FARGO FACTOR INSIGHT
Before You Deploy an AI System, Ask Why It Failed
Before investing months in an AI initiative, run a pre-mortem: identify the decisions, adoption gaps, and failure points before they become expensive.
Before You Deploy an AI System, Ask Why It Failed
Before you spend six months building, buying, or rolling out an AI system, ask one question:
“It is six months from now. This failed. Why?”
That is a project pre-mortem. The purpose is not to become pessimistic or kill a useful initiative. It is to surface the assumptions that will otherwise stay hidden until time, money, and internal attention have already been spent.
Most AI work does not break because a model was not capable enough. It breaks because the business never made the underlying decisions.
A tool cannot decide what problem matters most. It cannot create ownership. It cannot make people adopt a workflow. And it cannot tell you whether the result was worth the cost unless you define success before deployment.
The pre-mortem forces that work into the open.
What to ask before deployment
1. What business problem are we actually solving?
“Use AI” is not a business objective. Start with the bottleneck: slow decisions, poor lead qualification, inconsistent customer follow-up, scattered knowledge, weak visibility, or a process that consumes expensive human time.
If you cannot name the problem and the cost of leaving it unsolved, you are not ready to choose a tool.
2. Who owns the outcome?
A system without an owner becomes another login nobody uses.
Name the person responsible for the business result, not merely the person who set up the software. That person needs authority to define the workflow, gather feedback, correct failure points, and decide whether the system should continue.
3. Why would the team fail to adopt it?
The best AI system in the world is irrelevant if it adds steps, creates confusion, or gives people no reason to change how they work.
Ask where the system fits in the real workflow. Who uses it? At what moment? What decision or task becomes easier? What gets removed?
4. What inputs does it depend on?
AI reflects the quality of the information, processes, and judgment around it.
If the source material is scattered, outdated, incomplete, or inaccessible, the output will be unreliable. If the process itself is unclear, AI will accelerate confusion instead of fixing it.
5. How will we know it is working?
Define the measure before launch: time saved, speed to decision, qualified opportunities, reduced errors, response time, conversion, capacity, or another result tied to the actual problem.
Without a defined measure, teams confuse activity with progress and keep funding systems that feel innovative but do not materially improve the business.
6. What would make us stop, change course, or narrow the scope?
NIST’s AI Risk Management Framework organizes AI risk work around four functions: Govern, Map, Measure, and Manage. That is useful because it turns “we should probably be careful” into real operating questions: who is responsible, what are the risks, how will performance be evaluated, and does deployment still make sense?
The point is not to talk yourself out of using AI. The point is to make the decisions that determine whether AI becomes leverage or another expensive distraction.
Run the pre-mortem before you commit. Identify what must be true for the initiative to work. Then build—or do not build—against the answer.