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Evidence before projected returns
AI business case
An AI business case explains why a defined change to work deserves investment compared with the available alternatives. It should name the baseline, expected outcome, full cost and evidence needed to test the assumptions. This guide is a decision worksheet, not a promise of return on investment. A useful case can support a pilot, a smaller process change or a decision to wait when the evidence is weak.
Create a business case that makes its assumptions testable and its next investment decision explicit.
GDS sets out ten principles for safe and responsible AI use in government, including meaningful human control. GOV.UK: AI Playbook for the UK Government ↗
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
New to the topic? Read the background on artificial intelligence.
Evidence before projected returns
what is the current task worth changing?
Describe the existing work and measure the part that the proposed workflow would affect.
An illustrative commercial team could measure account briefing effort, including source checking and correction. Record task volume, elapsed work and the acceptable result. AI use cases for business helps compare candidates. Do not attribute all time in a wider process to the part AI might assist. A baseline should distinguish a measured task from an estimate and preserve the manual alternative as a valid comparison.
An AI strategy consultancy can compare business cases across a whole portfolio.
Define the affected task.
Measure the baseline.
Label estimates clearly.
Evidence before projected returns
which benefits can the case claim?
Separate intended improvements from results actually observed in comparable work.
A projected reduction in drafting time is an assumption until tested, and it may be offset by checking effort. Quality, traceability and more consistent handovers may also matter, but each needs a practical acceptance method. LLM evaluation structures the task evidence. Avoid treating extra output as automatic value: more summaries or messages are useful only if the organisation can explain the decision they improve and the effort required to use them.
Evidence before projected returns
which costs belong in the comparison?
Include preparation, implementation, suppliers, review and continuing ownership.
AI consulting costs separates discovery, build and operating spend. AI training cost for business teams includes curriculum preparation and follow-up. Record internal work as well as invoiced costs. A workflow that saves drafting time can still create source-maintenance or incident work. Compare the same cost boundary for a manual improvement, an existing product and a custom build rather than leaving difficult recurring items outside the forecast.
| Check | What to establish |
|---|---|
| 1 | Include internal preparation. |
| 2 | Budget recurring ownership. |
| 3 | Compare equivalent cost boundaries. |
Evidence before projected returns
how should a pilot test the assumptions?
Use representative tasks and decide in advance what result would change the recommendation.
AI proof of concept consultancy can test an uncertain capability. A pilot should preserve failed cases, review effort and the conditions that made successful attempts possible. The government AI Playbook provides public-sector adoption context; it is not a guaranteed business-case method for every organisation. The sponsor should know which unanswered question the next investment resolves and what evidence would justify stopping.
Evidence before projected returns
what should the final recommendation contain?
Recommend proceed, narrow, change or stop, with reasons and accountable next steps.
Use an AI implementation roadmap for the work justified by a positive result. AI risk assessment records unresolved consequences and controls. Present observed outcomes separately from forecasts, and explain which assumptions need review after launch. A payback calculation based on estimated time is not equivalent to realised cash savings; the business must explain whether released capacity can be used and what operating responsibilities remain.
Separate observed and forecast results.
Name the next owner.
Revisit material assumptions.
Make the decision concrete
What does the current task cost in time?
Choose a task connected to ai business case and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing ai business case.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our ai consultancy service or read the ai implementation consulting overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
GDS sets out ten principles for safe and responsible AI use in government, including meaningful human control. GOV.UK: AI Playbook for the UK Government ↗
NIST describes its AI risk framework as voluntary guidance for design, use and evaluation. NIST: AI Risk Management Framework ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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