A framework built on evidence, not hype.

Polaris Advisory evaluates organizational readiness for AI across four dimensions that collectively determine whether investments in AI will create lasting value — or stall in pilot purgatory.

The four dimensions

Where readiness is measured

Leadership Vision

AI can't succeed as a side project. It needs a thesis, a sponsor, and a budget.

Leadership Vision measures whether AI is woven into the company's strategy — with clear priorities, funded initiatives, and an executive owner accountable for outcomes.

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Organizational Culture

Tools don't transform organizations. People do.

Organizational Culture measures whether your workforce is ready to adopt AI — from literacy and trust to a safe-to-experiment environment and the talent to execute.

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Technology, Data & Governance

AI is only as trustworthy as the data behind it and the guardrails around it.

Technology, Data & Governance measures the foundations: consolidated, governed data; a repeatable deployment platform; and a responsible-AI framework that lets you scale safely.

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Value Realization

Pilots are easy. Production value is the whole point.

Value Realization measures whether AI actually delivers — solutions running in production, business impact tracked systematically, and returns reinvested to compound.

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How it works

From assessment to action

Assess

Answer twenty focused questions about your strategy, data, technology, people and governance.

Score

Receive an overall readiness score, your tier, and a breakdown that pinpoints your biggest gaps.

Connect

Get matched with vetted experts who specialize in exactly the areas you need to strengthen.

Scoring philosophy

Scores that drive decisions, not dashboards

Each dimension is scored on a 0–100 scale based on weighted responses across multiple indicators. Scores roll up into an overall readiness tier — from Emerging to Leading — that gives leaders a shared language for where they stand.

The framework intentionally surfaces the two weakest dimensions first. That is where expert matching and improvement efforts create the most leverage — and where most AI initiatives quietly fail without intervention.