An AI investment needs an operating case
Evaluate an AI system by the work it completes reliably and the full cost of completing that work. A lower model price or a convincing demonstration is only part of the decision. The business case also needs a baseline, quality criteria, human-review effort and an owner who can operate the service.
This Barry Advisory framework helps leadership teams compare a bounded pilot with the current workflow. It is a working decision aid; input assumptions should come from your own process and evaluation results.
Define the unit of useful work
Choose a unit that matters operationally: an approved document, a correctly resolved request or a completed research task. Define what “accepted” means before counting outputs. A system generating twice as many drafts has not necessarily doubled productive work if review and correction also increase.
Build a representative evaluation set. Include ordinary tasks, ambiguous requests, missing information and Arabic/English examples when those languages are part of the workflow. Record quality, failure type, completion time and the work a person must do afterwards.
Compare options against the same acceptance criteria
- Existing process or conventional automation
- Establish the baseline and test whether simplifying the workflow resolves the problem without a model.
- Managed AI service
- Evaluate task quality, integration effort, usage costs, access controls and operating dependencies.
- Self-managed or local inference
- Include infrastructure utilization, engineering ownership, maintenance and evaluation alongside model execution costs.
The right choice depends on the task and operating constraints. Keep quality and workload comparable when evaluating cost differences; a cheaper request that needs more correction may not reduce the cost of a completed task.
Estimate recurring cost per accepted task
The calculator below includes inference, human review and a monthly infrastructure/operations allowance. Defaults are hypothetical: 10,000 tasks, $4 inference per 1,000 tasks, 0.25 minutes of review at $30 per hour, $250 monthly overhead and a 90% acceptance rate.
Monthly cost = inference cost + review hours × hourly cost + monthly overhead. Cost per accepted task = monthly cost ÷ accepted tasks. One-time implementation and training costs are separate; include them in the investment case before comparing options.
Illustrative inputs, in USD. Calculations stay in your browser; nothing is sent or saved.
What needs to be true before scaling?
- Value: the pilot improves the selected workflow against the agreed baseline.
- Quality: difficult cases meet the acceptance criteria, with a usable fallback when they do not.
- Economics: review, infrastructure and support costs remain workable at expected volumes.
- Ownership: someone owns monitoring, incidents, access, changes and user feedback.
- Deployment: the intended users can use the result in their actual process, with agreed controls.
Document the decision to expand, revise or defer and the evidence behind it. For a broader risk-management reference, NIST's Generative AI Profile complements its voluntary AI Risk Management Framework. It is separate from this commercial worksheet.
Connect the decision to relevant experience
The supplied Barry Advisory presentation reports a tenfold inference-cost reduction for a UAE AI SaaS engagement involving an in-house LLM and local inference, with the same user base. The engagement summary preserves that scope; it does not establish that another workload will produce the same result.
Start with the six-question readiness guide if the workflow or ownership is still unclear. For a scoped assessment, roadmap or implementation mandate, see AI and digital transformation advisory.