Industry playbooks

AI governance is not abstract. A bank governing a credit-decisioning model, a hospital governing a triage assistant and a SaaS vendor governing an agentic copilot are solving recognisably different problems, with different regulators, different AI systems and different evidence expectations.

Industry playbooks give each of those teams a fast, concrete starting point: the AI systems typically in scope for their vertical, the governance drivers that apply, the specific scenarios Gamut is built to solve, and a step-by-step quick start through the actual product.

The pattern behind every playbook

However different the verticals look, the Gamut method is the same. Every playbook runs the same governance lifecycle:

Discover → Assess → Classify → Govern → Evidence → Audit → Report → Improve

What changes between industries is the inputs (which AI systems exist), the routing (which frameworks apply), and the outputs (which reports and evidence the regulator or buyer expects). Gamut keeps the operating model constant so a governance team can move between use cases without relearning the platform.

How to read a playbook

Each industry page follows the same structure:

• AI systems typically in scope so you can recognise your own estate.
• Governance drivers the regulations, standards and assurance demands that apply.
• Use cases concrete scenarios, each with how Gamut solves it.
• Quick start a numbered path through the real Gamut modules (AI System Records, Intake & risk tiering, assessments, evidence, reporting) for that scenario.
• Frameworks that apply so routing is obvious from the start.

The playbooks

Industry	Representative AI under governance
Financial services	Credit decisioning, fraud, AML, trading, customer GenAI, agentic operations.
Insurance	Underwriting, claims triage, pricing, fraud detection.
Healthcare & life sciences	Clinical decision support, triage, diagnostics, research copilots.
Public sector & government	Eligibility, casework, citizen services, fraud and risk scoring.
Technology & SaaS	AI features, copilots and agentic products sold to regulated buyers.
Retail & e-commerce	Recommendations, pricing, customer service agents, demand forecasting.
Telecommunications	Network optimisation, churn, service agents, fraud.
Energy & utilities	Grid optimisation, predictive maintenance, forecasting.
Legal & professional services	Research, drafting, review and advisory copilots over confidential data.
Manufacturing & supply chain	Quality inspection, predictive maintenance, planning, robotics.
Education	Tutoring, assessment, admissions and student-support AI.
Media, entertainment & creative	Generation, recommendation, moderation and synthetic media.
Pharmaceuticals & R&D	Drug discovery, clinical trials, pharmacovigilance, manufacturing.
Automotive & autonomous systems	ADAS, autonomous driving, in-vehicle and manufacturing AI.
Real estate & property	Valuation, tenant screening, pricing, portfolio AI.
Agriculture & agritech	Yield prediction, precision application, autonomous machinery.
Gaming & interactive entertainment	Matchmaking, moderation, generative content, anti-cheat.

Working by job rather than industry?

If you would rather start from the task than the vertical, see the scenario guides: outcome-focused quick starts for jobs like governing a GenAI chatbot, EU AI Act readiness, shadow-AI discovery, vendor due diligence, board assurance, agentic workflows and ISO 42001 certification.

Not seeing your exact scenario?

The playbooks are starting points, not limits. The same Gamut lifecycle governs any AI system you register. If your use case spans verticals (for example an agentic AI product sold into banking), combine the relevant playbooks: govern the product with Technology & SaaS and the agentic stack, and demonstrate the buyer-side obligations with Financial services.

Next

• The governance lifecycle: the model every playbook runs.
• Frameworks overview: how routing selects the right controls.
• Quickstart: the fastest path to your first assessed system.