Who this is for
Business owners and IT leaders evaluating an AI initiative and wanting to ask the right questions before committing budget.
Question this answers
What should we assess and what questions should we ask before starting an AI project?
What you'll leave with
- Business case questions that reveal whether AI is the right solution
- Data readiness questions that prevent expensive surprises
- Technical questions to understand scope and feasibility
- Red flags in vendor proposals that signal trouble
Why these questions matter
Most AI projects that fail don't fail because of bad technology. They fail because the business problem wasn't well-defined, the data wasn't ready, or the expectations didn't match reality.
Asking the right questions upfront costs nothing and can prevent expensive mistakes. Here are the questions we ask every client before recommending an AI approach.
Business questions
Answer these first
- What specific business problem are we solving?
If the answer is "we want to use AI" rather than a specific problem, step back.
- How do we solve this problem today?
Understanding the current process reveals what AI needs to improve.
- What does success look like?
Time saved? Cost reduced? Quality improved? Define it before you start.
- How will we measure success?
If you can't measure it, you can't prove it worked.
- What happens when the AI gets it wrong?
Every AI system makes mistakes. Plan for error handling and human review.
- Who will use this and how often?
Usage patterns determine whether the investment makes sense.
Data questions
Data readiness check
- Where does the relevant data live?
Multiple systems? Spreadsheets? Email? The more scattered, the more prep work.
- How clean is the data?
Inconsistent formats, duplicates, and gaps all need fixing before AI can use it.
- How much data do we have?
Not all AI needs big data, but RAG systems need sufficient content to be useful.
- How often does the data change?
This determines whether you need a one-time pipeline or ongoing sync.
- Are there privacy or compliance constraints?
Healthcare, financial, or personal data adds requirements.
Technical questions
- Does this need AI or would rules-based automation work? If the logic is fully definable, automation is cheaper and more reliable than AI.
- Where will the AI run? Cloud, on-premises, or hybrid? This matters for latency, cost, and data privacy.
- What systems does it need to integrate with? APIs, databases, document stores, email systems — each integration adds complexity.
- What's our tolerance for error? An AI that's right 90% of the time is amazing for some uses and unacceptable for others.
- Do we need explainability? Some use cases (legal, medical, compliance) require the AI to show its reasoning.
Questions for AI vendors
Ask potential vendors
- Can you show a similar project you've delivered?
- What data preparation is needed before we start?
- How long will a proof of concept take?
- What are the ongoing costs after launch?
- How do you handle model updates and drift?
- What happens if we want to switch vendors later?
Red flags in vendor responses
Watch out for
- Promising specific accuracy before seeing your data
- Skipping the discovery phase and going straight to development
- Unable to explain how the AI will work in plain language
- No mention of data preparation, testing, or error handling
- Quoting on a fixed scope without understanding your data
- Using buzzwords without concrete deliverables
Key takeaways
- Start with the business problem, not the technology — "What are we trying to improve?" comes before "Should we use AI?"
- Data quality is the number one risk factor in AI projects — assess it before you commit budget
- If you can't describe how you'd measure success, the project isn't ready
- Be wary of vendors who promise specific accuracy numbers before seeing your data
- A $5K-$15K discovery phase can save you from a $100K mistake