| Date | Venue | Fees | |
|---|---|---|---|
| 07 - 11 Jun 2027 | Dubai - UAE | $ 5,950 | |
| 16 - 20 Aug 2027 | Online | $ 4,950 | |
| 13 - 17 Sep 2027 | London - UK | $ 5,950 | |
| 20 - 24 Dec 2027 | Dubai - UAE | $ 5,950 |
Introduction
This AI Cost Management & FinOps training course equips professionals to make informed decisions about AI spending. As AI services move from pilot projects into everyday operations, their costs can become harder to predict and attribute. The training course applies FinOps principles to help organisations understand what drives AI expenditure, clarify who is accountable for it and base budget decisions on how teams actually use AI.
Offered by GLOMACS in association with Excentra AI Academy, the training course gives finance, technology and business teams a shared basis for assessing the value of AI services. Participants develop the judgement to identify where spending supports business outcomes and where savings are possible while maintaining service quality. They learn to explain these trade-offs to senior decision-makers so that investment decisions reflect both cost and performance.
This AI Cost Management & FinOps training course will highlight:
- Cost drivers across generative AI models, AI agents and supporting infrastructure
- Scenario-based budgeting and forecasting for variable AI demand
- Shared-cost allocation, showback and chargeback
- AI workload optimisation across cost, quality and service performance
- Spending controls and executive reporting on AI costs and business value
Objectives
By end of this AI Cost Management & FinOps training course, participants will:
- Identify the main cost drivers of AI models, applications and supporting services.
- Build AI budgets and revise forecasts using workload assumptions and demand scenarios.
- Allocate shared AI expenditure to business units, products and service owners.
- Compare workload optimisation options against cost, quality, performance and risk criteria.
- Define spending thresholds, approval responsibilities and escalation procedures for cost anomalies.
- Build an executive dashboard that links AI spending and usage to unit costs and business outcomes.
Training Methodology
The training course combines specialist briefings, guided financial analysis and group discussion. Participants use illustrative organisational records to build budgets, allocate costs and compare workload scenarios. In the final exercise, they present an executive dashboard and explain the spending controls and optimisation opportunities supported by the data.
Organisational Impact
- Clearer visibility of AI expenditure and accountability across business functions.
- More reliable budgets and forecasts for AI pilots, rollout and ongoing operations.
- Better-informed vendor and workload decisions based on cost and service requirements.
- Consistent financial measures across finance, procurement, technology and AI teams.
- Stronger oversight of spending exceptions, operating costs and realised benefits.
Personal Impact
By attending this AI Cost Management & FinOps training course, participants will:
- Interpret AI usage and billing records to explain spending variances.
- Build realistic forecasts and compare the cost of competing workload options.
- Assess AI vendor pricing against expected usage and service requirements.
- Recommend spending controls and optimisation measures supported by evidence.
- Explain AI costs and business outcomes to senior stakeholders through clear reporting.
Who should Attend?
This GLOMACS AI Cost Management & FinOps training course will greatly benefit:
- Chief Financial Officers and Finance Directors
- Financial Controllers
- Financial Planning and Analysis Managers
- Chief Information Officers and IT Directors
- FinOps Managers and Cloud Financial Management Leads
- Technology Procurement and Vendor Managers
- AI Platform Owners and AI Product Managers
- Digital Transformation Directors
Understanding AI Costs & FinOps Fundamentals
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
AI Cost Forecasting, Budgeting & Allocation
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
Generative & Agentic AI Cost Optimisation
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
AI Financial Governance & Business Value
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
Building an Enterprise AI FinOps Strategy
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
- Upon successful completion of the classroom-based training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
- Upon successful completion of the online training course, a GLOMACS Certificate will be awarded to all delegates. Guided Learning Hours – In accordance with ISO 9001:2015–certified quality management standards, one Guided Learning Hour is awarded for every 60 minutes of attendance.
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