AI in the Middle Market: Practical Lessons from ACG’s Value Creation Forum 

At the Value Creation Forum, part of ACG’s Middle Market Week - Deal Day in NYC, held at the Metropolitan Club of New York, Datasite’s Senior Vice President of Sales, James Viglione, moderated a dynamic panel discussion exploring how private equity firms and their portfolio companies are navigating AI adoption in real time. The panel featured insights from John Bova of Operating Partner Resources Group, Benjamin Humphreys of Monomoy Capital and Brandon Nott of IntelePeer. 

The conversation surfaced a clear message: the biggest wins in AI come from practical, accessible technologies, paired with strong data discipline and intentional governance. 

The rise of low/no-code AI tools 

Panelists agreed that the most immediate value comes from low or no-code AI tools that integrate easily into existing workflows. Rather than attempting to build custom models, portfolio companies are turning to third-party solutions that help automate routine tasks, improve internal productivity, and enhance visibility across functions. These tools are inexpensive, quick to deploy, and require little technical expertise, making them an ideal starting point for operators who want clear ROI without major investment. 

Why high-cost AI development doesn’t make sense, yet. 

When the conversation shifted to more advanced development, such as building proprietary models or deep research, the panelists were aligned: most middle-market companies simply don’t need to take on that level of complexity. The talent, infrastructure, and ongoing training required for custom AI is extraordinarily expensive, and the competitive advantage often fails to justify the cost. 

As one speaker put it, “Data is like oil, valuable only after it has been refined,” reinforcing that most organizations must prioritize strong data foundations before pursuing advanced AI initiatives. 

Instead, the prevailing approach is to become a “fast follower” rather than an AI pioneer, adopting proven tools once outcomes are clear, rather than funding innovation from scratch. 

From experimentation to outcome-driven adoption 

A recurring theme throughout the discussion was the shift from experimentation to measurable value. Many companies are exploring AI, but fewer have tied those experiments to specific business problems. The panel encouraged leaders to start with clear objectives, define what success looks like, and pilot small before scaling. AI that saves a few minutes is helpful, but AI that meaningfully increases revenue, speeds decision-making, or strengthens core processes is where true impact happens. 

Companies should continuously validate which tools matter, and which are simply noise; an essential discipline as the market becomes increasingly saturated with “AI-powered” offerings. 

Why the foundation still matters 

Before adopting any new technology, middle-market companies need strong operational foundations. The panel emphasized that systems like ERPs and CRMs can only succeed when companies first invest in mapping their business processes. Without consistent workflows, standardized data, and clear ownership structures, even the most advanced tools will fall short. 

This foundational work may not feel cutting-edge, but it is a critical prerequisite to any effective AI strategy. When companies prioritize data hygiene and alignment first, AI adoption becomes significantly more efficient and scalable. 

The growing risk of “work slop” and data leakage 

One of the most discussed challenges was the spread of uncontrolled AI usage across teams. Employees often adopt consumer-grade or unapproved AI tools on their own, which can lead to inconsistent outputs, version control issues, and, most concerning, exposure of sensitive information outside the organization’s secure environment. 

To combat this “work slop,” the panelists strongly recommended establishing clear AI usage guidelines, providing approved tools for employees, and educating teams on what should never be uploaded into AI applications. Governance is quickly becoming as important as the tools themselves. 

Where do we go from here? 

One initiative every middle-market company should prioritize in the next 90 days is getting their data house in order. That means taking stock of where data lives, cleaning and structuring datasets, updating permissions and security, and establishing a reliable single source of truth. Clean, well-governed data not only improves operational clarity, it also makes every future AI initiative faster, safer, and far more effective. 

AI continues to reshape the middle-market landscape, but the companies seeing real traction are the ones embracing simplicity: accessible tools, disciplined foundations, clear governance, and a focus on outcomes over novelty. With these principles in place, middle-market firms can accelerate value creation today while building a strong foundation for the more advanced AI capabilities of tomorrow.