AI and value creation in private equity: Where to next?

We read with interest the KPMG take on AI proliferation in the PE space.

Whilst there is a certain logic behind the potential use-cases for AI, in our experience, the adoption curve is still in its infancy and proof points are yet to be uncovered. Let’s dissect the arguments a bit more.

As Cherie Gartner explains, “AI tools in portfolio companies will be leveraged to streamline operations, enhance customer insights and optimise decision-making whilst building a source of a sustainable competitive advantage.” While there is some truth in this argument, the details of connecting legacy systems (and business ecosystems) with AI are far from trivial. Whether it is building a customer interaction system, or optimising reporting in finance, the maturity levels of businesses implementing roadmaps for scaling AI are currently low and outcomes unpredictable. The ethical considerations, safe usage, risk management and platform deficiencies like hallucinations, add further challenges to adopting AI, which was picked up in governance but requires stringent implementation guidelines for success.

According to Gavin Geminder, integrating AI technology into all private equity playbooks will be key, and this will continue to evolve in the years to come. In my opinion, this is the crux of the matter: as AI technologies evolve and create further layers, they will need to be fine-tuned and prioritised for six-sigma performance across the operational, financial and strategic landscapes. This will come with time – something some PE firms do not have. With the long holding periods and upheaval created by technology and AI, the firms that combine rigorous execution with testing and adoption in the first wave risk creating more challenges which may not be easy to overcome. The ability to measure impact of AI tech is critical, but it relies on the ability to test, adopt and execute at speed across the organisation.

In the PE space, the fear of being obsolete should be eclipsed with the rigour and methodical nature of managing complexity that is being outsourced to AI tech. Consider digital workforce in the age of AI: clear guidelines need to be issued (execution) and followed (compliance), which may be tricky to do if there is varying degree of AI skills in the business and tremendous energy needs to be exhausted to break the silos.

In summary, while the value creation potential of AI is significant, enterprise-wide execution roadmaps need to be inclusive, domain transient and compliant with the needs of the portfolio companies and their ecosystems.

Only with the clarity and transparency of the AI roadmap will one be able to master the current wave of tech disruption with skill and rigour, while generating a sustainable competitive advantage for the PE owners to capitalise on.