AI Business Case Template: Sections, Evidence, and Decision Criteria

Most AI business cases are optimism formatted as a document: a vendor's benchmark, a productivity multiplier, and a total that nobody will ever verify. Leadership approves them because saying no feels like falling behind, and the projects drift because nobody defined what success meant. A credible business case is different in structure: measured baseline, full costs, benefits with assumptions, risks with controls, and decision criteria that let the organization stop or expand on evidence. This template covers each section, drawing on FISTA Solutions' AI enablement practice. The measurement side is in the AI ROI measurement framework whitepaper and cost modeling in the AI total cost of ownership whitepaper.

What sections does an AI business case contain?

Section	Content	Evidence
Executive summary	Problem, proposal, ask, expected outcome, decision requested	The rest of the document
Problem and baseline	What is wrong, measured now: volume, cost, time, error, satisfaction	Operational data
Solution and scope	What AI will do, what it will not, autonomy level, integration points	Specification draft
Costs	Build, run, and change costs over the horizon	Estimates with drivers
Benefits	Mechanism, range, assumptions, timing	Baseline plus stated assumptions
Risks and controls	Register with owners	Risk assessment
Alternatives	Do nothing, buy, partner, simpler automation	Comparison
Plan and checkpoints	Phases, dates, decision points	Delivery plan
Decision criteria	Proceed, pause, or stop conditions per checkpoint	Thresholds
Owners	Sponsor, product owner, technical owner	Names

How do you establish the baseline?

Measure the process as it runs today: volumes, cycle times, cost per unit, error and rework rates, backlog, and satisfaction, from operational systems rather than estimates. If the baseline cannot be measured, the benefit cannot be verified, and the first phase of the project should be measurement. Baselines also reveal whether the problem is large enough to justify AI at all. Pilot design that starts with baselines is in the ai pilot checklist.

How do you state costs completely?

Cost category	Typical drivers
Build	Specification, engineering, integration, evaluation construction, security review
Run	Model usage scaling with adoption, infrastructure, monitoring, human review capacity
Change	Training, role redesign, communication, adoption support
Maintenance	Prompt and model updates, re-evaluation, integration upkeep
Governance	Documentation, audits, compliance work

Present costs as ranges with drivers, and show how run cost scales with adoption, because the most common surprise is a successful system that costs more as more people use it. Budget structure is in the ai budget planning checklist and agent run cost in ai agent maintenance cost.

How do you state benefits credibly?

Name the mechanism: which step gets faster, which error gets rarer, which capacity gets freed. Estimate the effect as a range with explicit assumptions about accuracy, adoption, and volume. Distinguish cost avoidance, realized savings, revenue effects, and risk reduction, because leadership values them differently. State when the estimate will be replaced by measurement. Never cite vendor benchmarks as evidence for your process. Attribution methods are in the AI ROI measurement framework whitepaper.

How should risks and alternatives be presented?

Risks as a short register: quality below threshold, adoption failure, cost overrun from usage, vendor dependence, security, compliance, and change resistance, each with likelihood, impact, control, and owner. Alternatives as a comparison: do nothing with the baseline trajectory, buy a product, partner for delivery, or apply simpler automation. A case that considered no alternatives is a proposal, not a case. Register format is in ai risk register and the build-buy decision in build vs buy vs partner for ai.

What decision criteria and checkpoints belong in the case?

Define checkpoints: after specification and evaluation design, after pilot, after production rollout, and at steady state. For each, define proceed, pause, and stop conditions: evaluation thresholds by category, adoption targets, cost per outcome ceilings, and dates. Criteria set in advance are what allow an organization to stop a failing project without a political fight. Kill criteria are in when to kill an ai project.

How do you keep the case honest after approval?

Revisit it at each checkpoint: replace estimates with measurements, update costs with actuals, re-score risks, and record decisions. Report to leadership from the updated case rather than from a new deck. Cases that are filed after approval become the source of the next year's disputed numbers. Reporting practice is in how to report ai progress to the board.

What are common mistakes?

Benefits from vendor claims; no baseline; build cost without run cost; adoption assumed at full rate from day one; no alternatives; risks listed without controls; no decision criteria; and cases written to justify a decision already made. Each is visible to an experienced reviewer within minutes. Executive persuasion built on evidence is in how to get executive buy-in for ai.

How FISTA Solutions helps build AI business cases

FISTA Solutions helps clients measure baselines, specify solutions with acceptance criteria, model full costs including usage scaling, state benefits with assumptions, and define checkpoints and decision criteria, then delivers against the case with evaluation evidence at each checkpoint. The AI enablement practice leads the work, forward deployed engineers deliver, and AI agents supplies the systems. The record behind the approach is 150+ projects with 47% efficiency gains where measured.

To build a business case leadership can approve and later verify, message FISTA on WhatsApp, or read the ai use case scoring framework for choosing which case to write first.