Choosing the first AI workflow to pilot
In-depth guideStart with a bounded task where success can be checked and failure can be contained. High volume alone does not make a process suitable for AI.
Read the guide ↗All insights/ AI strategy
AI strategy
Choose practical AI workflows and evaluate their business value.
In-depth guides
Worked examples, decision tables and troubleshooting to help you apply the ideas.
Calculating AI value after human review
An AI workflow saves time only after review, correction and operation are included. Measure the complete task rather than model response speed.
Read the guide ↗Deciding between automation rules and language models
Use deterministic rules when the decision is clear and stable. A language model is more useful when interpretation of varied language is genuinely required.
Read the guide ↗Preparing data before an AI pilot
A pilot cannot compensate for inaccessible, contradictory or poorly owned information. Data preparation is part of the product, not an optional cleanup.
Read the guide ↗Defining a human approval boundary
Human approval should occur before an action whose consequences exceed the system's demonstrated reliability. The reviewer needs enough context to make a real decision.
Read the guide ↗Writing acceptance criteria for an AI pilot
Acceptance criteria convert a promising demo into a testable proposal. Include failure conditions as well as average success.
Read the guide ↗Budgeting for AI operating costs
The operating budget includes more than model usage. Integration, retrieval, monitoring, human review and maintenance can dominate the economics.
Read the guide ↗Choosing build versus buy for an AI assistant
Buy when a product fits the workflow and governance needs without excessive workarounds. Build when differentiation or integration requirements justify ongoing ownership.
Read the guide ↗Planning employee adoption of AI tools
Adoption depends on whether the tool improves real work and fits existing responsibilities. Training cannot rescue an unreliable or poorly integrated workflow.
Read the guide ↗Designing an AI pilot exit decision
A pilot should end with a decision to expand, revise or stop. Define that decision before investing in a broader rollout.
Read the guide ↗Separating an AI proof of concept from production
A proof of concept demonstrates feasibility under selected conditions. Production requires dependable behavior across real users, failures and operational changes.
Read the guide ↗Assigning ownership for AI-generated work
Someone must remain accountable for the result even when software produces the first draft. Define responsibility across content, operation and approval.
Read the guide ↗Creating a safe fallback when AI is unavailable
A useful workflow should degrade predictably when the model, retrieval service or network fails. Plan the fallback before users depend on the tool.
Read the guide ↗Reviewing AI vendor data handling
Understand what information leaves your systems and what contractual or technical controls apply. Evaluate the actual service configuration rather than generic marketing claims.
Read the guide ↗Designing a small AI evaluation dataset
A small well-chosen dataset can expose important failures before a large rollout. Represent difficult and ordinary cases rather than only successful demonstrations.
Read the guide ↗Explaining AI confidence to business users
A fluent answer is not evidence of certainty. Give users evidence and actionable uncertainty cues rather than treating a model's self-reported confidence as calibrated truth.
Read the guide ↗Planning AI change management
A model or prompt change can alter behavior without a traditional code change. Treat it as a release with evidence and a rollback plan.
Read the guide ↗Building an AI risk register that teams will use
A risk register is useful when each item has a trigger, owner and response. A long list of vague concerns rarely changes behavior.
Read the guide ↗Estimating AI project scope without a fixed promise
Scope depends on data readiness, integrations, evaluation and operational obligations. Break the work into discovery and evidence-based delivery increments.
Read the guide ↗Choosing meaningful AI pilot metrics
Measure the outcome the workflow exists to produce. Model-level scores can help diagnosis but should not replace business-level checks.
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