Bring Economic Discipline to AI Spend
AI is not a feature. It is a new consumption model layered across SaaS, cloud, data platforms, and infrastructure.
Most organisations see AI adoption and spend accelerating, but few have the insights and governance to control it.
Our FinOps for AI services help you establish visibility, ownership, forecasting discipline, and commercial control before AI spend becomes normalised and embedded.
Why FinOps for AI?
AI changes how technology is consumed:
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Costs are usage-led, not entitlement-led
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Tokens, prompts, GPUs, and data scans replace predictable licence metrics
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Embedded AI features drive SaaS uplifts
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Pilots quickly become permanent run-rate costs
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Software and services are introduced with no exit plan
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Value realisation lags behind cost acceleration
Without modern governance, AI creates cost and risk. With it, AI creates value.
Engagement Models
We offer flexible delivery models:
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Standalone AI spend assessment
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Targeted optimisation reviews
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End-to-end FinOps for AI programme
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Ongoing advisory support
Who This Is For
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FinOps leaders managing unpredictable cloud growth
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ITAM professionals navigating AI-driven licence change
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CIOs and CTOs scaling AI adoption
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CFOs seeking visibility and control
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Procurement teams negotiating AI purchases and renewals
AI Spend Baseline & Exposure Assessment
Overview
A structured assessment to identify, categorise, and quantify all AI-related spend across SaaS, cloud, data platforms, and embedded licensing. Establishes a clear baseline and exposes hidden cost drivers.
Customer Benefits
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Full visibility of direct and indirect AI costs
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Identification of embedded AI licence uplifts and add-ons
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Mapping of token, inference, and GPU consumption
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Detection of fragmented or duplicated AI initiatives
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Executive-ready AI cost baseline for reporting and planning
AI Cost Allocation & Unit Economics Design
Overview
Design and implementation of allocation models that link AI consumption to teams, products, or use cases. Introduces unit economics appropriate for AI workloads.
Customer Benefits
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Clear ownership of AI consumption
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Cost per prompt, per user, per model, or per business transaction metrics
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Reduced centralised “shadow” AI spend
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Improved accountability across engineering and business teams
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Foundation for chargeback or showback models
AI Forecasting & Financial Modelling
Overview
Development of forecasting models tailored to token-based, inference-led, and GPU-intensive workloads. Addresses non-linear consumption growth and experimentation risk.
Customer Benefits
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Improved predictability of AI run-rate costs
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Scenario modelling for adoption growth
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Better renewal and budget planning
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Early warning of cost acceleration trends
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Stronger alignment between AI scale decisions and financial impact
SaaS AI Licence Optimisation
Overview
Commercial and entitlement review of AI add-ons, copilots, premium tiers, and credit-based models across Microsoft, Salesforce, ServiceNow and other SaaS providers.
Customer Benefits
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Reduction of unused or low-value AI add-ons
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Clarity on pooled vs per-user AI credit models
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Improved negotiation position at renewal
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Avoidance of modern “AI shelfware”
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Clear entitlement tracking for AI features
AI Governance & Guardrail Framework
Overview
Design of governance controls that integrate ITAM, FinOps, procurement, and data governance into a coherent AI operating model.
Customer Benefits
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Defined ownership for AI spend
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Clear guardrails for experimentation vs production
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Policy framework for training rights and data usage
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Reduced contractual and compliance risk
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Sustainable scaling of AI initiatives
Book a consultation today!
Speak with our team to find the best solutions for your needs!
