SuperReturn 2026: 70% of AI value creation has nothing to do with technology

Across the halls of the SuperReturn International in Berlin, where more than 6,000 attendees representing over $50 trillion in assets under management gathered, a clear pattern emerged. The conversation has shifted from experimentation to implementation, from pilots to performance, and from AI tools to organizational capability. The challenge is no longer getting AI to work. The challenge is getting it to matter. 

The numbers tell the story. According to OMMAX's AI Trend Survey 2026/27, 74% of organizations are already deploying agentic AI in production, while 77% consider it a strategic priority. AI adoption is no longer a differentiator. Value creation is. 

Yet this is precisely where many organizations struggle. The graveyard of AI pilots continues to grow, filled with promising proofs of concept that never translated into measurable EBITDA impact. For private equity investors under pressure to accelerate growth and expand margins, the question is becoming increasingly urgent: what actually separates AI leaders from everyone else? 

At the AI/Tech Summit, that discussion crystallized around one of the most consequential talent decisions facing PE-backed companies today: should they hire new AI specialists or build AI capabilities within the teams they already have? 

Moderated by Christine Graeff, Global Chief Growth Officer at FGS Global, the panel brought together Anieke Lamers, Operating Partner at Borski Fund, Emily Cook, Founder and Performance Coach at FOUND, and Dr. Stefan Sambol, Founding Partner at OMMAX. Their discussion revealed five lessons every PE fund and portfolio company should be paying attention to right now. 

1. The technology is not the problem, and it never was 

When digital transformation fails in a portfolio company, the most common explanation points to tooling, infrastructure, or the wrong platform choice. In practice, the evidence points elsewhere. Drawing on close to 4,000 projects over the past decade, OMMAX’s consistent finding is that technology accounts for roughly 30% of what determines success. The remaining 70% is change management and ownership, whether the right people, at the right levels of the organization, are actively driving adoption and connecting initiatives to business outcomes. 

This view was echoed across the room. As performance coach Emily Cook put it, organizations consistently over-index on the technology and the data, and under-index on the people and the processes. The 70% of value that comes from workflows, upskilling, and culture is routinely left on the table while leadership debates which model or tool to use. 

OMMAX’s AI Trend Survey confirms this at scale. Initiatives most often fail not at ideation but during or after the pilot phase: 35% fail during the pilot and 44% fail after it. Scaling, not starting, is where organizations lose momentum. The leading failure reasons are data quality (28%) and integration complexity (27%), both organizational and architectural challenges, not technical ones. 

2. One-third of every organization is resistant to change, and you need to find them fast 

A pattern that comes up consistently across OMMAX’s transformation work is what we describe as the organizational third split. When entering an organization, the same distribution appears almost universally: one-third of people have been waiting for new tools and are enthusiastic from day one. Another third knows they need to learn but is willing. The final third remains highly resistant to change and can significantly slow progress if not addressed early. 

The critical point is timing. Identifying where that resistant third sits, whether at the executive level, second-level management, or deeper in the organization, needs to happen in the first months of an engagement, not at year three. In a typical PE holding period, leaving it that late means you are already running toward exit before the cultural foundation is in place.

On a current large-scale engagement for a food processing company in the Netherlands, OMMAX ran an eight-week diagnostic phase covering every department. The team identified over 250 use cases and mapped what we called AI enthusiasm across the organization, a diagnostic of where openness to change was highest. That map then guided where to create early pilots, turning initial skepticism into visible wins that shifted the broader culture. 

3. The hire versus upskill decision is more nuanced than most firms think 

When a portfolio company identifies a capability gap, the instinct is often to hire. It feels like progress. But the decision is more complex than it appears, and getting it wrong is expensive in both directions. 

The right framework considers three factors: scope, domain ownership, and compounding value. On scope, internal lower-stakes use cases where domain expertise already exists in-house, such as automating LP reporting or financial modelling, are well-suited to upskilling. Enterprise-wide deployments involving data privacy, security, and authorization complexity are a different matter. The demo solution may take hours to build, but the 80% of work that actually matters requires someone who has done it before. On compounding value, a team that develops genuine fluency gets better with each iteration and becomes more capable of spotting opportunities across the portfolio. That long-term return rarely gets factored into the calculation. 

There is also a timing dimension that PE firms consistently underestimate. The speed of change means that the role you thought you were hiring for at deal close may look meaningfully different within months. One operating partner at the conference described a portfolio company that abandoned plans to hire an AI engineer post-acquisition in favor of bringing in an external mentor to coach the existing CTO through rapid developments. A more agile response, and a cheaper one. 

4. This is a CEO topic, not an IT project 

According to OMMAX’s AI Trend Survey, 48% of organizations still place ownership in IT and engineering functions, while only 7% have placed it in business units. That structural imbalance explains a pattern that shows up repeatedly in diligence work: companies with technically functional capabilities that are strategically disconnected from where business decisions actually get made. 

The organizations making real progress have resolved this. The agenda sits at the top of CEO priorities, connected directly to the operating model, management incentives, and value creation targets. One practical intervention that consistently moves the needle is what OMMAX calls the AI inspiration session: a four-hour interactive workshop with the full executive team covering real use cases, hands-on experimentation with agent-building, and an honest conversation about what the technology can and cannot do. The effect is a shared baseline at the leadership level that is a prerequisite for any initiative to receive the budget and sponsorship it needs. 

Prioritization is the other half of the equation. One of our clients recently described his situation with a phrase that has stayed with us: “We have more pilots than the German Air Force.” That accumulation of uncoordinated initiatives, each with its own logic and timeline, is one of the most common reasons transformation stalls. The answer is to focus on the top two or three use cases that demonstrably touch profitability and govern them on a biweekly or monthly basis, not quarterly. 

5. Readiness is becoming a diligence and exit variable 

Increasingly visible in OMMAX’s buy-side diligence work is a pattern that the broader market is only beginning to price in: capability is becoming a meaningful variable in transaction valuations. Portfolio companies preparing for exit routinely include ambitious narratives in their materials. When a buy-side team looks closely, the gap between the story and the reality is frequently significant. Initiatives described as value creation turn out to be pilot-stage experiments without KPI evidence and without the internal talent to sustain them. 

That gap creates a yellow or red flag for sophisticated buyers. The funds building readiness into value creation plans from day one, assessing operating model, data infrastructure, leadership capability, and governance from close, are the ones whose portfolio companies will command the strongest exit multiples. 

What this means for your business 

The winners of the next phase will not be those running the most pilots. They will be those who build the leadership alignment, operating model, talent capabilities, and governance required to turn AI into measurable business performance. 

For PE funds and their portfolio companies, four things determine whether that happens:

1. Make it a CEO priority with a structured roadmap, clear use case prioritization, and value creation targets connected to it. Without executive ownership, initiatives stall at middle management. 
2. Address the resistance problem early. Mapping enthusiasm and resistance in the first 100 days shapes where early pilots should land and determines whether momentum builds or dissipates.
3. Design the hire versus upskill decision around scope, domain ownership, and compounding value, not convenience. Hiring is the quick fix that feels like progress. Upskilling compounds. The right answer is usually a hybrid model running both in parallel.
4. Build readiness into the investment thesis and value creation plan from day one. By the time a company is 18 months from exit, it is too late to build credibility from scratch.

For investors, the challenge is no longer identifying AI opportunities. It is building the capabilities, governance, and leadership commitment required to turn those opportunities into measurable value creation.

From AI strategy to value creation

OMMAX is a leading AI-first management consultancy supporting investors and portfolio companies across AI strategy, business transformation, transaction advisory, and value creation. Our work spans the full transformation journey: identifying value creation opportunities, assessing AI readiness during due diligence, designing operating models and governance structures, building organizational capabilities, and scaling AI initiatives into measurable growth, profitability, and exit outcomes.

If you would like to discuss any of these themes or receive our checklist of success factors drawn from close to 4,000 projects, please reach out directly to our team.