire deal lifecycle.