- AI is shifting from a narrative lift to a diligence checkpoint
- Investor questions are becoming more precise and economically grounded
- The challenge is not adoption but integration that moves performance levers
AI is no longer a side conversation in dealmaking; it is a central topic across diligence, value creation, and exit planning. Yet despite the volume of discourse, few companies are translating AI ambition into operational returns.
At Liberty, we do not claim to know precisely where AI is headed. No one does. But we do believe that value accrues to those who apply the same fundamentals that have underpinned every major technology shift: define the business need, quantify the outcome, and build when necessary.
Bill Valasek has led IT diligence and enterprise architecture efforts across a vast range of transactions and operating environments. His experience offers a grounded view into how AI is influencing buyer expectations, internal priorities, and portfolio planning. Below, he shares a practical approach to navigating AI and maximizing value creation.
What should companies think about AI when preparing to go to market? Is it a value driver, a risk factor, or both?
It is both. Buyers increasingly see AI as a potential driver of operating leverage, but only when tied to measurable economic outcomes. At the same time, they assess risk exposure; that includes technical debt, fragile integrations, and unclear IP ownership. The narrative must find balance; AI should be framed as a contributor to performance, not a surface-level enhancement.
Liberty advises teams to avoid broad claims and instead highlight where AI is reducing unit costs, compressing cycle times, or enabling commercial flexibility. Demonstrating impact in areas like onboarding, fulfillment, or scaled service delivery provides stronger validation than simply stating that AI is in use.
From an operating partner’s perspective, what AI-related questions are investors asking – and where are you seeing the greatest risk?
Investors are no longer asking whether AI is in use; they’re asking how, where, and to what effect. They want to understand whether automation is lowering transaction cost, whether insights are being generated fast enough to influence decisions, and whether the talent infrastructure exists to scale what’s working.
What concerns me most is when the pace of AI adoption exceeds an organization’s ability to absorb it. When deployment outpaces change management or model outputs fail to integrate with frontline workflows, friction emerges. In those cases, AI adds complexity instead of creating leverage. The real risk lies less in the models themselves and more in the disconnect between what is built and how the business functions day to day.
Where are you seeing real operational value being created through AI in portfolio companies?
The clearest near-term impact is in task automation and internal enablement. Generative models are reducing the time spent on content generation, summarization, intake classification, and low-level customer service. These are high-frequency tasks where the marginal cost of output has decreased exponentially.
The difference-maker is integration. AI only drives performance when its outputs are embedded directly into existing tools and decision cycles. It’s not enough to generate insight; that insight needs to flow into CRM fields, support queues, or planning dashboards. The companies realizing value are those that treat AI not as a new function, but as infrastructure augmentation.
Where do you anticipate the next wave of AI value creation?
Forward-facing functions such as demand forecasting, dynamic pricing, and strategic resource planning are likely next. These are areas where complexity overwhelms deterministic tools, and where probabilistic modeling can create more elastic responses to market signals.
But again, it is not about scale of deployment; it’s about economic materiality. We look for use cases that change the slope of the cost curve or accelerate revenue recognition. That may mean fewer total deployments, but deeper integration into processes that move financial outcomes. AI’s role isn’t to replace judgment; it’s to make the system more responsive where variability matters most.
