AI and private equity: shifting the return distribution
Why the contribution of artificial intelligence on investment outcomes is likely to show up at the tails – and how LPs can assess manager readiness.
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Artificial intelligence (AI) is fast becoming universal across the investment industry, including within private equity. Yet amid the rush to adopt generative tools, the more important question for investors is not whether managers are using AI – but whether it can materially improve investment outcomes.
In our latest white paper, we set out the case that AI's great promise in private equity is not in reducing costs or speeding up processes, but in improving outcomes at the ‘tails’ of return distribution.
Key takeaways from the paper include:
- In private equity, a small number of outlier deals – the big winners and the potentially avoidable losses – have outsized influence. One standout investment being missed, or one big loss being avoided, can determine whether a fund outcome is strong or weak.
- AI's highest-value contribution is likely to be improving return shape: extending the upside tail by helping teams find more great investments, and shortening the downside tail by helping teams avoid more bad ones.
- AI should therefore not be about doing the same investment work with fewer resources. The larger opportunity is doing 10-100x more analysis, and doing it 10-100x faster, with the same resources – so that more weak opportunities are stopped and strong opportunities are identified earlier.
- Human judgement remains central to private equity investing. Augmentation beats autonomy – and investment philosophy, culture and proprietary data will define the winners as AI technology is commoditised.
- Currently no AI systems in use in private equity operate above the level of multi-task assistance. We anticipate this evolving over time and becoming more integrated throughout the investment and portfolio monitoring process, with greater self-correction capabilities.
The paper also introduces a practical framework for evaluating AI capability across private equity firms, distinguishing between basic automation tools and more advanced systems, using Schroders Capital’s own tools, which have been developed over the past three years, as a case study.
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