The AI Readiness Checklist: 6 Questions Before You Buy Another AI Tool

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Every enterprise software vendor now has an AI story. Most of them are compelling. Few of them are relevant to your organization’s actual readiness level.

Before you approve the next AI tool purchase, budget allocation, or pilot program, run through these six questions. They won’t tell you whether AI is worth pursuing — it almost certainly is. They’ll tell you whether your organization is ready to get value from the investment you’re about to make.

1. Do You Know What Problem You’re Solving?

This sounds obvious, but it’s where most AI initiatives go sideways. “We need an AI strategy” is not a problem statement. “Our customer support team spends 40% of their time on tier-1 tickets that could be deflected” is.

The test: Can you describe the workflow, the current cost (in time or money), and what success looks like in specific, measurable terms? If not, you’re buying a solution before you’ve defined the problem.

2. Is Your Data Accessible and Clean?

AI models are only as good as the data they operate on. If your customer data lives across four CRMs and a shared drive full of spreadsheets, no AI tool is going to magically unify it.

Key questions to answer:

  • Where does the relevant data live today?
  • Is it structured or unstructured?
  • Who owns it, and what are the access controls?
  • When was the last time it was audited for completeness and accuracy?

Most organizations that struggle with AI adoption don’t have a technology problem. They have a data infrastructure problem that predates their AI ambitions.

3. Who Will Own This Day-to-Day?

AI tools require ongoing management — prompt tuning, output review, model updates, user training. If your plan is “IT will handle it,” you need a more specific answer.

Define clearly:

  • Who is the business owner responsible for outcomes?
  • Who handles the technical maintenance?
  • Who reviews outputs for accuracy and bias?
  • What’s the escalation path when something goes wrong?

Tools without owners become shelfware. AI shelfware is especially expensive.

4. What’s Your Governance Framework?

This is the question most organizations skip entirely until something goes wrong. AI governance isn’t bureaucracy — it’s the set of policies that determine:

  • What data can AI tools access? Not every dataset should be available to every model.
  • Where are outputs used? Internal analysis is different from customer-facing decisions.
  • How do you handle errors? AI will produce incorrect outputs. What’s the review process?
  • What’s your procurement policy? Can any team buy an AI tool, or is there a review process?

If your organization doesn’t have clear answers to these questions, adding more AI tools adds more risk — not more capability.

5. Have You Audited Your Existing Stack?

Before buying a new AI-powered tool, check whether your current vendors have already shipped AI capabilities you’re paying for but not using.

In the last 18 months, almost every major SaaS platform has added AI features:

  • Your CRM likely has AI-powered lead scoring and forecasting
  • Your support platform probably offers AI ticket routing and suggested responses
  • Your BI tool may have natural language query capabilities

The audit question: What AI capabilities are already available in tools you’re paying for today? In many cases, the fastest path to AI value isn’t a new purchase — it’s activating features in your existing stack.

6. Can You Measure the Impact?

If you can’t measure it, you can’t manage it — and you definitely can’t justify the next round of investment.

Before you start, define:

  • What’s the baseline metric today? (resolution time, cost per transaction, throughput, error rate)
  • What’s the target improvement?
  • How will you measure it? (existing dashboards, new instrumentation, manual sampling)
  • What’s the timeline for evaluation?

The organizations that succeed with AI aren’t the ones that move fastest. They’re the ones that define success criteria upfront and measure against them rigorously.

The Bottom Line

AI readiness isn’t about technology sophistication. It’s about organizational clarity: knowing what problem you’re solving, whether your data supports the solution, who’s responsible, and how you’ll measure results.

Organizations that invest in this groundwork before purchasing AI tools see faster adoption, higher ROI, and fewer abandoned pilots. Those that skip it end up with a growing line item and a shrinking return.

The six questions above aren’t meant to slow you down. They’re meant to make sure that when you do invest, the investment actually lands.