Most failed AI projects were doomed before the first model was chosen. An AI readiness assessment surfaces the gaps — in data, infrastructure, skills, and governance — while they are still cheap to fix.
Data readiness
Is the data you need collected, labeled, accessible, and legally usable? Do you know its lineage and quality? If reporting teams argue about numbers today, models will amplify that confusion tomorrow.
Infrastructure and skills
Can you serve a model behind an API with monitoring? Who retrains it when performance drifts? A capable data engineering foundation matters more than exotic ML talent in year one.
Governance
Decide who approves use cases, how outputs are audited, and what your policy says about customer data in prompts. Datastrel runs structured two-week readiness assessments that end with a prioritized, costed roadmap — not a slide deck that gathers dust.