Governing AI Spend
The AI spend governance gap
Most organisations deploy their first AI tool quickly — a small group of champions advocates for it, a pilot succeeds, and deployment accelerates. The governance processes that manage other technology spend (formal procurement, budget approval, usage review) are often bypassed in the enthusiasm to move fast.
This creates a gap: two years into an enterprise AI deployment, the organisation has multiple AI subscriptions (Claude Enterprise, GitHub Copilot, a chatbot platform, a legal AI tool, etc.) with no consolidated view of cost, no process for evaluating whether the spend is justified, and no mechanism for reallocating resources when a tool is not delivering value.
This lesson covers how to build the governance processes that prevent this gap.
Defining a usage policy for AI spend
An AI spend usage policy covers three areas:
1. Who can approve new AI tool purchases
Define a clear approval chain:
- Self-service (no approval required): individual employees may purchase personal AI subscriptions (e.g., Claude Pro, ChatGPT Plus) up to a defined monthly limit (e.g., £30/month), without a procurement process. These are personal productivity tools, not organisational infrastructure.
- Team-level approval: purchases covering a team (5–20 users) require department head sign-off and IT security review. Security review confirms data handling compliance.
- Organisational approval: purchases covering a large group (20+ users) or requiring data sharing agreements, SSO integration, or a DPA require formal procurement: Finance, IT, Legal and a named business owner.
2. What justifies a seat allocation
Define the criteria for giving someone a Claude Enterprise seat:
- They have a named use case (not "to see if it's useful")
- Their manager has confirmed they will use it
- They have completed the 45-minute onboarding training
- They are in a role where Claude is applicable (not every role in every organisation benefits equally)
This prevents seat hoarding — departments claiming seats they will not use, preventing allocation to teams with genuine need.
3. When to remove seats
Define the triggers for deprovisioning:
- User has not been active in Claude for 60 consecutive days
- User's role changes and no longer has a Claude use case
- User leaves the organisation (immediate, automated via SCIM)
A quarterly seat audit (cross-reference active users in the Usage Dashboard against the seat list) operationalises this.
The seat approval workflow
For growing organisations, manage seat additions through a lightweight approval workflow rather than ad-hoc admin requests:
Step 1: Team lead submits a seat request form (a simple internal form: requesting person's name, role, department, use case, manager approval checkbox).
Step 2: IT admin receives the request, verifies the requester is not already provisioned, confirms they have completed onboarding training (or schedules training), and provisions the seat.
Step 3: IT admin emails the team lead confirming the seat is provisioned and the user has been invited.
This workflow takes 10–15 minutes per request and creates an audit trail. It prevents the "just add them" pattern where seats are added informally and never reviewed.
Automate where possible: if your HR onboarding process already includes a "which SaaS tools does this role need?" step, add Claude Enterprise to the list. Roles that get Claude as standard (e.g., all managers, all analysts) can be auto-provisioned via SCIM. Roles that require a case-by-case decision go through the approval workflow.
Reporting on AI ROI
Finance and leadership will periodically ask whether the AI spend is justified. Prepare for this question with a standing ROI report updated quarterly:
Inputs to the report:
- Annual cost (from billing data)
- Active user count and adoption rate (from Usage Dashboard)
- Self-reported time saved per user per week (from your 30/90/180-day surveys)
- Specific productivity metrics for key Projects (e.g., "Contract Review Project: 350 contracts reviewed, estimated 87 hours of first-pass time saved at £65/hour = £5,655 value")
- Qualitative evidence — testimonials from users, case studies from high-value use cases
Simple ROI formula:
ROI = (Total estimated value generated) ÷ (Annual cost) × 100
For example: 150 active users × 45 minutes saved/week × 48 weeks × £35/hour = £189,000 estimated annual value ÷ £150,000 annual cost = 126% ROI.
This is a simplified calculation that relies on self-reported time savings, which may be optimistic. Present it as a directional estimate, not a precise figure.
Building an AI cost governance committee
For large deployments (200+ users) or organisations with multiple AI tools, consider a lightweight AI cost governance committee:
Membership: IT (chair), Finance, Legal/Compliance, 2–3 business representatives from high-usage departments.
Cadence: quarterly, 60 minutes.
Agenda:
- Usage dashboard review (10 min)
- Cost vs. budget review (10 min)
- New tool requests and evaluations (15 min)
- Policy updates (10 min)
- AOB (15 min)
Outputs: a quarterly AI cost report to the CFO/COO, decisions on new tool approvals, and any policy updates.
Scenario: the AI cost explosion
A property management company deploys Claude Enterprise (150 seats, £12,750/month). Six months later, the CFO asks the IT Director why the AI tools budget line has increased by 240% year-on-year. Investigation reveals:
- Claude Enterprise: £12,750/month (approved)
- GitHub Copilot: £840/month (approved by the engineering lead without IT review)
- A legal AI tool: £3,500/month (approved by the General Counsel without IT review)
- An HR chatbot platform: £1,200/month (approved by the HR Director without IT review)
- Five individual departmental ChatGPT Team subscriptions: ~£600/month combined (approved by department heads individually)
Total: £18,890/month. The CFO was not aware of four of the six items.
The IT Director establishes the AI cost governance committee, creates the approval workflow, and requires all AI tool purchases to route through IT for security review and Finance for budget approval. Three months later, the HR chatbot is consolidated into a Claude Enterprise Project (saving £1,200/month), and the individual ChatGPT subscriptions are cancelled as their use cases are absorbed into Claude (saving £600/month).
Key takeaway
AI spend governance is not about restricting innovation — it is about making sure the organisation knows what it is paying for, that the spend is delivering value, and that new tools go through an appropriate review process. A lightweight policy, a seat approval workflow, and a quarterly review committee are sufficient for most organisations.
📖 Official Documentation See this in practice in Anthropic’s live support docs: