Building an AI Business Case That Survives Scrutiny

Building business cases for AI projects is harder than traditional technology investments. The benefits are often uncertain. The costs are frequently underestimated. Executives are increasingly sceptical after disappointing AI initiatives.

Here’s how to build an AI business case that survives scrutiny.

Why AI Business Cases Fail

Common failure patterns:

Vague benefits. “Improved productivity” or “better insights” aren’t measurable. Without specific metrics, benefits can’t be validated.

Optimistic assumptions. Assuming 100% user adoption, perfect data quality, and no implementation challenges produces unrealistic projections.

Incomplete costs. Including license fees but forgetting implementation, integration, training, and ongoing maintenance understates true investment.

Missing alternatives. Not comparing AI to non-AI solutions leaves the question: could we achieve this more simply?

No sensitivity analysis. Single-point estimates ignore uncertainty. What if adoption is 50% of projections?

Disconnected from strategy. AI for AI’s sake doesn’t justify investment. Connection to business priorities is essential.

The Structure That Works

Section 1: Problem Definition

Start with the business problem, not the AI solution.

What to include:

  • Clear statement of the problem being solved
  • Quantified impact of the current state (costs, delays, errors)
  • Who experiences the problem and how
  • Why existing approaches aren’t sufficient
  • Strategic importance of solving this problem

What to avoid:

  • Starting with “we should use AI”
  • Generic problems that don’t have specific impact
  • Problems that could be solved with simpler approaches

Test: Can someone unfamiliar with AI understand why this problem matters?

Section 2: Proposed Solution

Describe what you’re proposing to do.

What to include:

  • High-level solution description
  • Why AI is the appropriate approach
  • Key components and how they connect
  • Comparison with alternative approaches (including non-AI)
  • Precedents or analogous implementations

What to avoid:

  • Deep technical details (save for appendix)
  • Assuming AI is obviously the right answer
  • Ignoring simpler alternatives

Test: Is it clear what the solution does and why AI specifically is needed?

Section 3: Benefits Quantification

This is where most business cases are weakest.

What to include:

  • Specific, measurable benefit categories
  • Quantification methodology for each benefit
  • Assumptions underlying each estimate
  • Evidence supporting assumptions (pilots, benchmarks, analogies)
  • Confidence level for each benefit line

Benefit categories to consider:

  • Time savings (be specific about what happens with saved time)
  • Error reduction (with quantified error costs)
  • Capacity increase (if enabling more throughput)
  • Revenue impact (if defensibly attributable)
  • Risk reduction (if quantifiable)

What to avoid:

  • Benefits you can’t explain how to measure
  • Stacking benefits that overlap
  • Claiming full value of productivity gains

Test: Could a sceptic verify these benefits after implementation?

Section 4: Cost Estimation

Complete cost accounting.

What to include:

  • Software/platform costs (licenses, usage fees)
  • Implementation costs (internal and external)
  • Integration costs
  • Training and change management
  • Ongoing operational costs
  • Infrastructure costs
  • Contingency (15-25% for AI projects)

Categories often missed:

  • Data preparation and quality improvement
  • Security and compliance reviews
  • Internal stakeholder time
  • Opportunity cost of resources

What to avoid:

  • Only counting vendor quotes
  • Assuming zero internal effort
  • Forgetting ongoing costs

Test: If costs came in 30% higher, would anyone be surprised?

Section 5: Risk Assessment

Honest acknowledgment of what could go wrong.

What to include:

  • Technical risks (integration, performance, data quality)
  • Adoption risks (user acceptance, change resistance)
  • Organisational risks (skills, ownership, priorities)
  • External risks (vendor, regulatory, market)
  • Mitigation strategies for major risks
  • What happens if risks materialise

What to avoid:

  • Minimising risks to make the case look better
  • Generic risk lists not specific to this project
  • Risks without mitigations

Test: Would a reasonable sceptic find additional significant risks?

Section 6: Financial Analysis

Pull it together into investment metrics.

What to include:

  • Total investment required
  • Net Present Value at appropriate discount rate
  • Payback period
  • Sensitivity analysis (best case, expected case, worst case)
  • Comparison to hurdle rates and alternative investments

What to avoid:

  • Single-scenario analysis
  • Unrealistic discount rates
  • Ignoring time value of money

Test: Does the investment make sense even in a conservative scenario?

Section 7: Implementation Approach

Show that you’ve thought about how to actually deliver.

What to include:

  • High-level timeline and phases
  • Key milestones and decision points
  • Resource requirements
  • Governance and oversight approach
  • Success metrics and how they’ll be measured

What to avoid:

  • Unrealistic timelines
  • Skipping pilot/validation phases
  • Assuming everything goes smoothly

Test: Is this plan achievable by real people in real conditions?

Making It Defensible

Beyond structure, some principles:

Be conservative. Better to underpromise and overdeliver. Optimistic cases that fail damage credibility for future projects.

Show your work. Every number should trace to an assumption. Every assumption should have rationale.

Address the sceptic. Anticipate challenging questions and answer them in the document.

Include what you don’t know. Uncertainty acknowledged is more credible than false precision.

Connect to what matters. Strategic alignment isn’t optional. Explain why this investment advances business priorities.

The Approval Conversation

The document is necessary but not sufficient. In the approval meeting:

  • Lead with the problem, not the solution
  • Acknowledge limitations and risks proactively
  • Be prepared to defend assumptions
  • Have alternatives ready if concerns arise
  • Know your walk-away conditions

Executives respect intellectual honesty. A realistic case presented confidently beats an optimistic case that crumbles under questions.

Final Thought

Good AI business cases are rare. Most are either too optimistic or too vague to support informed decision-making.

The investment in building a rigorous case pays off: better decisions, appropriate expectations, and credibility for future initiatives.

Do the hard work upfront. Your future self will thank you.