AI is rewriting the economics of software and most due diligence processes haven't caught up. For PE firms investing in software assets, the gap between what traditional tech DD captures and what actually determines long-term value has never been wider.
Private equity has always demanded rigour at acquisition. Financial models, legal structures, commercial due diligence, decades of refinement. Technology due diligence, by contrast, has too often been a checklist: is the code maintained, is the architecture scalable, is there meaningful technical debt?
That framing is no longer sufficient. The question is not simply whether a software asset is well-built. The question is whether it will still matter in three to five years.
Engineering power is no longer scarce
Tools like Claude Code, Replit, and GitHub Copilot have placed meaningful engineering capability in the hands of businesses that would previously have lacked the resources to build internally. Where it once took a team of engineers months to create a bespoke workflow tool, it can now take days. Point solutions whose primary value is automating well-defined, repetitive tasks face a competitive threat that simply did not exist two years ago.
The seat model is starting to break
Orchestration layers, AI-driven middleware sitting between employees and the platforms they use, mean staff can access software outputs without logging in directly. Usage and revenue are beginning to decouple. The risks won't yet show in NRR metrics or renewal conversations, but they are building.
Four things rigorous AI due diligence must address
› Genuine moat vs apparent defensibility
Does the asset hold data that cannot be replicated, or does it simply feel sticky today?
› Pricing model durability
Is seat-based or usage-based pricing robust to orchestration layer adoption in this specific niche?
› AI substitution timeline
Which product elements are exposed to commoditisation, and how fast is adoption moving in this market?
› AI opportunity identification
Where can AI extend the asset's existing advantages, and does the business have the capability to move?
The businesses that will hold and grow value are those with real data moats, embedded distribution and the capacity to deploy AI against their proprietary advantages. Identifying them and distinguishing them from assets that only appear defensible, requires expertise at the intersection of AI implementation and commercial strategy.
About Palladium Digital Group
Palladium Digital works with leading PE firms and their portfolio companies on technology strategy and AI implementation. We'd welcome a conversation about what rigorous Tech & AI due diligence looks like for your next software deal.
