Build, buy, or partner for enterprise AI
A decision framework for where internal teams should and shouldn't invest.
The short version
The build-vs-buy question is really a question about where your team's time creates durable advantage.
This piece is mock editorial content created for a design reference build. It exists to exercise the article template — table of contents, related content, and newsletter CTA — not to convey real guidance.
Why it matters for the enterprise
Most AI initiatives stall at the last mile: integration, evaluation, and the operational controls that let a system run unattended. The gap between a convincing demo and a governed production deployment is where value is won or lost.
Treating evaluation as a release gate — rather than an afterthought — is what separates systems teams trust from ones they quietly switch off.
What to do next
Start by baselining a single high-volume workflow: its cost, cycle time, and error rate. That baseline turns 'AI strategy' into a measurable bet.
From there, scope the smallest deployment that can clear a real production bar, and instrument it so payback is provable from day one.
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Field notes on putting AI into governed production — for the operators and engineers who own it.
Related resources
The last mile of enterprise AI is an operations problem
Demos are easy. Production is an operations problem — integration, evaluation, and control.
Measuring ROI on AI deployments without fooling yourself
Baselines, counterfactuals, and the metrics that survive a CFO review.
Treat evaluations as a release gate, not a report card
If your evals don't block a release, they're decoration. Here's how to wire them as gates.