From pilots to performance: Top 10 takeaways from SuperReturn International 2026

If there was one conclusion from SuperReturn International 2026, it was this: AI has moved from experimentation to economics.

Across more than 6,000 attendees representing over $50 trillion in assets under management, the conversation was no longer about whether AI works. It was about who is turning AI into measurable EBITDA, stronger valuations, and durable competitive advantage, and who is not.

OMMAX Founding Partners Dr. Stefan Sambol and Toni Stork spent the week speaking with investors, operating partners, founders, and portfolio company executives. Among the most valuable exchanges were conversations with the operating partner community, the people closest to portfolio companies and furthest from the hype, whose on-the-ground perspective cut through the noise in ways that boardroom discussions rarely do. What emerged was a picture of an industry moving into a new phase: from pilots to performance, from adoption to accountability, and increasingly from productivity to valuation.

Over the course of the week, three themes emerged repeatedly across panels, sessions, and side-room conversations: people, performance, and valuation. Conversations focused on the talent, leadership, and organizational readiness required for AI transformation; the challenge of scaling AI into measurable EBITDA impact; and the growing role AI plays in shaping business durability, growth, and valuation multiples. The eleven takeaways below sit at the intersection of all three.

1. The conversation has shifted from experimentation to accountability

A year ago, the dominant question at conferences like this was whether AI could deliver value in private equity portfolios. That question has been answered. The question dominating SuperReturn 2026 was sharper: why are the returns so unevenly distributed, and what are the funds genuinely winning actually doing differently?

OMMAX’s upcoming AI Trend Report 2026 captures the state of play precisely: 74% of organizations are already deploying agentic AI in production, and 77% consider it a strategic priority. Adoption is no longer a differentiator. Value creation is. The graveyard of AI pilots has never been fuller, and the industry knows it.

2. AI is now a valuation topic, not just a productivity topic

One of the most significant signals of this year was a market event that happened before the conference even started. In Q1 2026, approximately $300 billion of market value evaporated across SaaS, data, and software-heavy companies. This was not a general correction. It was a structural repricing: investors making a judgment about which businesses AI would strengthen and which ones it would commoditize.

The assets being rewarded are those where AI has moved the business toward controlling operational workflows, transactional rights, and the data generated by execution. The assets being discounted are those where AI has produced interface improvements and feature additions without changing what the business actually does or what it is worth.

The assets being rewarded are increasingly those moving toward what OMMAX describes as execution authority: controlling workflows, decisions, and operational outcomes rather than simply providing software access. The distinction is becoming increasingly important as software shifts from systems of record toward systems of action.

For private equity, this is not a software-sector observation. It is a portfolio-wide valuation signal. The firms creating the strongest returns are no longer necessarily those adopting AI fastest. They are the ones embedding AI deeply enough to change the economics, defensibility, and ultimately the valuation profile of the underlying business.

3. The 70/30 rule: technology is the smaller half of the problem

Drawing on close to 4,000 transformation projects, OMMAX’s consistent finding, echoed loudly across the SuperReturn AI/Tech Summit, is that technology accounts for roughly 30% of what determines whether an AI initiative succeeds. The remaining 70% is change management, workflow adjustments, and ownership.

As performance coach Emily Cook put it on our panel: organizations consistently over-index on the technology and the data, and under-index on the people and the processes. OMMAX’s AI Trend Survey confirms this at scale: 35% of initiatives fail during the pilot phase, and 44% fail after it. Scaling, not starting, is where organizations lose momentum.

4. Most firms are spending AI budget in the wrong places

The visible is back-office automation: HR chatbots, invoice processing, document handling. These are real use cases, but they are also largely solved by third-party vendors. Workday, SAP, and their peers have been embedding AI into administrative workflows for years. A portfolio company building its own proprietary tool to answer HR policy questions is not generating a competitive advantage. It is reinventing a commodity at significant cost.

The valuable lives in two places: the product or service delivered to customers, and the front- and middle-office operations that support client-facing teams. Riccardo Basile shared a picture of exactly where Permira’s portfolio sits today: 100% of companies are using machine learning in some form, 90% are working with large language models, and 25% have already moved agentic AI into live production environments. But crucially, 45% have AI embedded directly in their products, and 85% are deploying it in front and middle-office operations. The back office has been deliberately left to commodity vendors. That is not a gap in their AI strategy. It is the strategy.

5. Five metrics now separate AI leaders from AI tourists

A joint session between Permira and AWS, led by Riccardo Basile, Operating Partner and Head of Value Creation at Permira, produced the most precise answer this conference has generated on what structural AI value creation actually looks like in financial terms. Riccardo’s direct account of what Permira has built and measured across its portfolio gave the room something that whitepapers rarely do: real numbers, real commitment, and honest reflection on what it actually took. The OMMAX whitepaper on B2B software repricing identifies five metrics that now separate companies structurally capturing AI value from those merely adding features:

• NRR above 110-120%: the clearest signal of compounding value through AI-driven expansion 
• GRR above 95%: mission-critical embedding and high switching costs 
• >50% of new ARR from expansion: growth becoming self-reinforcing 
• EBITDA margins of 25-35%: scalable unit economics and productized AI 
• Organic growth above 15%: genuine product-market fit and pricing power 

Companies meeting these thresholds are capturing execution-layer value. Companies missing them are adding AI features. The distinction is not subtle. It shows up in multiples.

6. Two transformations that prove what conviction looks like

Octus, the global credit intelligence provider, invested approximately 12% of revenue into technology to rebuild its core product around AI. ARR per client tripled. Publishing time fell by 90%. Sustained growth above 30% followed. This was not AI added as a feature. It was a rebuilt core product that made the old experience incomparable to anything the market had seen before.

Acuity, a knowledge process outsourcing firm with 6,000 analysts, faced the obvious existential fear when Permira evaluated the investment in April 2023: would AI displace the business? Permira committed 20 million euros to build Agent Fleet, an AI platform designed to automate workflows that did not require human judgment. Today, 15% of company revenue is fully automated, 30% of workforce capacity runs through automation, and core efficiency has improved by around 20%. The fear of disruption became the primary mechanism of value creation.

What both cases share is not just sophistication or scale, but the willingness to make a real investment decision with real accountability behind it. As Riccardo put it, these were not proofs of concept. “They were proofs of conviction.”

7. The organizational third split and why you need to find it fast 

A pattern that comes up consistently across OMMAX’s transformation work is what we call the organizational third split. Almost universally, one-third of people are enthusiastic from day one. Another third knows they need to adapt and is willing. The final third is highly resistant and can significantly slow progress if not identified early.

In a typical PE holding period, leaving this diagnosis until year three means you are already running toward exit before the cultural foundation is in place. On a current engagement for a food processing company in the Netherlands, OMMAX ran an eight-week diagnostic covering every department, mapped over 250 use cases, and identified where AI enthusiasm was highest across the organization. That map guided early pilots, turning initial skepticism into visible wins that shifted the broader culture.

8. Token spend is the new cloud spend, and most firms are repeating the same mistake

A decade ago, PE firms watched cloud bills balloon without clear attribution to business outcomes. The same dynamic is now playing out with AI token spend. Costs are rising. EBITDA impact is lagging. The instinct has been to apply hard limits: rollbacks, caps, and leaderboards tracking individual consumption. That instinct is wrong.

Amazon itself rolled back an internal software engineering leaderboard tracking token consumption just two weeks before SuperReturn, a visible signal that blunt cost suppression is not the right mechanism. Riccardo Basile was direct about this: treat token spend the way mature organizations treated cloud spend, with FinOps discipline, visibility into what is being consumed and why, and intelligent routing of tasks to the most cost-appropriate model rather than defaulting to the most capable one for every step. In practice, this means smaller, task-specific models for routine steps, caching where repetition is predictable, and incentives that reward teams for spending efficiently rather than penalizing them for spending at all. The organizations that build this capability early compound the advantage. The ones that apply blunt suppression slow their own innovation and quietly widen the gap with more disciplined competitors.

9. AI readiness is becoming an entry and exit variable

Perhaps the most important structural shift visible at SuperReturn 2026 is the integration of AI risk directly into the investment committee process. The leading funds are no longer treating AI readiness as a post-acquisition priority. They are assessing it at entry, looking at automation depth, execution embedding, monetization resilience, and dependency on seat-based expansion, and tracking it through to exit.

Riccardo Basile was specific about how Permira applies this in practice. The firm actively avoids segments where AI enablement does not generate incremental growth: generic tax advice is a clear example, where AI reduces the cost of the service but does not drive additional demand. Certain media software categories face similar constraints. In these segments, AI compresses margins without creating growth, which means the investment case deteriorates even if the technology is adopted successfully. What Permira prioritizes instead are market leaders with strong commercial relationships, physical assets, regulatory moats, and management teams already investing ahead of the curve.

The gap between the AI narrative in a CIM and the verifiable financial reality is something sophisticated buyers will no longer overlook. The funds building readiness into their value creation plans from day one are the ones whose portfolio companies will command the strongest exit multiples. By the time a company is 18 months from exit, it is too late to build credibility from scratch.

10. AI is becoming a source of competitive divergence 

The most important realization from SuperReturn 2026 is not that AI creates value. That debate is over. It is that AI is increasingly creating separation.

Between companies that scale and those that stagnate. Between portfolios that compound and those that absorb cost. Between assets that command premium valuations and those that face repricing pressure.

The AI maturity gap is becoming a valuation gap. And at the pace the most advanced portfolios are moving, the gap is widening faster than most firms in the room are positioned to close.

The winners of the next cycle will not necessarily be the firms adopting AI fastest. They will be the firms translating AI into durable economics fastest. Those who have built the leadership alignment, operating model, talent capabilities, and governance required to turn AI into measurable business performance, and who can demonstrate that in verifiable financial terms when it matters most.

About OMMAX

OMMAX is a leading AI-first consultancy and AI-engineering platform specializing in AI strategy, business transformation, transaction advisory, and value creation in the age of AI. Founded in Munich in 2011, OMMAX serves large corporates, mid-sized companies, and private equity firms across Europe and the US, with more than 4,000 completed projects and a Net Promoter Score of 90.