Key takeaways
- The "AI multiplier" is real but unevenly distributed; companies with structured data and clean APIs see 30%+ productivity gains, others see negligible change.
- Knowledge-work productivity is the most-studied surface; the under-discussed one is operations and procurement.
- Sustained advantage requires re-architecting around AI as a primary user, not retrofitting existing systems with AI add-ons.
Two years into broad enterprise AI deployment, the productivity story is not “AI multiplies everything.” It’s “AI multiplies what was already structured.”
The multiplier is uneven {#section-1}
Companies with clean, accessible data and well-defined APIs see step-changes in throughput. Companies operating on a layer of legacy systems and undocumented processes see modest gains at best.
Operations: the underrated win {#section-2}
Most public AI productivity studies focus on knowledge-work — coding, content, research. The biggest wins we’ve seen are in operations: procurement automation, contract review, supplier onboarding. Less photogenic, more impactful.
Architecture matters more than tooling {#section-3}
The companies pulling ahead aren’t the ones with the most AI tools. They’re the ones who restructured their core systems to assume AI is a primary user — not a bolt-on.