Looking beyond the low hanging fruit
Charles Towers-Clark
Artificial intelligence is forcing a fundamental rethink of how Private Equity and Venture Capital firms evaluate and create value in their portfolio companies. Traditional investment metrics need updating as AI transforms industries.
In interviews with Caroline Ohlsson, who leads AI initiatives at European private equity firm Verdane, and Sanjot Malhi, a partner at venture capital firm Northzone, both offer contrasting but complementary perspectives on how investors are adapting to create value in the AI era.
AI Readiness Becomes A Due Diligence Priority
Verdane has made AI assessment a key component within its due diligence process. The firm’s operational excellence team evaluates data maturity and AI readiness across three areas: technical capabilities, business processes, and talent.
The evaluation goes beyond current AI usage. Ohlsson explained that the firm conducts AI impact assessments to understand how AI affects each company’s business model, competitive landscape, and strategic opportunities. The critical question for portfolio companies, she noted, is whether they are thinking about “how defendable their business model is in an AI-led future.”
What matters most is not current AI sophistication but leadership mindset. Companies where leaders show excessive hesitation or risk aversion face obstacles executing AI initiatives. For private equity firms with substantial ownership stakes, AI represents a clear value creation opportunity.
Traditional Metrics are Losing Relevance In AI-Era Deals
Venture capital firms are discovering that traditional evaluation frameworks require recalibration. Malhi observed that “with AI everything is on steroids. Metrics that previously signaled product-market fit, such as reaching $1 million in annual recurring revenue, have become less reliable. With every company experimenting with AI products, strong initial retention numbers may not predict long-term success.”
This has led to greater focus by Malhi on qualitative assessment. “Is the product critical for customers? Would customers’ lives fundamentally change if it disappeared? Can the company transform from product to platform?” In Malhi’s opinion these questions differentiate genuine value creation from temporary enthusiasm.
For AI data companies, Malhi opinions that value will arise from distribution channels that are proprietary and locked in, or that possess proprietary datasets that large language models cannot access.
The Importance Of Human Leadership In An AI World
Both Ohlsson and Malhi emphasized that leadership mindset is the single most important factor determining whether companies will successfully navigate AI transformation. The problem is not a lack of opportunities but a failure of vision. Many companies focus only on low-hanging fruit such as using AI for customer support or content creation. These applications deliver incremental improvements in the short term but avoid the fundamental strategic changes that should be addressed as a result of AI.
Ohlsson has incorporated moonshot thinking into Verdane’s sessions with portfolio companies, pushing leaders to consider scenarios years into the future. “What is your view and your vision of how you will fit into a future 10 years from now?” she asks. “If today your customers are human but in the future agents are running the show, what does that mean in terms of how you price your product, how do you connect to those agents? How does your value, as a solution, belong to that future?”
Few portfolio company leaders are engaging with these questions. The tendency is to delegate AI discussions to chief technology officers, treating it as a technical rather than strategic issue. This misses AI’s potential impact.
Boards bear particular responsibility for this gap. Ohlsson argued that boards should commission AI impact assessments to understand various scenarios, opportunities, and risks. “The responsibility needs to start with the leaders, including the board.” Without this understanding, boards cannot effectively guide resource allocation or strategic planning in an AI-transformed landscape. To help with this, Ohlsson has created an AI scorecard to support board-level discussions on AI adoption at portfolio companies.
There is also a divide in how companies approach AI value creation. Cost savings initiatives, while valuable, are generic and easier to implement. Revenue growth opportunities related to AI tend to be more closely connected to product development, innovation and customer value propositions, requiring deeper strategic thinking and often longer time horizons.
The challenge for investors is identifying leaders who demonstrate the courage to make bold decisions without certainty of outcomes. As Ohlsson has found, community sessions where portfolio company leaders share their AI implementation experiences can help shift mindsets by demonstrating that AI-first approaches are achievable.
Rethinking Process, Not Just Technology
AI cannot simply be layered onto existing processes. Short-term gains from such approaches are possible, but transformational value requires reimagining how humans and AI collaborate within entirely redesigned processes.
Malhi expressed excitement about AI’s potential to disrupt established but inefficient sectors, from democratizing banking to accelerating drug discovery. His observation that “every company will be an AI business to survive” whilst debatable, highlights the ubiquitous nature of AI in the future.
For investors, value creation strategies must go beyond operational improvements to address strategic positioning in an AI-native future. As AI capabilities continue maturing, the companies and investors that succeed will be those willing to engage in the philosophical and strategic discussions that many are still avoiding.
The question is no longer whether AI will transform industries, but which business models will remain defensible when that transformation is complete.
