Service 08 · AI Transformation

AI spend management.

Enterprise AI spend has gone from rounding error to board-level line item in 24 months. We bring SAM discipline to the new generation of vendors.

Full Estate Discovery Contract Risk Review Consumption Optimisation
15–40
Distinct AI vendor relationships in a typical large enterprise estate.
30–50%
Of AI seats typically dormant within 90 days of provisioning.
20–35%
Reduction in total AI vendor spend our clients aim for in year one.
The problem we solve

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.

Shadow AI detection Token-level analytics Contract forensics
The spend optimisation model

Four stages. One outcome: AI spend under control.

From estate discovery to sustained governance — every action is sequenced, time-boxed, and data-driven. The spend gauge tracks reduction in real time.

AI Spend level
DISCOVER ANALYSE OPTIMISE GOVERN
↓ REDUCING
Stage 01
Weeks 1–2
Discover
Full AI estate mapped — from sanctioned tools to shadow AI. Finance, SSO logs, and SaaS platforms cross-referenced before a single vendor conversation happens.
Stage 02
Weeks 2–5
Inventory & Assess
Contract risks surfaced. Usage analytics run. Dormant licences identified. Single source of truth for AI spend built — across every department and vendor.
Stage 03
Weeks 5–10
Optimise & Consolidate
Licence harvesting, contract renegotiation, vendor consolidation, and consumption optimisation. Each move quantified. Savings tracked to the line item.
Stage 04
Week 10+
Govern & Sustain
AI procurement governance handed over. Optional managed service monitors emerging tools and reports monthly on spend and value capture.
Estate Discovery Engine

Cross-references finance systems, SSO logs, expense reports, and SaaS platforms to surface every active AI tool — including the ones procurement doesn't know about yet.

Usage Intelligence

Per-seat and per-token analytics identify dormant licences and over-provisioned tools — giving you the data to harvest licences without cutting access to genuine power users.

Governance Framework

Procurement workflows, usage monitoring, and policy guardrails that prevent the next wave of fragmented buying — integrated with existing IT governance and renewed quarterly.

What's included

Six work-streams. One outcome.

Every AI spend engagement covers these areas — adapted in scale and depth to your environment, contracts, and timeline.

01
Work-stream 01 · Weeks 1–2

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
Full shadow AI detection
02
Work-stream 02 · Weeks 2–4

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
AI-specific clause analysis
03
Work-stream 03 · Weeks 3–6

Usage analytics & dormant licence 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 — licence reclaim schedules built around renewal dates and notice periods
Right-sizing model — optimal seat counts recommended per tool and department
Dormant licence recovery
04
Work-stream 04 · Weeks 4–8

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
Token & agent cost control
05
Work-stream 05 · Weeks 6–10

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
Overlap & redundancy removal
06
Work-stream 06 · Post-programme

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 licences, 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
Ongoing procurement controls
How we work

A four-stage delivery model. Outcome-anchored.

AI Spend Management engagements typically run 8–14 weeks for an initial programme, with managed service options for ongoing governance.

01

Discover

Free no-obligation consultation. We agree which AI tools are in scope, which spend categories matter most, and what governance maturity you're aiming for.

02

Inventory & assess

Complete AI estate map built. Contract risks surfaced. Prioritised remediation list produced. By the end of this phase, you have a single source of truth for AI spend across the enterprise.

03

Optimise & consolidate

Execution. Licence harvesting, contract renegotiation, vendor consolidation, and consumption optimisation for usage-priced services. Each move quantified and tracked.

04

Govern & sustain

Hand-over of the AI procurement governance model. Optional managed service maintains visibility, monitors emerging tools, and reports monthly on AI spend and value capture.

Proof

A recent engagement.

"AI spend has the same shape as software spend a decade ago — fragmented buying, opaque pricing, weak governance. The SAM techniques translate. The vendors are different. The discipline is the same."
VP
Vash Patel
Founding & Managing Partner · Proteam Advisory
Target year-one outcome
20–35%
Reduction in total AI vendor spend within the first year of governance — without restricting access for genuine power users or stalling AI rollout momentum.
Book a consultation →
Adjacent services

Often deployed alongside.

AI Spend Management is the cost discipline. AI Adoption is the value discipline. Most enterprises need both — they answer different questions for the CFO.

Ready to talk

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.