AI Business case
AI Business Case
Enables organizations considering a GenAI proof of concept or production workload to generate a neutral, third-party business case that resonates with financial leadership.
The customer and their cloud or AI platform partner define the use case and objectives. FinOptik builds the CFO-grade financial component that helps the project cross the goal line with finance.
Approximately six to eight weeks. Available direct or through AWS Marketplace.
Is this the right engagement for us?
The methodology assumes four conditions. Where they hold, the output is decision-grade. Where they don't, the engagement produces guesses.
Large enterprise taking on a significant AI project
A blocker to production sits within the financial office, which needs more rigour in analysis and risk
The technical solution is validated and internal technical leadership is satisfied
Technical integration to return is not the open question
If the proof of concept isn't complete or the reference architecture isn't settled, it's worth waiting.
What do we need from you?
A completed proof of concept and a defined reference architecture
Projected data volumes and transaction throughput
Assumptions about model refresh cycles
Access to commercial and pricing teams, for grounded revenue scenarios
Access to finance stakeholders
A defined pilot scope, constrained by region, segment, or product line rather than full-scale rollout assumptions
What does the engagement produce?
Cost forecast for a net new AI project, grounded in projected usage such as tokens and compute, based on the use case, and separated by pilot versus rollout.
Comparative cost analysis across services that would meet the business objectives, including Bedrock, SageMaker, third-party model providers, and open-weight models on your own infrastructure.
Projections across evolutionary phases, covering model fine-tuning, retrieval-augmented generation, inference, and full custom training.
Qualitative risk considerations, including the cost of measures for data security, governance, IP, third-party licenses, regulatory regimes such as GDPR and HIPAA, and information security exposure given hosting and model options.
A cash flow and cash outlay model.
Net expense reduction estimate and projected P&L impact.
Impact on accrual earnings and EBITDA.
Sensitivity analysis identifying break-even thresholds and what must be true for the project to meet expectations.
Downside quantification, modelling financial exposure if results fall short.
Qualitative and quantitative analysis of business outcomes, with particular emphasis on the transformation of internal business operations and on potential new product offerings enabled by the project.
What do we receive at the end?
Two deliverables;
A written business case with a clear recommendation to proceed, pause, or reconsider.
A financial model with assumptions separated from calculations, built for your controllers to own and update as the pilot progresses.
Outputs are structured to fit existing budgeting, forecasting, and variance reporting processes.
What does a finished business case look like?
Industry: Gaming
Project: AI-powered anti-cheat system
Situation: A ninety-day proof of concept achieved 94% detection accuracy and 60% fewer false positives. Leadership needed a financial assessment before committing to production rollout.
| Cloud cost | $1.8M over three years |
| Protected revenue | $4.1M, most-likely scenario |
| Net EBITDA impact | +$2.3M by year three |
| Break-even | 3.2% sustained churn reduction over 18 months; pilot achieved 5% |
| Downside exposure | $400K maximum with phased rollout |
| Recommendation | Go, with stage-gates |
Sensitivity finding: Returns were roughly twice as sensitive to churn improvement as to infrastructure cost. The decision hinged on product and community validation rather than on further cost optimization.
Structure: Three phases with numeric gate criteria to proceed. Phase one at two regional servers, phase two at national scale, phase three global, with defined thresholds at each and explicit conditions for not proceeding.
Who is involved?
The output is co-built with finance office leadership, VP Architecture or VP Technology, Director of Cloud, and where relevant, cloud or AI platform account leadership.
How long does it take?
| Weeks 1–2 | Weeks 3–5 | Weeks 6–8 |
|---|---|---|
| Scoping | Modeling | Delivery |
| Define pilot scope, validate architecture, align stakeholders | Translate architecture to cost drivers, model scenarios, run sensitivity analysis | Written business case, financial model, recommendation |
Which platforms does this cover?
AWS, Azure, and Google Cloud, and AI providers including Amazon Bedrock, Anthropic, OpenAI, and open-weight models run on your own infrastructure.
We have no reseller relationship, take no rebates, and sell no tooling. Nothing about our compensation depends on which platform you select.
Why FinOptik?
Built on twenty-five years of FP&A, by co-founders of the modern FinOps framework and cloud economics practice builders from Cloudability, Onica/Rackspace, and SADA/Insight.
The models are built in finance and FP&A language, for finance to own. Rich Hoyer served on the FinOps Foundation Governing Board and co-authored the Google Cloud FinOps global operating model.
What does it cost?
$40,000 to $50,000, fixed scope. Partner funding may be available depending on your AI or cloud provider relationship.