Generative AI playbook for regulated enterprises
Regulated enterprises understand AI’s potential yet face governance, privacy, and change-management hurdles. This playbook outlines practical steps to move from experimental pilots to production-grade programmes.
Key takeaways
- Start with value-based use-case prioritisation
- Design responsible AI guardrails from day one
- Align teams through change management rituals
Prioritise use cases with measurable value
Successful AI programmes begin with a clear articulation of business outcomes. Work with stakeholders to map use cases to revenue, efficiency, or customer experience metrics.
A scoring matrix covering feasibility, data readiness, risk, and potential impact helps teams agree on where to invest first.
Build responsible AI guardrails
Create an AI policy that defines acceptable data usage, model monitoring, and escalation paths.
Embed human-in-the-loop reviews for high-risk workflows to maintain trust and compliance.
Operationalise with change management
Communicate the purpose of AI workflows and provide training tailored to each role.
Track impact and adoption through regular showcases and feedback loops to maintain momentum.
Frequently asked questions
How do we get started if data isn’t ready?
Begin by standing up a lightweight data foundation—ingestion pipelines, catalogues, and quality checks—focused on your first use case.
What’s the best way to govern third-party models?
Assess vendors against your security, privacy, and explainability requirements and document ongoing monitoring responsibilities in contracts.