AI Readiness Assessment — Stonefield Strategy

Question 1 of 5

Do you have a specific, high-value problem you want AI to solve?

The clearest sign of AI readiness is knowing exactly which problem you're solving — not "we want to use AI somewhere." Businesses with a defined problem see dramatically higher ROI.

Yes, clearly defined We have a specific bottleneck or opportunity and can describe it in a sentence.

Somewhat defined We have a few ideas but haven't prioritized or validated them yet.

Vague direction We know AI matters but we're still figuring out where it fits for us.

Not sure We're exploring AI at a general level — no specific problem yet.

Question 2 of 5

Does your team have the bandwidth and willingness to adopt new tools?

The most common reason AI projects fail isn't the technology — it's people. Even the right AI tool fails without change management and team buy-in.

High readiness Our team actively uses digital tools and adapts well to change.

Moderate readiness Some team members are open; others will need time and support.

Low readiness Change is difficult here — past tool rollouts have struggled.

Significant resistance The team is stretched or skeptical. Adoption would be a major lift.

Question 3 of 5

Is your data in good enough shape to support AI?

AI needs usable data. Poor data quality is the silent killer of AI initiatives. You don't need perfect data — but you need data that's accessible, reasonably accurate, and structured enough to work with.

Good shape We have clean, organized data in systems we control and can access easily.

Decent but fragmented We have the data but it's spread across multiple systems or spreadsheets.

Needs cleanup Data quality is inconsistent and would need significant work before AI can use it.

Significant gaps Data is largely unstructured, incomplete, or inaccessible.

Question 4 of 5

Can you clearly define what success looks like?

Without a measurable definition of success, you can't evaluate ROI, make the case for continued investment, or know when to adjust course. This is often what separates pilot experiments from real outcomes.

Clear metrics We know exactly what we'd measure and what a win looks like in numbers.

General sense We have a direction — "faster" or "cheaper" — but no specific numbers yet.

Hard to measure The problem we want to solve is real, but success is difficult to quantify.

No framework yet We haven't thought through how we'd measure impact.

Question 5 of 5

Are you solving for an immediate need or building long-term capability?

Neither answer is wrong — but they require different strategies. A quick win with the wrong foundation creates technical debt. An enterprise-scale strategy may take too long to show results for an SMB. Knowing which you're doing shapes every decision.

Both — we have a plan We've thought about quick wins and where we want to be in 3 years.

Immediate need, open to more We have a pressing problem now but are thinking about the bigger picture.

Long-term vision only We want to build capability but don't have a specific immediate use case.

Still figuring it out We're not sure yet — both feel relevant and we need help prioritizing.

Stonefield AI Readiness Assessment

Do you have a specific, high-value problem you want AI to solve?

Does your team have the bandwidth and willingness to adopt new tools?

Is your data in good enough shape to support AI?

Can you clearly define what success looks like?

Are you solving for an immediate need or building long-term capability?