5 questions you must ask before deploying AI
Before you spend a single dollar on AI, answer these questions. You'll save yourself months of frustration.
Most AI projects don't fail for technical reasons. They fail because nobody asked the right questions at the start. According to McKinsey, only 39% of companies see any impact of AI on EBIT - and most of those that do report an impact below 5%. These five questions are the difference between success and an expensive lesson.
1. What specific problem are we solving?
Sounds obvious, but I've seen dozens of companies "deploying AI" with no clear answer to this question. "We want to be more innovative" is not a problem. "Our team spends 40 hours a week manually categorizing tickets" - that's a problem.
Without a specific problem, you have no success metric. Without a metric, you have no idea whether AI works better than an Excel sheet with macros.
2. Do we have the data?
AI without data is a car without fuel. But it's not just about quantity - it's about quality, availability, and GDPR compliance.
DATA CHECKLIST
- ✓ Is the data in one place or scattered across 15 systems?
- ✓ Do we have at least 6-12 months of history?
- ✓ Is the data compliant with GDPR/industry regulations?
- ✓ Do we have consent to use it for ML?
3. Who will be the business owner?
AI projects without a business owner die a natural death. IT will deploy it, but without a champion on the business side nobody will use the solution.
The business owner is the person who:
- • Has the budget and can make decisions
- • Understands the process we're automating
- • Will be accountable for adoption across the team
- • Has skin in the game (their KPI depends on success)
4. What's the plan B?
What happens when the AI gets it wrong? Because it will get it wrong - it's not a question of "if" but "when".
You need:
- • A process for escalating to a human
- • Monitoring that catches anomalies
- • A way to quickly switch off the AI and fall back to the old process
"AI that works in 95% of cases is a great result. But that 5% of errors across 10,000 transactions a day is 500 problems to solve."
5. How will we measure success after 90 days?
Not after a year. Not "someday". After 90 days you need to know whether it works.
Concrete metrics:
- • Process time: from X hours to Y hours
- • Cost: from X to Y per transaction
- • Quality: errors dropped from X% to Y%
- • Adoption: Y% of the team uses the solution
Summary
These five questions are not a formality. They're the difference between a project that transforms the company and yet another "innovation initiative" that ends up in a PowerPoint. Ask them before you write the first line of code.