Richard Waddell, Boyden’s Global Practice Co-Leader, Leadership Consulting, based in the United Kingdom, comments, “In private equity, the question is not just who looks right on paper, it is who can actually deliver. We bring together rigorous, evidence-based leadership assessment and deep market knowledge to help portco boards understand whether a leader can scale a business, drive transformation and create value”.
The need for transformational leadership is starkly visible in tech investments, where AI is redefining business models, leadership requirements, and the drivers of enterprise value.
Part III: When business models reset: AI creates ‘intelligence as a service’
SaaS businesses provide one of the clearest examples of how AI is resetting assumptions around growth, valuation, and value creation.
Heavy investment in previously sticky, high margin software companies has, in the era of AI, created a so-called ‘SaaS-pocalypse’ with a crash in the valuations of software companies in a new competitive environment. Some commentators, such as LPGP Connect, point to an ‘AI reckoning’.
According to CatalAIze, in Q1 2025, 58% of VC funding went to AI-native companies, with traditional SaaS companies facing ‘survival of the fittest’. Bain notes PE firms dividing portfolio companies into ‘AI-native’ (high potential) and AI-adapter (high risk) categories.
“Private equity and VC funds loaded up with SaaS are now struggling to meet exit valuation goals. Multiples have plunged and re-gearing these assets is a top priority. From a VC perspective, it has become extremely complex if not impossible to raise money for a SaaS play with no strong AI component,” explains John McCrea, Boyden’s Global Sector Co-Leader, AI, Cloud & Software, and Americas PE/VC Regional Practice Leader.
Software, semi-conductor and cybersecurity providers are striving to keep pace with AI developments, moving towards ‘intelligence as a service’. Transformative Head of AI roles are therefore synthesising strategic, commercial, technical, operational, human capital and collaborative capabilities. William J. Farrell, Boyden's APAC PE/VC Regional Practice Leader, explains, “These leaders are running teams comprising directors, managers, AI research scientists, AI engineers and business intelligence experts, to deliver for example, supply chain intelligence, HR management, competitor analysis and production planning. The strategic priority is to transform the enterprise through high-impact AI-enabled systems, while overseeing on-going, advanced AI research for future deployment”.
Forrester’s Kate Leggett asserts, ‘[So-called] “death of the core” and “death of SaaS” narratives are overstated. The brain of the enterprise remains, the central nervous system is evolving, and the centre of gravity is becoming more intelligent8’.
The advent of AI is the next reset in an on-going cycle: we have seen ‘on-premise’ move to SaaS; and SaaS move to AI, with AI redefining how decisions are made, not just how work happens. Software Equity Group (SEG) points to fast movers and integrators such as Microsoft, NVIDIA and Adobe, with laggards being feature-only SaaS vendors adding AI as window dressing.
Microsoft rapidly embedded AI across its productivity stack through Microsoft 365 co-pilot, shifting software from a tool you operate to an intelligent system that helps operate the work. The company is enabling ‘agent operated’ workflows that automate decisions and action steps across documents, meetings and daily tasks, extending far beyond mobile interfaces into a continuous intelligence layer9.
Adobe integrated AI into its software by giving humans a ‘creative superpower’ rather than replacing them, integrating powerful AI models into familiar legacy apps such as Photoshop and Acrobat. Another distinctive approach was to act as an aggregator, so users could access third party tools such as Google, OpenAI, or Black Forest Labs inside Photoshop. Other intelligence tools include document summaries, data manipulation and answers to questions within a document10.
For software businesses, the challenge is therefore not awareness, it is translating urgency into execution. These organisations need AI leaders with experience leading teams delivering advanced AI-enabled analytics and system design, responding to the needs of business stakeholders, and recruiting and developing AI research scientists and engineers for the next iteration of the cycle.
Despite high levels of uncertainty and complexity, the outlook is favourable, with optimism around the key challenges of:
- Delivering on the math: infrastructure AUM are forecast by Preqin11 to reach 12.9% annualised growth rate by 2030, spurred primarily by Europe (achieving 12 as the new 5);
- Addressing management fee compression: this will be lifted by longer fund durations for infrastructure and a higher volume of AUM, projected to reach $3 trillion by 203012.
- Redefining operational effectiveness and transformational leadership: the trend towards increasingly active management, and greater stakeholder engagement, indicates a boost in operational effectiveness.
Part IV: Talent truths
As models change across the board – from funding and investors, to organisational and business – what does this mean for leadership? And do we have the right portco leaders and GPs to lead the industry through this new stage of maturity?
Boyden poll data show that adaptability, resilience and influence are the top three most important skills in assessing portco candidates: