Report · 2026
The 2026 State of AI Engineering
Our 2026 report on shipping AI to production: what 400 engineering leaders told us about where it stalls and what the successful teams do differently.
400
Engineering leaders surveyed
22%
Have AI in production, despite 78% having a prototype
3.4x
More likely to ship with an evaluation set in place
Summary
What we found
We surveyed 400 engineering leaders at companies from Series A to public, across the US, UK and EU, about their experience taking AI features from idea to production. We combined their answers with anonymised data from our own AI engagements.
The headline: the model is rarely the blocker. The teams that ship AI do the unglamorous engineering around it, and the teams that stall keep polishing the demo.
Findings
The six things that mattered
The prototype is not the problem
78% of teams had a working AI prototype within two weeks. Only 22% had shipped one to production. The gap is evaluation, guardrails and cost, not model capability.
Evaluation is the dividing line
Teams with an automated evaluation set were 3.4x more likely to have AI in production. Without one, every change is a guess and confidence never builds.
Cost surprises are common
41% of teams that shipped were surprised by inference cost at scale. The ones who were not had modelled it during the prototype and designed for model switching.
Guardrails are still rare
Only a third of production AI features had both input and output filtering. The teams that had been burned once now treat it as non-negotiable.
Humans stay in the loop where it matters
The successful high-stakes features almost all kept a person making the final decision, with the AI drafting. Full automation correlated with rollbacks.
The winning teams look like software teams
Version control for prompts, tests, CI, monitoring, on-call. The teams treating AI as a research project stalled; the ones treating it as engineering shipped.
Method
How we ran it
The survey ran in Q1 2026 with 400 complete responses from engineering leaders (Director level and above) at companies with at least one AI initiative. Responses were weighted by company size to avoid over-representing large organisations.
The delivery data covers Solidslate AI engagements over the previous 18 months, anonymised and aggregated. The full report includes the question set, the cross-tabs and the raw distributions.
Want help getting your AI feature to production?
Tell us what you're working on. We come back within two business days with a point of view and next steps.