AI Cost Optimization
15–40
30–50%
20–35%
AI spend is fragmented, opaque, and accumulating fast.
Microsoft Copilot, ChatGPT Enterprise, Claude for Enterprise, Gemini, plus dozens of vendor-embedded AI features in Salesforce, ServiceNow, Adobe, Atlassian — each charges separately and the line items are not small. The challenge is not that AI is expensive. It’s that AI spend is fragmented across procurement, IT, R&D, marketing, and shadow IT — accumulating faster than any procurement function was designed to track.
Pricing models don’t look like software. Tokens, agents, tasks, monthly active users, workflow runs — comparing two AI vendors on cost is rarely like-for-like. Forecasting next quarter’s bill requires usage visibility procurement doesn’t have.
And the contracts contain risks legal teams haven’t seen before — data retention, model training rights, output ownership, IP indemnification, regional data residency. These weren’t standard concerns in a Microsoft EA. They’re now central to every AI agreement.
Four stages. One outcome: AI spend under control.
Discover
Inventory & Assess
Optimise & Consolidate
Govern & Sustain
Six work-streams. One outcome.
AI estate discovery.
Forensic inventory of every active AI tool — including the ones procurement doesn’t yet know about. We pull from finance systems, expense reports, SSO logs, and SaaS management platforms to map who’s paying for what, and who’s actually using it.
- Finance and P-card cross-reference — AI line items surfaced from across the full chart of accounts
- SSO and directory integration — active AI tool access mapped against user populations
- Shadow IT scan — AI tools procured outside central IT identified and risk-assessed
- Vendor relationship map — every AI contract, renewal date, and owner logged in a single register
Contract review & risk assessment.
Every AI agreement reviewed against an AI-specific checklist. These contracts contain risks that didn’t exist in a Microsoft EA — and most legal teams haven’t had time to develop a framework for them yet.
- Data retention and deletion rights — what happens to your data when the contract ends
- Model training clauses — whether your usage trains the vendor’s models, and what opt-outs exist
- Output IP and indemnification — who owns AI-generated content, and who bears liability
- Regional data residency — where data is processed and stored, mapped against your compliance obligations
Usage analytics & dormant license harvesting.
Per-seat tools — Copilot, ChatGPT Enterprise, Claude — are typically over-provisioned. We measure actual usage, identify dormant seats, and design harvesting that reduces cost without restricting genuine power users.
- Seat-level activity analysis — active vs. dormant users measured over a 90-day rolling window
- Power-user identification — users driving measurable output protected from harvesting
- Harvesting design — license reclaim schedules built around renewal dates and notice periods
- Right-sizing model — optimal seat counts recommended per tool and department
Consumption optimisation.
For consumption-priced AI services — API access, agent platforms, model usage — we analyse usage patterns and recommend optimisations that reduce cost without reducing capability.
- Model routing analysis — identifying where cheaper models deliver equivalent output quality
- Prompt efficiency review — reducing token consumption through prompt engineering and caching
- Commitment structure — mapping usage patterns against commitment tiers for optimal unit economics
- Agent and workflow audit — identifying redundant automation runs and optimising batching
Vendor consolidation strategy.
Most AI estates contain redundant capabilities — three transcription tools, four code-assist platforms. We map functional overlap, model the trade-offs, and design a consolidation the executive team can act on.
- Capability overlap mapping — functional comparison across all active AI tools
- Consolidation trade-off modelling — cost saving vs. capability loss quantified for each scenario
- Negotiation strategy — using consolidation as leverage in vendor renewal conversations
- Migration risk assessment — user impact and change management requirements for each consolidation path
Governance framework.
Procurement workflows for new AI tools, ongoing usage monitoring, periodic vendor review cadence, and policy guardrails that prevent the next wave of fragmented buying — designed to integrate with existing IT governance.
- AI procurement playbook — intake process for new tools, approval tiers, and standard contract requirements
- Usage monitoring cadence — monthly reporting on AI spend, dormant licenses, and emerging tools
- Vendor review schedule — quarterly commercial reviews built into the governance calendar
- Policy guardrails — spend thresholds, approved vendor lists, and shadow-AI prevention controls
Want to know what your AI estate looks like?
Most CFOs we speak with can’t name their top five AI vendors by spend. The free consultation is exactly that — 30 minutes with a senior consultant to understand your AI footprint, surface the contract risks worth knowing about, and indicate the savings range we typically capture.
What you'll get
- 30-minute call with a senior AI spend specialist.
- View on the typical AI vendor risks worth flagging at your scale.
- Estimated savings range based on similar enterprises.
- Honest read on whether external help is warranted.
