Before you sign that AI vendor contract or approve that project budget, stop. Ask these ten questions. If you can’t answer them clearly, you’re not ready.

I’m not trying to discourage AI investment. I’m trying to prevent bad AI investments. The difference matters.

Question 1: What Specific Problem Are We Solving?

“We need to be doing AI” is not a problem statement. Neither is “improve efficiency” or “stay competitive.”

A real problem statement looks like:

• “We spend 12,000 hours annually on manual invoice matching with a 4% error rate”
• “Customer service response times average 18 hours, causing 15% churn”
• “Our demand forecasting is 35% off, leading to $2M in excess inventory”

If you can’t articulate the problem with specificity, you can’t measure whether AI solves it.

Test: Can you explain the problem to someone outside your industry in two sentences? Can you put a dollar figure on it?

Question 2: Why Can’t This Be Solved Without AI?

AI is not the answer to every problem. Traditional automation, better processes, or simpler analytics might work just as well – often at lower cost and risk.

Before committing to AI, establish that:

• The problem genuinely requires pattern recognition, prediction, or natural language capabilities
• Traditional approaches have been evaluated and found insufficient
• The added complexity of AI is justified by the added capability

Test: Have you documented why rule-based automation or traditional analytics won’t work?

Question 3: What Data Do We Have?

AI requires data. Specifically:

• Enough data to train or fine-tune models
• Clean enough data to produce reliable results
• Relevant data that actually relates to the problem
• Accessible data that you can actually use

Most organisations overestimate their data readiness. Common discoveries include poor quality, missing fields, inconsistent formats, and access restrictions that take months to navigate.

Test: Have you done a detailed assessment of the data you need versus the data you have? Has anyone actually looked at the data, not just assumed it’s fine?

Question 4: Who Owns This Problem Today?

Every problem has a current owner – the person or team who handles it with existing processes. Successful AI projects need these people involved from the start.

Questions to answer:

• Who currently manages this process?
• Have they been involved in defining the AI solution?
• Do they support this initiative?
• Will they be responsible for the AI solution once implemented?

Test: Can you name the business owner who will be accountable for AI-driven outcomes?

Question 5: How Will We Measure Success?

Define success criteria before starting, not after. These should be:

• Specific (exact metrics, not general categories)
• Measurable (you can actually track them)
• Time-bound (when you’ll evaluate)
• Agreed (stakeholders align on what success means)

Bad metrics: “improved customer experience,” “better insights” Good metrics: “20% reduction in average handling time within 6 months,” “forecast accuracy within 15% for 90% of SKUs”

Test: Have success criteria been documented and agreed by all stakeholders?

Question 6: What Happens When the AI Is Wrong?

Every AI system makes mistakes. The question is: what are the consequences?

Consider:

• What’s the cost of a wrong prediction or recommendation?
• How will errors be detected?
• What’s the escalation path for AI failures?
• Is human review required before AI decisions become actions?

For high-stakes decisions, AI might not be appropriate. For lower-stakes decisions, error tolerance can be higher.

Test: Have you defined acceptable error rates and the processes for handling errors?

Question 7: Who Will Maintain This?

AI systems require ongoing maintenance:

• Model monitoring for performance degradation
• Retraining as data patterns change
• Updates for new requirements
• Support for users

If your plan assumes the vendor handles everything or the project team maintains it indefinitely, you haven’t thought this through.

Test: Have you identified who will maintain the system in years 2-5 and budgeted for it?

Question 8: What’s the Real Total Cost?

Vendor pricing is just the start. Real costs include:

• Data preparation (often 30-50% of project cost)
• Integration with existing systems
• Change management and training
• Ongoing maintenance and support
• Internal staff time throughout the project
• Contingency for the inevitable surprises

Most AI projects cost 2-3x the initial estimate. Plan accordingly.

Test: Have you built a comprehensive total cost of ownership model including internal costs?

Question 9: What’s the Exit Strategy?

If the AI doesn’t work, what happens? If the vendor relationship sours, what are your options?

Consider:

• Data portability – can you take your data and models elsewhere?
• Contractual lock-in – what are the terms for exit?
• Process fallback – can you revert to pre-AI processes?
• Alternative providers – who else could deliver this capability? For enterprise knowledge tools, AI consultants Brisbane like Team400 may offer viable alternatives worth keeping in your back pocket.

Test: Have you read the contract terms on termination and data ownership?

Question 10: Are We Ready to Change?

The hardest question for last. AI implementation requires organisational change:

• People need to trust AI-assisted decisions
• Processes need to incorporate AI outputs
• Culture needs to accept new ways of working
• Leadership needs to support the transition

Technology is the easy part. Organisational readiness is what determines success.

Test: Have you assessed change readiness across the affected teams? Have you budgeted for change management?

Using This Checklist

Score yourself honestly:

• 8-10 clear answers: You’re ready to proceed. Strong foundation for success.
• 5-7 clear answers: Pause and fill the gaps. You’re not ready but could be.
• 0-4 clear answers: Stop. Do the foundational work first. Proceeding now is high risk.

There’s no shame in pausing. The organisations that succeed with AI are the ones that did the preparation, not the ones that rushed in first.

Final Thought

These questions aren’t designed to prevent AI investment. They’re designed to ensure AI investment succeeds.

The answers don’t have to be perfect. They have to be honest. If you don’t know something, say so. If a gap exists, address it. If risks are real, acknowledge them.

Good preparation doesn’t guarantee success, but poor preparation almost guarantees failure.

Do the work. Then invest.