Build the case around changed work: baseline, production release, usage, outcomes, and the decision to scale.
AI business cases become fragile when they count theoretical automation instead of changed work. The credible measure is whether value, cost, usage, and adoption move together after the workflow ships.
That is the difference between pay-before-value consulting and outcome-led delivery. The first release should show what got faster, safer, cheaper, clearer, or easier to scale.
EnterpriseAI business cases start with the workflow baseline, then track adoption and realised outcomes so scale decisions are based on evidence rather than enthusiasm.
Measure volume, cycle time, effort, rework, backlog, escalation, quality, and service before delivery starts.
Name the exact workflow steps AI will assist, automate, or improve, and the operating cost of doing so.
Track whether people actually complete work through the new workflow and where exceptions occur.
Compare forecast to actual results and separate hard savings, capacity, quality, risk, service, and learning.
Decide whether to expand, adjust, or stop based on evidence from the first release.
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The baseline is captured before delivery starts, even if the first version is approximate.
Savings and adoption assumptions are visible, conservative, and updated after real usage.
Delivery, subscription, integration, support, governance, training, and change effort are included.
The business case is revisited after usage and outcome data exists.
A range with clear assumptions is more credible than a single impressive number.
Capacity release is valuable when it reduces backlog, avoids hiring, improves service, or lets specialists focus on higher-value work.
Many good AI workflows create capacity, quality, risk, or service value before cash savings. The business case should label each type honestly.
Measure baseline before delivery, forecast before launch, and realised value after enough users have adopted the workflow.
Use conservative assumptions, include operating costs, track adoption, and compare forecast against actual workflow data.
We can help you baseline a workflow, estimate value, and create the post-launch evidence model.
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