Brief
AI readiness checklist for modern engineering teams.
Prepare teams, data, and governance for responsible AI initiatives with this concise, actionable guide.
Checklist overview
Key focus areas
- Governance questions to align AI initiatives with risk and compliance expectations.
- Data quality, lineage, and stewardship considerations before scaling models.
- Tooling requirements-MLOps platforms, experimentation tracking, and monitoring.
- Talent and operating model needs across engineering, product, and data science.
Putting it into practice
From checklist to program
- Suggested milestones for a 90-day AI readiness plan.
- Questions to ask stakeholders when prioritising AI investments.
- Metrics for tracking adoption, value, and ethical safeguards.
- Guidance on how Velinsa supports discovery, pilots, and scaling efforts.