AI Value Scorecard

1 Strategy & Roadmap How would you describe your organization's current AI strategy?

1We don't have one; AI decisions are made ad hoc 2We have informal goals but nothing documented or agreed upon 3We have a documented AI strategy with some leadership alignment 4We have a clear, board-aligned AI roadmap with defined priorities and KPIs

2 Strategy & Roadmap How connected is your AI activity to your broader business strategy?

1They're separate; AI is an IT initiative, not a business one 2There's loose alignment but no formal connection 3Key business leaders are aware of AI goals and generally supportive 4AI is embedded in our business strategy and owned at the executive level

3 Tool & Investment Landscape How well does your organization understand its current AI tool landscape?

1We don't have a clear picture; teams use whatever they find 2We have a rough sense, but there are tools we don't officially track 3We have an inventory of approved tools, though shadow AI is likely present 4We have a complete, governed inventory with clear policies for each tool

4 Tool & Investment Landscape How would you describe your AI governance policy?

1We don't have one 2We're working on it; something informal exists but isn't enforced 3We have a policy, but it's more restrictive than enabling 4We have a policy that both protects the organization and empowers teams to move

5 Data Foundation How would you assess the quality and accessibility of your organization's data?

1Our data is fragmented, inconsistent, and hard to access 2Some data is clean and accessible, but significant gaps exist 3Most critical data is reasonably clean, but AI readiness hasn't been formally assessed 4Our data is well-structured, documented, and assessed for AI readiness

6 Data Foundation How integrated are your data systems across the organization?

1Data lives in silos; systems don't talk to each other 2Some integration exists, but key systems are still disconnected 3Most systems are integrated, with a few exceptions 4We have a unified data architecture that supports cross-functional AI use cases

7 Talent & Skills How would you describe your organization's AI talent strategy?

1We don't have one; we hire generalists and hope for the best 2We've made a few AI-focused hires but without a clear framework 3We're building AI capability through a mix of hiring, training, and partners 4We have a deliberate build/hire/partner strategy tied to our AI roadmap

8 Talent & Skills How AI-capable are your frontline teams (beyond the IT/data function)?

1AI literacy is very low; most staff have never used AI tools 2Some individuals are experimenting, but there's no org-wide capability 3We've run training programs and many teams use AI tools regularly 4AI fluency is embedded across functions; teams know how to use AI in their work

9 Execution & Value How many of your AI pilots or POCs have reached full production?

1None; everything is still in pilot or has stalled 2One or two have made it, but most pilots don't progress 3Several initiatives are in production with measurable results 4We have a consistent track record of scaling AI from pilot to production

10 Execution & Value How would you describe the business impact of your AI efforts to date?

1Minimal; we have activity but can't point to clear business impact 2Some isolated wins, but they haven't changed how we operate 3Meaningful impact in specific areas: efficiency gains, cost reduction, or revenue 4AI is delivering compounding value; each initiative builds on the last

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Your Results Breakdown

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