Start an AI program with consequences, then reach for tools
If your AI program starts with tools and pilots, it's already off-sequence.
Start with consequences, or you scale mistakes.
I use a simple consequence-driven sequence with clients. It looks dry on paper, but it changes everything.
• 1) Problem without AIDerive problems that keep your company or team from implementing your strategy without thinking about AI.
• 2) StakeholdersIdentify the core stakeholders and process owners.Evaluate their motivation to co-design an AI-system.Get their commitment, otherwise change your focus onto another problem.
• 3) Process realityDescribe the workflow as it runs todayWalk the real process, not the PowerPoint version.See handovers, Excel side-channels, manual fixes.Quantify the pains with KPIs relevant to your strategic goals.
• 4) Process designCreate scenarios how the new processes with software and AI support could look like.Be clear about which parts AI can take over and which are best handled by code or humans only.Define delays, errors, costs, legal risks.Quantify the potential gains.Agree on who owns the outcomes in the future.
• 5) Data + regulationCheck where data comes from, how clean it is.Check which regulations apply.Decide which failures trigger compliance exposure.
• 6) Human judgment boundariesMark the decisions that must stay human.Define where AI may suggest, where it may decide, where it is forbidden.Assign who owns the final decision line in each case.
• 7) System designOnly now describe architecture, models, infrastructure.Determine which are acceptable technical and vendor dependencies for you and aim for the maximum of sovereignty possible.Design for failure paths, monitoring, AI human handover, rollback.
Run this sequence once on a single "boring" core workflow, for example invoice matching or claims handling.
You gain a template: one shared language, one pattern of ownership, one playbook you can copy into every future AI initiative.
AI then stops being a shopping list of tools and becomes an accountability architecture across the company.
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- A good PoC in a mid-sized company ends in go or no-goWhy consultant PoCs stop at demo data, the six questions a PoC in a mid-sized company has to answer, and what the client keeps on a no-go.