Most PE Firms Are Using AI to Speed Up Engineering. The Real Value Lies Upstream.
- mdoody0
- 7 days ago
- 2 min read
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; some see code migration speeds triple. Mike Arshinskiy notes that "mid-market companies are no longer catching up, they're taking the lead."
The article focuses on execution gains: faster coding, accelerated legacy modernization, automated testing. Real benefits. But there's a higher-value application being overlooked.
The Execution Problem Is Solved. The Decision Problem Isn't.
Current AI adoption focuses almost entirely on execution speed. These tools assume the engineering decision is already made; they just help you execute faster.
In PE 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. The question isn't how fast you can migrate code. It's which platform deserves continued investment and which gets wound down. Traditionally, this takes months. You interview engineers from both companies. You audit features manually. You assess technical debt through tribal knowledge and incomplete documentation. By the time you have clarity, you've burned quarters of development capacity maintaining both systems.
AI Changes the Economics of Technical Due Diligence
The same capabilities that accelerate code migration can compress this analysis dramatically. AI-powered tools can now:
Compare feature coverage across codebases systematically; identify real overlaps versus unique capabilities
Quantify technical debt through code quality metrics, dependency analysis, security vulnerability scanning
Map integration complexity by analyzing API surfaces and system dependencies
Estimate modernization effort for each option
What took three months of manual analysis now surfaces in two weeks. AI doesn't make the decision; business context, customer commitments, team capabilities still require human judgment. But you're making that judgment with data instead of instinct.
The Compounding Effect in Portfolio Management
For PE 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 rationalization decisions; reduce duplicate system carrying costs
Identify consolidation opportunities across portfolio companies with similar tech stacks
Prioritize integration investments based on actual technical complexity, not estimates
The ROI isn't measured in developer productivity percentages. It's measured in quarters of runway saved, capital allocation clarity, reduced technical drag across the portfolio.
Implementation Reality
The underlying capabilities exist across code analysis tools, security scanners, and AI-powered platforms. The barrier isn't technology; it's recognizing where to apply it and building the analysis workflow.
Most portfolio companies use AI tactically: helping individual developers write code faster. The opportunity is using the same technology to inform capital allocation and platform investment decisions at the portfolio level.
The Quiet Advantage
PE firms that figure this out won't announce it. Faster, better-informed technical rationalization decisions just show up as improved EBITDA and smoother integrations. A quiet competitive advantage in portfolio management.
The question isn't whether AI will transform software engineering; that's already happening. The question is whether PE operators will apply it where it creates most value: not just in execution speed, but in decision quality upstream of execution.
That's where the real leverage sits.
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