By Will Bellinger, Principal Consultant, Private Equity Transformation, Stanton House

The pressure to have an AI strategy has arrived long before many organisations have established the foundations needed to make that strategy successful.

In my conversations with private equity investors, transformation leaders and portfolio company executives, AI is now firmly on the board agenda. Yet while the technology continues to evolve at remarkable speed, the challenge facing PE-backed businesses is not a lack of AI tools. It is a lack of readiness to turn those tools into measurable value.

For the first episode of Portfolio Perspectives, my new interview series exploring transformation within private equity-backed businesses, I sat down with Amira Modi. Amira is an experienced transformation and operational leader whose career spans FTSE 100, Fortune 500 and private equity-backed organisations.

One message came through consistently in our conversation: AI is an enabler of value creation, not a substitute for it.

For private equity leaders operating in an environment defined by longer hold periods, greater scrutiny and increased pressure on EBITDA growth, that distinction matters more than ever.

The AI conversation is happening in the wrong order

Many businesses are approaching AI with a sense of urgency. That is understandable.

Boards are asking questions. Investors are asking questions. Competitors are announcing AI initiatives. Leadership teams understandably feel pressure to demonstrate progress.

The problem is that many organisations are starting with AI and then working backwards.

As Amira explained, creating an AI strategy that sits separately from the business strategy is likely to fail. AI should not determine where the business is going. Instead, leaders should first be clear on their commercial objectives, competitive position and value creation priorities before assessing where AI can accelerate outcomes.

Recent research suggests this challenge is widespread. McKinsey's latest global AI survey found that while 88% of organisations now use AI in at least one business function, nearly two-thirds have still not begun scaling AI across the enterprise, and only 39% report any EBIT impact from AI at organisational level. The gap between adoption and measurable value remains significant.

For PE-backed businesses, this creates a significant risk. When AI becomes a solution searching for a problem, investment can quickly become fragmented, disconnected from value creation plans and difficult to measure.

The starting point should be simple:

• What are we trying to achieve?
• Where is value currently being lost?
• Which operational constraints are limiting growth?
• What capabilities will accelerate execution?

Only then should leaders ask whether AI is the right tool to solve those problems.

As Amira put it, AI is a means to an end, not the answer in itself. Sometimes it will provide a competitive advantage. Sometimes it will not.

Knowing the difference is becoming a critical leadership capability.

Private equity has entered an execution era

The context for AI adoption matters.

Across the PE landscape, longer hold periods and increased scrutiny on profitability have pushed operational execution to the centre of value creation. Transformation leaders are increasingly being assessed on their ability to translate investment theses into measurable outcomes.

Against this backdrop, AI should be viewed through the lens of execution.

The most successful portfolio businesses are not necessarily those running the largest AI programmes. They are the organisations focused on strengthening operating models, improving processes, integrating systems and building scalable foundations.

Amira highlighted a challenge she regularly encounters within PE-backed businesses: organisations of significant scale still carrying unresolved operational issues that should arguably have been addressed years earlier. These can include:

• Fragmented processes
• Inconsistent operating models
• Manual workflows
• Siloed systems
• Poor data quality
• Unclear accountability

Introducing AI on top of those weaknesses rarely fixes them. In practice, it often amplifies them.

McKinsey's research reinforces this point. Organisations generating the strongest returns from AI are far more likely to redesign workflows and operating processes rather than simply deploy technology on top of existing ways of working.

For private equity leaders, the question is not whether AI can create value. It is whether the organisation is operationally mature enough to unlock it.

Data remains the foundation most businesses underestimate

Every AI conversation eventually arrives at data. The challenge is that data issues are rarely new.

Whether in global corporates, scale-ups or PE-backed businesses, data quality, governance and accessibility have been persistent leadership challenges for decades. AI has not changed that reality. It has simply increased the consequences of getting it wrong.

As Amira explained, organisations must understand the quality of their data before introducing AI into the equation. Leaders need confidence in what data exists, where it sits, how reliable it is and who owns it. This aligns closely with what we see across transformation programmes.

The businesses making the greatest progress are typically focused on:

• Creating consistent data environments
• Integrating fragmented systems
• Improving performance reporting
• Establishing governance and accountability
• Building confidence in decision-making data

Without these foundations, AI outputs become difficult to trust. This is why many successful transformation leaders continue to focus on fundamentals first.

Leadership, not technology, determines adoption

One of the most valuable insights from my discussion with Amira was that the fundamentals of change management remain largely unchanged. Despite the focus on emerging technologies, transformation ultimately remains a people challenge.

Many employees are asking what AI means for their role, their future responsibilities and how they should use the technology.

When leadership teams fail to answer those questions, employees will create their own answers, that uncertainty can quickly become resistance.

Amira described the importance of open communication and listening sessions throughout periods of transformation. Rather than relying solely on top-down communication, leaders should create opportunities for genuine dialogue and feedback.

People rarely resist technology itself, instead they resist uncertainty.

For PE-backed businesses operating under compressed timelines, maintaining engagement throughout change programmes can have a direct impact on delivery outcomes.

That requires leaders to move beyond announcements and create meaningful opportunities for employees to contribute to solutions.

As Amira noted, AI adoption should not be entirely top-down. The most effective organisations combine strategic direction from senior leadership with practical insight from teams closer to day-to-day operations.

Building transformation capability

One of the most important concepts Amira discussed was the idea of building “transformation muscle”.

Private equity businesses can no longer view transformation as a one-off programme. Change is becoming a permanent operating reality, driven not only by AI, but by shifting markets, evolving operating models and increasing investor expectations.

For leaders, this means developing organisations that can adapt continuously. The most effective transformation leaders combine strategic thinking with hands-on execution, remaining close to delivery while helping teams navigate change.

As AI evolves, the organisations that create the most value will be those that strengthen their ability to execute, learn and adapt repeatedly.  

Practical takeaways for PE leaders

If you're assessing AI opportunities within a portfolio business, focus on these priorities first:

1. Start with the value creation plan
   Define business objectives before defining AI initiatives.
2. Assess operating model maturity
   Identify process, system and organisational weaknesses that could limit AI adoption.
3. Audit your data foundations
   Understand data quality, ownership, governance and accessibility before introducing AI at scale.
4. Invest in leadership capability
   Build leaders who can manage continuous change, not just one-off transformation programmes.
5. Measure outcomes, not activity
   Focus on commercial impact, operational improvement and EBITDA contribution rather than the number of AI pilots launched.

The real opportunity

Private equity has always been about creating value.

AI undoubtedly offers significant opportunities to accelerate that value creation. However, as Amira observed, success still depends on the same foundations that have always differentiated high-performing businesses: clear strategy, strong leadership, effective operating models, quality data and the ability to execute consistently.

The organisations that generate the greatest return from AI will not necessarily be those that adopt it first.

They will be the ones that prepare their businesses properly before they do.

Watch the full conversation

This article draws on my conversation with Amira Modi for Portfolio Perspectives, a Stanton House series where I speak with transformation and operational leaders about the challenges, opportunities and realities of creating value within private equity-backed businesses.

In our full conversation, Amira and I explore AI readiness in greater depth, including the importance of data and operating model maturity, leadership through change, and why successful transformation ultimately comes down to execution.

Watch the full interview with Amira: AI Readiness, Leadership and Value Creation.

If you're a transformation or operational leader with a perspective to share on value creation in private equity and would like to join me for a future episode of Portfolio Perspectives, please get in touch.

Sources
McKinsey, The State of AI 2025