Should You Build This With AI? Cost and Scope

Reading

Guides in this collection.

Understanding the problemWhat does enterprise AI cost after review and rework?The useful cost is accepted work after integration, evaluation, review, exceptions, operations, and downstream error, not model usage alone.8 min readRead
Understanding the problemWhen should a business not use AI?Reject AI when rules suffice, representative evidence is unavailable, errors cannot be contained, verification erases value, rights are unresolved, or no fallback exists.8 min readRead
Understanding the problemAI governance should produce evidence, not only policyA governance programme is working only when it produces artifacts an auditor can inspect for a named system on a named date. A policy document proves that someone wrote a policy, which is a different claim.8 min readRead
Understanding the problemMeasure adoption, not deploymentA shipped system that people route around has no value, so rollout needs task-level usage, abandonment, override, and rework measurement alongside model quality.9 min readRead
Understanding the problemHow much of my team's time will checking the AI take?Compare preparation, review, correction and sign-off time with the manual baseline. Mandatory review can still save time; unattended processing needs its own evidence and approval policy.6 min readRead
Understanding the problemWill my staff actually use it?Measure adoption through completed tasks, workarounds and review effort. Fit the system to users’ routines and decisions; logins alone do not establish useful adoption.6 min readRead
Understanding the problemDo you need AI for this, or just software that works?Much of what is sold as AI is a database and a rule; AI earns its place only where the input cannot be specified in advance.6 min readRead
Understanding the problemWhich job should be the first one you hand to AI?The first job is repeated, checkable and boring, with a measure you already report; it is not the most impressive one.6 min readRead
Understanding the problemWhy do AI demos work and AI systems fail?A demo runs on chosen inputs with a person steering; a system runs on whatever arrives with nobody watching, and the gap is exceptions, access and an owner.4 min readRead
Understanding the problemWhat will an AI system cost to run after it is built?Budget review, corrections, hosting, model usage, integration maintenance and support. Their relative size depends on the workflow; a build-only quote is not a running-cost estimate.5 min readRead
Comparing the approachesGenerative AI, predictive AI, and automation solve different problemsThe three answer different questions. Automation executes a decision already specified, prediction estimates a quantity from history, and generation produces a candidate artifact that still needs checking. Choosing the wrong one shows up as a verification cost you cannot remove later.8 min readRead
Comparing the approachesDo we need our own AI model?Test rules and retrieval before custom training. Train when a measurable requirement remains unmet, and compare the complete data, integration, evaluation and maintenance cost.6 min readRead
Judging the evidenceWhat happens when the AI gets one wrong?The question for a vendor is not the accuracy figure but what happens to the one it gets wrong: who sees it, when, and what has already happened by then.6 min readRead

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