AI Cost Management & FinOps

This day explores the foundations of AI cost management, including expenditure visibility, FinOps principles, cost ownership and the factors that influence AI-related spending across technologies, services and business functions. The topics covered will include:

Make AI spending visible across models, applications, infrastructure and business teams.

• AI FinOps principles and the differences between AI spending, conventional cloud costs and IT financial management
• Mapping cost drivers across large language model APIs, token usage, inference, GPUs, storage, data, software and integrations
• Analysing total cost of ownership, hidden costs and differences between pilot and production expenditure
• Defining cost ownership, allocation boundaries and reporting requirements across finance, technology and product teams
• Practical exercise on building an AI cost inventory and baseline consumption map

This day explores budgeting, forecasting and allocation approaches that provide visibility into AI expenditure while improving accountability for costs across organisational units and services. The topics covered will include:

Build forecasts and budgets that explain cost drivers and assign accountability.

• Forecasting AI workload demand using volumes, usage patterns, growth and uncertainty ranges
• Building budgets for proof of concept, rollout and ongoing production operations
• Allocating direct and shared costs through tagging, showback and chargeback
• Measuring unit economics using cost per request, user, task and successfully completed business outcome
• Practical exercise on creating a scenario-based AI budget and cost-allocation worksheet

This day explores methods for managing AI workload consumption while maintaining service quality, evaluating optimisation opportunities and improving cost efficiency across models, agents and supporting infrastructure. The topics covered will include:

Control model and agent consumption while protecting quality and service performance.

• Comparing model selection and routing options across quality, latency and cost per task
• Improving token efficiency through caching, batching, retrieval design and context-window management
• Analysing the cost of AI agents, multi-step tool calls, retries and delegated workflows
• Evaluating infrastructure utilisation, vendor pricing and commitments while managing lock-in risk
• Practical exercise on comparing baseline and optimised workloads against quality and cost constraints

This day explores governance structures, financial controls and performance measurement practices that connect AI expenditure with demonstrated business value and operational outcomes. The topics covered will include:

Connect spending policies and financial controls with measurable business performance.

• Establishing AI spending policies, cost ceilings, approval thresholds and escalation responsibilities
• Monitoring AI expenditure and using alerts to identify unusual consumption
• Evaluating AI investment value through total costs, adoption, productivity, service quality and validated benefits
• Creating executive dashboards that track spending, usage, unit costs and business outcomes
• Practical exercise on designing an AI FinOps governance scorecard and benefits measurement framework

This day explores the development of an organisation-wide approach to AI cost management, including decision-making structures, optimisation priorities, performance reviews and continuous improvement practices. The topics covered will include:

Define who makes AI spending decisions and how teams review costs, controls and performance.

• Designing a cross-functional AI FinOps operating model and defining decision rights
• Prioritising optimisation opportunities by business value, implementation effort and operating risk
• Establishing reporting schedules, review cycles and continuous improvement measures
• Reviewing cost variances and service performance to assess the impact of optimisation decisions
• Final exercise on presenting an AI cost management dashboard and explaining the spending controls and optimisation opportunities it supports