Most PE Firms Are Using AI to Speed Up Engineering. The Real Value Lies Upstream.
Inflexion recently published an analysis on AI's impact in mid-market software engineering. The data is compelling: 82% of companies using AI in development report productivity gains of 20% or more, and some see code migration speeds triple. The article focuses on execution gains — faster coding, accelerated legacy modernisation, and automated testing — and these are real benefits, but there is a higher-value application being almost entirely overlooked.
Current AI adoption in PE-backed engineering focuses on execution speed; the tools assume the engineering decision is already made and simply help you execute faster. In private equity environments, the harder problem sits upstream: which technical decisions should you make in the first place? When your portfolio company acquires a competitor, you inherit overlapping platforms — two CRMs, two billing systems, two inventory management solutions — and the question that matters is which platform deserves continued investment and which gets wound down. Traditionally, this process takes months; you interview engineers from both companies, audit features manually, and assess technical debt through tribal knowledge and incomplete documentation, and by the time you have clarity you have burned quarters of development capacity maintaining both systems.
The same capabilities that accelerate code migration can compress this analysis dramatically. AI-powered tools can now compare feature coverage across codebases systematically and identify real overlaps versus unique capabilities, quantify technical debt through code quality metrics, dependency analysis, and security vulnerability scanning, map integration complexity by analysing API surfaces and system dependencies, and estimate modernisation effort for each option. What took three months of manual analysis can now be completed in two weeks. AI does not make the decision — business context, customer commitments, and team capabilities still require human judgement — but you are making that judgement with data instead of instinct.
For private equity firms managing multiple acquisitions, this analysis capability compounds. You can assess technical risk during due diligence more thoroughly without extending timelines, make faster post-acquisition rationalisation decisions and reduce duplicate system carrying costs, identify consolidation opportunities across portfolio companies with similar tech stacks, and prioritise integration investments based on actual technical complexity rather than estimates. The ROI is not measured in developer productivity percentages; it is measured in quarters of runway saved, capital allocation clarity, and reduced technical drag across the portfolio.
The underlying capabilities exist across code analysis tools, security scanners, and AI-powered platforms. The barrier is not technology; it is recognising where to apply it and building the analysis workflow. Most portfolio companies use AI tactically, helping individual developers write code faster, and the opportunity lies in using the same technology to inform capital allocation and platform investment decisions at the portfolio level.
Private equity firms that figure this out will not announce it. Faster, better-informed technical rationalisation decisions will manifest as improved EBITDA and smoother integrations — a quiet competitive advantage in portfolio management. AI is already transforming software engineering; the question worth asking is whether private equity operators will apply it where it creates the most value, which is in decision quality upstream of execution rather than in execution speed alone.