Key takeaways
- Begin with one decision that has an owner, frequency, cost of delay and safe fallback.
- Select deployment from the data boundary and operating need, not from a cloud-versus-on-premises slogan.
- Test with representative normal, edge, missing and conflicting evidence.
- Budget for integration, evaluation, training, monitoring and handover, not only the model.
The outcome to target after 90 days
The objective is not an enterprise AI strategy deck. It is a decision about one workflow supported by evidence. At day 90, the SME should know whether the use case creates enough operational value, whether the data and integration are workable, what can fail, how it will be operated and what the next investment would buy.
Days 1 to 15: choose one operating decision
Interview process owners about recurring delays, rework, searches, handoffs and decisions. Convert ideas into a precise statement: who makes what decision, how often, using which evidence and what happens when it is late or wrong.
Shortlist use cases using value, evidence availability, integration effort, risk and owner commitment. Good first candidates often assist an existing decision: document retrieval, work-pack checking, inspection triage, maintenance evidence or exception review.
Avoid safety-critical control, broad transformation programmes and use cases with no reliable baseline.
Days 16 to 30: establish evidence and boundaries
Map source systems, data owners, identifiers, update frequency, history, permissions and known quality issues. Draw the data flow from source to user, including any model or service outside the customer's environment.
Define the authoritative source, retention, access and deployment boundary. If data must stay offline, select an on-premises model, retrieval index and integration pattern that can be updated and supported without hidden internet dependencies.
Establish the current baseline: time, rework, downtime, scrap, review effort, missed items or another measure tied to the decision. Record protected constraints such as safety, quality, regulatory and customer commitments.
Days 31 to 60: build a narrow pilot
Build the smallest end-to-end workflow that reaches the real user. Prefer read-only integration and a human approval point. Show sources, confidence or uncertainty, and the safe fallback when evidence is missing.
Create a representative acceptance set before tuning. Include normal cases, edge cases, missing records, contradictory information, obsolete documents and requests outside scope. Decide the pass, fail and stop conditions with the owner.
Days 61 to 75: test in the workflow
Run the pilot beside the current process. Observe whether people understand the output, can challenge it and have enough time to review it. Record accepted results, corrections, missed cases, unsafe suggestions, latency and integration failures.
Do not improve only the impressive examples. Investigate the failure distribution and whether the fallback actually works during network, model or data-source problems.
Days 76 to 90: decide and hand over
Compare the pilot with the baseline and protected constraints. Estimate the total next-stage cost, including integration, infrastructure, licences, evaluation, support, monitoring, training and content ownership.
Choose one of three outcomes: stop because the workflow or evidence is unsuitable, revise the scope and test again, or scale with a production plan. Assign a business owner, technical owner and risk or quality owner as appropriate.
Handover should include architecture, data flow, access, test set, known limitations, monitoring, backup, rollback, update procedure and user training. The SME should be able to explain how the system works and what to do when it fails.
Apply Industry 5.0 as a design test
Ask three questions throughout the roadmap. Does the system improve worker agency and skill? Does it measure resource trade-offs rather than move waste elsewhere? Can the operation continue safely if the model, network or supplier is unavailable?
These human-centric, sustainable and resilient tests turn Industry 5.0 from a label into practical project requirements.
What not to buy in month one
Do not begin with a large agent platform, a factory-wide data lake or a multi-year licence before the first workflow is defined. Those investments may later be justified, but the first pilot should reveal the integration, governance and operating capabilities actually required.
For an SME, disciplined scope is an advantage. One useful, owned and supportable AI workflow is a stronger foundation than many disconnected demos.
