AI Readiness Assessments Compared: Cisco, Microsoft, Google, Gartner, NIST and Jumbi (2026)

An AI readiness assessment measures how prepared an organization, or a specific system, is to put AI into production and keep it there. The well-known assessments differ mainly in what they examine: most score an organization's strategy, data, governance and people, while a few look at the code itself. Disclosure: Jishu Labs, which publishes this blog, makes Jumbi, one of the six compared here.

Pillars, scoring and costs below come from each publisher's own pages as of October 2026. For what readiness means in the first place, see What Is AI Readiness?

Which AI readiness assessment should you use in 2026?

Use Cisco's AI Readiness Index to benchmark a whole organization, Microsoft's AI Readiness Assessment if you build on Azure, Google Cloud's AI Adoption Framework to plan an AI capability, Gartner's AI Maturity Assessment if you are a Gartner client, the NIST AI RMF to govern AI risk, and Jumbi to check whether a codebase is ready to ship.

What is the difference between organizational and technical AI readiness?

Organizational AI readiness asks whether a company has the strategy, data, governance, infrastructure and people to run AI. Technical readiness asks whether a particular system or codebase is safe to ship. Cisco, Microsoft, Google and Gartner assess the first, Jumbi assesses the second, and NIST's framework covers how AI risk is managed across both.

The two answers can disagree. A company can score well on strategy and governance while one service still carries hardcoded secrets and no tests, and a clean codebase does not make up for missing data governance. Readiness is also specific to a use case: an organization can be ready for internal document search and unready for anything that touches customer decisions.

What does the Cisco AI Readiness Index measure?

The Cisco AI Readiness Index measures organizational readiness across six pillars: strategy, infrastructure, data, governance, talent and culture. Its online self-assessment lets you choose which pillars to score and places you in one of four stages: Pacesetters score higher than 86 out of 100, Chasers 61 to 85, Followers 31 to 60 and Laggards 0 to 30.

A stage is a benchmark position. It is useful for a leadership conversation about where the company stands, but it does not point to a specific system that needs work. Read the results with the publisher in mind, too: Cisco sells networking and data center infrastructure, and infrastructure is one of the six pillars it scores.

Best for: leadership teams that want a quick, organization-wide benchmark. Choose it over Jumbi when the question is whether the company is ready for AI, not whether one codebase is.

What does Microsoft's AI Readiness Assessment cover?

Microsoft's AI Readiness Assessment covers seven pillars: business strategy, AI governance and security, data foundations, AI strategy and experience, organization and culture, infrastructure for AI, and model management. It is a free questionnaire of multiple-choice and multiple-response questions on Microsoft Learn, and it returns a score with personalized recommendations you can work through over time.

Signed-in users can share results and export them to CSV, according to the Microsoft Assessments FAQ. The same FAQ describes the platform as helping customers improve their posture in Azure, so expect the recommendations to point toward Microsoft's own services.

Best for: organizations already building on Azure. Choose it over Jumbi when you need guidance on strategy, governance and model management rather than findings in your code.

How does Google Cloud's AI Adoption Framework assess maturity?

Google Cloud's AI Adoption Framework assesses maturity across six themes, learn, lead, access, scale, secure and automate, which span people, process, technology and data. For each theme an organization sits in one of three phases: tactical, with narrow short-term use cases; strategic, with several ML systems in production; or transformational, with ML expertise spread across the business.

It is a whitepaper, not a scored tool, so you place yourself on its maturity scale. The whitepaper is free to read. Its cited sources date from 2018 and 2019, before the current wave of generative AI, and its themes are framed around machine learning pipelines and data rather than language models or agents.

Best for: teams designing how an AI capability should grow. Choose it over Jumbi when you are planning an organization's machine learning practice rather than checking code before a release.

Is Gartner's AI Maturity Assessment worth it?

Gartner's AI Maturity Assessment is worth it mainly for organizations that already pay for Gartner. It scores maturity across seven pillars, strategy, data, governance, engineering, operating model, culture and AI product or value, and groups organizations into five stages: foundational, emerging, operational, scaled and transformational.

Clients receive a maturity heat map of current against target level for each capability, a visualization of the largest gaps and analyst recommendations, per Gartner's assessment page. Others can request an interactive demo with a work email; the page lists no price.

Best for: enterprises with a Gartner subscription that want an analyst-backed roadmap. Choose it over Jumbi when you need an operating-model and governance assessment that executives already recognize.

Where does the NIST AI Risk Management Framework fit?

The NIST AI Risk Management Framework fits as a governance lens, not a readiness score. NIST released AI RMF 1.0 on January 26, 2023, for voluntary use, built around four functions: govern, map, measure and manage. On July 26, 2024, NIST added a Generative AI Profile that identifies risks specific to generative AI and suggests actions.

NIST also publishes a companion Playbook, and its framework page says AI RMF 1.0 is being revised as part of the White House AI Action Plan. For the regulatory side in Europe, see what the EU AI Act asks of software teams.

Best for: anyone who has to explain how AI risk is owned and measured. Choose it over Jumbi when you need a vendor-neutral governance structure rather than a technical check.

What does Jumbi assess, and what does it miss?

Jumbi is an AI readiness assessment tool made by Jishu Labs. Jumbi runs guided AI readiness assessments, scores where you stand, and turns the gaps into a clear, prioritized action plan. It assesses a codebase across security, code quality, dependencies and test coverage, and its scores are risk-oriented, so a lower score means more ready.

The action plan ranks each gap by impact and effort, and re-assessing over time shows whether the risk is trending down. The free plan runs assessments with an overall score, a scorecard and basic gap analysis, with no credit card. Pro, at $15 a month as of October 2026, adds unlimited assessments, the full action plan, per-category deep dives, trend tracking, peer and industry benchmarks and report export.

Jumbi's limits are real:

• It does not assess the organization. Strategy, data governance, talent and culture, the pillars Cisco, Microsoft and Gartner score, are outside its scope.
• It is not a governance framework. It reports findings about code, not an AI risk policy, so it complements the NIST AI RMF rather than replacing it.
• It is a young product, without the years of survey data or analyst research behind the organizational assessments.
• The parts that drive improvement are paid. Deep dives, trend tracking, benchmarks and the full action plan need Pro, and self-hosting is Enterprise-only.
• Its scale runs the other way. Lower is better, which is easy to misread next to Cisco's 100-point scale, where higher is better.

Best for: teams about to ship AI-generated code to production that want to know what to fix first.

How should you combine AI readiness assessments?

Combine one organizational assessment, one governance framework and one technical check, because each answers a different question. A practical pairing is Cisco's or Microsoft's assessment for the organization, the NIST AI RMF for how AI risk is owned and measured, and a code-level assessment such as Jumbi before a specific AI feature ships.