Enterprise AI scenarios, economics and governance
Practical AI playbooks covering spend leakage, AP exceptions, token economics, approvals, demand forecasting, service operations, executive reporting and portfolio control.
The lifecycle on this page shows how AI demand should move from idea to scale or retire. Each playbook below then replaces that category view with its own workflow, systems and data boundary, human controls, KPI model and business impacts.
Wide diagram — scroll horizontally, or use the arrow keys once it has focus. A text description is available to screen readers.
Category lifecycle for the AI library. Each playbook below swaps this view for a use-case-specific operating workflow with named controls and metrics. Outcomes are not guaranteed.
The lifecycle above stays on this index page. Each playbook below swaps that category view for a workflow sized to a single enterprise scenario. AI-001 is open to read; unlock the others with your corporate details.
Each playbook includes a use-case-specific workflow, primary KPI, systems and data boundary, human controls and business impacts.
Procurement leaders need a practical way to expose spend leakage, price variance and contract non-compliance before renewal and sourcing decisions are made.
Primary KPI: Addressable spend accurately classified and linked to the right supplier and category.
Improved spend visibility across suppliers and categories, Reduced contract leakage and off-contract buying
Accounts payable teams need faster, safer handling of invoice exceptions without letting AI approve payments or override financial controls.
Primary KPI: Average AP-exception resolution time.
Faster resolution of AP exceptions, Fewer duplicate or invalid payments
HR and business leaders want capability development tailored to role-specific gaps rather than generic learning catalogues.
Primary KPI: Priority capability gaps closed for the target role population.
Faster role proficiency, More relevant training investment
Planning teams need SKU-level forecasting that combines internal and external signals while keeping planners in control of overrides and replenishment decisions.
Primary KPI: Forecast accuracy at the agreed SKU-location-horizon level.
Fewer stock-outs, Lower excess inventory
Organisations need a structured way to assess whether a business process problem is actually a good AI candidate before funding a pilot.
Primary KPI: Assessed opportunities with a validated baseline, owner and target outcome.
Fewer wasteful pilots, Faster prioritisation of viable opportunities
Customer service teams want AI to resolve suitable contacts or guide agents faster, while preserving safe escalation and policy-based answers.
Primary KPI: Eligible customer contacts resolved accurately without repeat contact.
Faster response to common issues, Lower handling effort for service teams
Executive offices need a weekly operating brief that is faster to produce, grounded in approved data and traceable to resulting decisions and actions.
Primary KPI: Time to produce and approve the weekly operating brief.
Lower reporting effort, More consistent executive metrics
Employees need quick, cited answers to policy and procedure questions without exposing restricted content or relying on outdated documents.
Primary KPI: Eligible policy queries answered correctly with authoritative citation.
Faster self-service for staff, Lower ticket volume
Legal, procurement and commercial teams need obligations, renewals and clause deviations surfaced from contracts before deadlines or exposures are missed.
Primary KPI: Active contracts with validated obligations and renewal dates recorded.
Fewer missed renewals, Faster contract review
Each playbook includes a use-case-specific workflow, primary KPI, systems and data boundary, human controls and business impacts.
Technology and finance teams need AI consumption measured at the gateway so each workload has a visible owner, cost centre and unit cost.
Primary KPI: AI consumption attributed to the correct use case, owner and cost centre.
More predictable AI run cost, Reduced token waste and avoidable model spend
Approved AI use cases need budget envelopes and business-unit showback so sponsors can act before workloads become uneconomic.
Primary KPI: AI opex assigned to an approved budget and owner.
Better budget predictability, Stronger sponsor accountability
Executives need one view of validated AI cost, adoption, risk and benefit against the baseline approved for each production use case.
Primary KPI: Production AI use cases with validated cost and benefit measured against baseline.
Better investment transparency, Earlier detection of underperformance
Each playbook includes a use-case-specific workflow, primary KPI, systems and data boundary, human controls and business impacts.
A central intake and approval process is needed so active AI use cases are visible, risk-classified and owned before they reach production.
Primary KPI: Active AI use cases registered, risk-classified and owned.
Reduced shadow AI, Faster proportional approvals
Enterprise AI portfolios need explicit stop, merge and scale decisions so funds do not remain trapped in overlapping or low-evidence initiatives.
Primary KPI: Active initiatives with a current evidence-based continuation decision.
Reduced pilot sprawl, Faster reallocation of funding
Production AI services need current control monitoring and a tested incident response path so issues can be contained before they become broader operational or assurance failures.
Primary KPI: Production AI use cases with current controls and a tested incident-response path.
Earlier detection of production issues, Faster incident containment
Related service: AI Usage Value & Governance. One playbook per category is open to read; unlock the rest with corporate details. Playbook content is illustrative and outcomes are not guaranteed.
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Playbook library access
One complimentary playbook is open in each category. Enter your details once to unlock every remaining playbook across Software Value, AI Value and Technology Governance.