While the question of whether to adopt AI has largely been settled, many organizations continue to wrestle with the far more difficult challenge: how to embed AI into the fabric of their value creation engines in a scalable, repeatable, and transformational way. The difference between companies that unlock game-changing outcomes and those that see only incremental gains often comes down to how they approach organizational transformation.
In a recent conversation with Dr. Ben Herndon, Chief Strategy Officer at Kungfu.AI, former Chief Analytics Officer at the IRS, and ex-Head of AI Strategy at Vista Equity Partners, we unpacked what separates high-impact AI programs from costly, one-off experiments. Drawing on his deep experience across the public sector, private equity, and enterprise AI, Dr. Herndon shares a pragmatic and strategic perspective on how to operationalize AI as a sustained source of competitive advantage. This discussion also serves as a precursor to Dr. Herndon’s upcoming session at PCG’s AI Value Creation Summit in Austin on September 18th, where he’ll dive even deeper into these ideas.
One of the most pervasive, and costly, missteps in enterprise AI adoption is the tendency to view artificial intelligence as a standalone technology deployment, rather than a transformative shift in organizational capability. When AI is approached like a conventional IT initiative, it often results in isolated tools with limited impact, rather than a systemic uplift in performance.
In contrast, companies that see sustained value from AI begin by rethinking the foundation of how their organizations operate. They recognize that AI success depends not just on data and models, but on early and deliberate investments in cross-functional collaboration, leadership alignment, and institutional change. This includes designing the right org structures, building AI literacy across departments, and ensuring that incentives and talent strategies reinforce adoption.
"One of the most common pitfalls is thinking of AI as just a point solution," Dr. Herndon explained. "That might work in small settings, but for large organizations, like the IRS or Fortune 200 companies, you have to organize for AI. That includes leadership, org structure, hiring, compensation, training, and retention."
This reframing of AI demands a shift in how organizations plan, resource, and manage transformation. It moves AI from the IT team's domain to a boardroom priority, requiring buy-in from every level of leadership.
Technical execution is rarely the sole determinant of AI project success. Often, the biggest breakdowns occur in the overlooked human and operational layers. A high-performing model can easily become a failure if the broader organization isn't equipped to absorb, support, or understand the new system.
Dr. Herndon illustrated this point with a striking case: "One company embedded AI into their software without changing anything about customer support or training. Once deployed, they were flooded with support tickets. Users thought the software was broken."
This example underscores a broader truth: AI deployment cannot succeed without an integrated change management plan. Preparing internal teams for new workflows, updating customer-facing training, and redesigning support structures are not optional. When operational alignment is an afterthought, AI systems can generate more confusion than value.
For operating partners seeking to drive top-quartile returns, AI offers a lever to fundamentally reshape portfolio company operating models. During his tenure at Vista Equity Partners, Dr. Ben Herndon observed a clear distinction: the companies that realized outsized value from AI weren't merely layering in automation; they were re-architecting their businesses for scalable intelligence.
"At Vista, companies that succeeded with AI weren't just experimenting, they were transforming. They rethought their business models and processes. They built engines to embed AI across products, operations, and finance," Herndon noted.
Embedding AI as a capability rather than a tool unlocks compounding value across functions. It enables enhancements in product personalization, predictive service capabilities, and proactive financial optimization, all of which become mutually reinforcing. The firms that lead in AI maturity build operating models where AI is central to the enterprise value engine, enabling accelerated growth without linear cost scaling.
Human capital is often the most underutilized lever in AI strategies. Strategically deployed AI allows organizations to automate high-volume, rules-based decisions, redirecting talent toward judgment-intensive, value-generating work that drives differentiation.
"We had a client in the factoring industry. They wanted to use AI to help people make better decisions. We helped them see that nearly 70% of those decisions could be automated. That didn't mean layoffs; it freed their people to focus on more strategic and complex decisions," Dr. Herndon shared.
This reallocation increases enterprise agility. When knowledge workers are no longer bottlenecked by repetitive workflows, companies unlock latent strategic capacity without expanding SG&A. For operating partners focused on lean scale-up and platform efficiency, this model offers a compelling route to drive growth while protecting human capital, particularly in high-churn or skill-constrained sectors.
In industries focused on software and technology, the engineering teams, such as software developers, DevOps, and QA, are usually the first to see the biggest benefits from AI. This is because AI can speed up product release schedules, streamline quality assurance, and make it possible to build advanced new features using machine learning. That’s why engineering is typically the leading area for realizing value from AI.
"Engineering, for sure—especially in enterprise software. Whether it's improving personal productivity, code assistance, or leveraging proprietary data, these are the areas where we see massive ROI and industry disruption," he explained.
However, the advantages of AI are not limited to engineering alone. As companies become more experienced with AI, other departments like finance and customer success also start to gain substantial value. For example, AI can improve financial forecasting, help manage working capital, and enable proactive customer retention through advanced analytics.
Engineering is usually the first department to benefit the most from AI, especially in tech-driven companies. As AI adoption grows, other areas such as finance and customer support also begin to see significant returns.
Scaling a business from $50M to $200M in revenue requires an operating platform that supports intelligent decision-making at scale. AI accelerates this trajectory, but only when underpinned by robust infrastructure.
"You need to maintain data quality as you grow. That means investing in data engineering and infrastructure early. You also need a forward-looking data strategy, not just managing what you have now, but preparing for what you'll need in the future," Dr. Herndon emphasized.
Infrastructure readiness includes not only technical capabilities but also strategic data acquisition. Leading firms are now incorporating data-rich asset acquisitions, third-party partnerships, and proprietary signal development into their buy-and-build playbooks. This unlocks real-time visibility, predictive decisioning, and continuous learning loops.
Private Equity Reality Check: Bridging the AI Readiness Gap
AI adoption timelines often get derailed by a critical misalignment between deal thesis assumptions and portfolio company realities. Herndon calls out a pattern that many operating partners will recognize:
"They often overestimate the readiness of portfolio companies' data maturity, team capabilities, and culture. Once the work begins, it becomes clear that reality doesn't match expectations. That adds cost, extends timelines, and challenges the original ROI case."
For firms operating on compressed hold periods, this execution gap poses serious risk. Effective pre-close diligence must go beyond tech stack audits. It requires a cultural and operational assessment: Is the leadership aligned? Does the company have the change management muscle? Can existing teams evolve in parallel with technical implementation?
These questions increasingly dictate whether AI becomes a drag on returns or a force multiplier within the first 12–18 months post-close.
AI is a fundamental enabler of scalable value creation and competitive advantage. Portfolio companies that treat AI as a peripheral initiative will be rapidly outpaced by those that embed it into their operating models, workforce strategies, and data infrastructure.
As Dr. Ben Herndon emphasized, successful AI adoption requires rethinking business models, building cross-functional capabilities, and investing early in the infrastructure that allows AI to scale. Strategic automation can elevate human capital, while a dynamic approach to AI strategy ensures adaptability as markets evolve faster than traditional hold periods allow.
For operating partners, the call to action is clear: lead the transformation. Evaluate AI-readiness in diligence, integrate AI into your value creation playbooks, and champion infrastructure and talent strategies that support long-term AI integration. The window for slow adoption is closing.
Want to learn more? Join Private Capital Global in Austin this September 18th as we dig deeper into this conversation with Dr. Herndon. Check out the event and register at: Harnessing AI for Scalable Value - Home