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Somewhere in the last twelve months, the question changed. It used to be "should we be looking at AI?" Now it's "why aren't we further along?"

There is a version of this story that plays out at a surprising number of PE-backed companies in the 12 to 18 months after a close.

Forward Deployed Engineers (FDEs) are engineers embedded directly within customer environments to build and deploy production systems against real enterprise data. The role was pioneered by Palantir and is now being adopted widely across AI companies as organizations struggle to operationalize AI inside complex enterprise architectures. What distinguishes an FDE from a traditional consultant or solutions architect is ownership: they build production systems, iterate after launch, and often feed operational insights back into the platform or delivery infrastructure.

Generative AI for product design uses domain-trained models to transform natural language prompts, sketches, and reference images into production-ready CAD files, patterns, and engineering outputs tailored to individual customer specifications. Instead of batch-producing standardized designs, AI design automation allows businesses to generate unique, manufacturing-validated outputs for each customer at the same unit cost as catalog production.

Most engineering leaders evaluating conversational analytics tools are running the wrong test. They ask the system a question like: “What were Q3 revenues?”

Your Tableau licensing renewal just came through at 23% higher than last year. Again. Your CFO is asking why BI costs per user keep climbing while your AWS data infrastructure costs keep falling. It's a fair question.

Private equity portfolios depend on operating cadence. Value creation plans assume that initiatives move in parallel, decisions are made quickly, and execution risk is controlled.

PE-backed portfolio companies are under constant pressure to execute technology initiatives quickly. Post-acquisition integration, reporting improvements, platform modernization, and AI initiatives are often expected to run in parallel.

PE-backed portfolio companies face a unique set of technology challenges after an acquisition. They are expected to integrate systems, improve reporting, modernize platforms, and explore AI initiatives, often at the same time and with limited internal capacity.