AI Advantage Assessment | Marquee Insights

Question 1 of 5

How clearly has your organization defined where AI should create value first?

Think about prioritization, funding, governance, and clarity of use cases. Are teams aligned on where AI belongs, or are too many ideas competing without a clear decision model?

1 We are still exploring and have no clear AI priorities 2 We have many ideas, but no consistent way to prioritize them 3 Some priorities are emerging, but governance and decision criteria are still uneven 4 We have a focused set of priority workflows, but governance is still maturing 5 We have a clear AI focus, decision criteria, and governance model

Question 2 of 5

How much of your critical business information is trapped in documents, email, or semi-structured workflows?

PDFs, forms, scanned files, attachments, spreadsheet handoffs, and other places where information exists but is not easily usable by systems.

1 Most critical information lives in documents, email, or manual handoffs 2 A significant portion is document-bound and requires manual extraction 3 Some information is structured, but key processes still depend on documents 4 Most information is structured, with a few remaining document-heavy exceptions 5 Critical information is structured, accessible, and flowing into the right systems

Question 3 of 5

How ready are your target workflows to support AI in real operations?

Think about owners, handoffs, approvals, exception paths, and whether the process is defined well enough to survive live execution.

1 Workflows are informal, ad hoc, or largely undocumented 2 Some workflows exist, but handoffs and exceptions are handled manually 3 Key workflows are documented, but AI integration would require significant redesign 4 Target workflows are structured with clear ownership, but visibility and exception handling need work 5 Target workflows are production-ready with defined handoffs, exception handling, and governance

Question 4 of 5

How reliable are the inputs, metrics, and outputs your teams would need to trust before automating with AI?

If people routinely reconcile numbers, verify reports manually, or hesitate to act on outputs without checking, the information layer is not yet reliable enough for AI execution.

1 People routinely question the numbers and rarely act without manually checking 2 Conflicting reports are common and require manual reconciliation before decisions 3 Some metrics are trusted, but consistency and definitions vary across teams 4 Most reporting is trusted, but edge cases and cross-team alignment still create friction 5 Metrics are consistent, definitions are shared, and teams act on outputs with confidence

Question 5 of 5

How intentionally are you scaling AI across your current technology environment?

Think about whether your organization has a clear approach to grounding, orchestration, platform choice, and scaling AI in the systems you already rely on, especially in Microsoft-heavy environments.

1 We are experimenting, but there is no clear scaling approach 2 AI tools exist, but platform decisions and integration strategy are unclear 3 Some teams have a direction, but scaling is still fragmented 4 We have a growing approach to scaling AI, but grounding and platform choices still need work 5 We have a clear, intentional approach to scaling AI in our environment

Your results

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AI Fit & Governance

AI-Ready Data

Operational AI

Microsoft Intelligence

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