Start With Engineering Problems, Not AI Technology<\/h2>\n
Observe a recurring task with the people doing it. Examine recent examples, including awkward cases, and record where time, information or judgement is lost. Ask what a better output would enable someone to do differently.<\/p>\n
Write each candidate in operational terms: a document controller extracts specified fields from certificates, an engineer checks them against the source, and accepted values enter a register. Identify the owner, current effort, errors and simpler alternatives. A mandatory field or deterministic calculation may need ordinary software rather than AI.<\/p>\n
Google\u2019s problem-framing guidance<\/a> separates deciding whether machine learning suits a problem from defining the machine-learning solution. Keep that distinction during discovery so the technology remains a choice to justify.<\/p>\n Look where useful information already exists but interpreting it consumes repeated effort:<\/p>\n These are candidate engineering AI applications, not established benefits. For each, ask whether an authorised user can act on the output. Predicting a problem after the intervention window has closed provides little operational value.<\/p>\n NEXATEK\u2019s AI solutions for engineering workflows<\/a> cover document processing, engineering assistants, predictive analytics and integration. Selection should narrow those capabilities to a task the company can evaluate.<\/p>\n An AI opportunity assessment should retain the evidence behind each judgement. Record assumptions and unresolved questions alongside the candidate, so confidence does not silently become a fact.<\/p>\n Establish how often the task occurs, who spends time on it and what consequence an error creates. Estimate the recoverable benefit after preparation, human checking, correction and ongoing support. Recovered capacity becomes a cash saving only where spending actually changes.<\/p>\n Include the cost of data preparation, interfaces, licences and review capacity in the business case. Avoid counting the same recovered hours again as separate productivity and cost benefits.<\/p>\n Inspect samples before judging the archive by its size. Check access rights, current revisions, units, identifiers, completeness and whether the material represents the intended users and operating conditions.<\/p>\n Data needs depend on the task. Predictive models need historical inputs linked to relevant outcomes. Document retrieval needs authoritative, permission-controlled source material and representative questions. Image analysis needs representative photographs and expert judgements for evaluation. An existing model may work without training on company data, but it still needs evaluation on representative company examples.<\/p>\n Assign an owner to gaps and estimate the work required to close them. Treat unresolved access or missing critical evidence as a prerequisite rather than a small scoring penalty.<\/p>\n Check whether the proposed output can be tested within the workflow\u2019s constraints. Include system access, response time, connectivity and the effort required to maintain the solution. Compare AI with a credible manual or rule-based baseline.<\/p>\n For an illustrative pre-pour quality flag, batching records may be available when the decision is made. The later 28-day strength result can evaluate the prediction but cannot be an input to that earlier decision. Including future information in a historical test creates a misleading result.<\/p>\n Reserve representative cases for evaluation and keep related records together when splitting data. Duplicated reports or near-identical photographs across development and evaluation sets<\/a> can exaggerate performance. Evidence from one plant may not transfer to different products or capture conditions.<\/p>\n Identify what happens when the output is wrong, incomplete or unavailable. Assess missed problems as well as false alarms. Name a qualified reviewer with access to the evidence, time to check it and authority to reject the output.<\/p>\n The NIST AI Risk Management Framework<\/a> addresses documented human oversight and consideration of viable non-AI alternatives. Applied here, oversight must be a workable part of the task. A review label alone cannot control an error the reviewer cannot detect.<\/p>\n Retain the authorised engineer\u2019s responsibility for technical acceptance. Resolve confidentiality, access and consequential failure risks before admitting a candidate to a pilot shortlist.<\/p>\n NEXATEK <\/span><\/p>\n Checking effort belongs in the AI business case <\/strong><\/p>\n If an engineer must reconstruct the source to verify every extracted value, the proposed saving may disappear. Assess how quickly reviewers can find evidence, detect errors and correct the result. That checking effort belongs in the value estimate before a pilot is funded.<\/p>\n<\/div>\n Screen out candidates with unresolved data rights, unacceptable consequences or no accountable owner. Mark missing evidence as U<\/strong>, meaning unknown, and assign follow-up work. Then compare eligible candidates using an illustrative 1-5 scale, with higher scores representing stronger evidence.<\/p>\nWhere to Look for Potential AI Use Cases?<\/h2>\n
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Evaluate Each Candidate Use Case<\/h2>\n
Business Value<\/h3>\n
Data Availability<\/h3>\n
Technical Feasibility<\/h3>\n
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<\/a>Risk and Human Oversight<\/h3>\n
Prioritize AI Use Cases<\/h2>\n
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