Private Equity: Hurdles to adopting AI

Despite AI and big data becoming a key component of public markets, private equity remains a world where signals are harder to find. Here we explore a growing trend toward using technology to accelerate the origination process.

A lack of standardised data at scale is one of the key barriers preventing PE firms from adopting AI for deal origination. AI requires a vast array of underlying data for it to work, but with the sheer complexity and uniqueness of each deal, AI is less effective for PE firms. Valuation data is also less readily available, both on purchase and on exit, making it hard to link that to the drivers of what makes a good deal.

For firms that invest in only a handful of new companies a year, it can be counter-productive to invest in large data models. By leveraging available data-sets, with some modest coding, firms can build their own proactive target-monitoring, tracking signals they know will match their investment criteria. From growth rates to Director shareholder changes, data providers are increasingly offering flexible ways of accessing. Once a basic data strategy is in place, firms can run their own AI models on top, based on learned experience from their historic portfolios. The longer the track record, the better the models.

The largest barrier to technology adoption ultimately remains one of culture. As Associates learn how to code, they’ll begin to use that ability to reduce administrative activity and run deep learning models to find the next 10x-returning companies. Portfolio managers that collaborate with third parties will boost productivity.

Scribe is a data platform for company information, powered by research-based AI. We help Banks and Private Equity to uncover deep insights about private companies and end data entry from PDFs. Welcome to the future of company knowledge.

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