Score yourself across six dimensions, 0 to 4 points each, for a total out of 24. Every scoring level is written out below, so there is nothing to submit and no vendor tool to run — total your score, then use the band at the bottom to decide what to fix first. Data as of July 2026.
This is a self-scored, six-dimension rubric you can complete on this page — no email address, no interactive quiz, no sales call. Every framework reviewed below requires at least one of those three before it shows you a score; this one does not.
For the broader question of what AI readiness actually means and whether the right move is to build, buy, or hire, see the AI readiness overview. That page is the orientation, this page is the instrument you actually run.
Because they disagree by design. The published frameworks we could read range from four to seven dimensions, and each vendor built its assessment to lead toward its own service — a consulting engagement, a cloud platform, or a governance product.
| Framework | Dimensions | Format | Score visible on the page? |
|---|---|---|---|
| Microsoft | 7 | 45-minute interactive quiz | No — must run the tool |
| Cisco | 6 | Interactive tool, 0–100 score | Bands are public; your score needs the tool |
| RSM | 6 | Four-week paid engagement | No — not self-serve |
| TDWI | 5 | ~75-question survey | No — gated behind a work-email form |
| Eide Bailly | 4 | 10-question snapshot | Partial — short, no numeric score shown |
| Future Processing (5P) | 5 | Qualitative checklist article | No numeric scoring at all |
We use six dimensions below — the same count as Cisco's and RSM's — but not the same six. We fold model management (Microsoft's and RSM's seventh and sixth pillar) into infrastructure, because a company still deciding whether to hire an agent leader is rarely running enough models in production to need a separate MLOps pillar; that becomes relevant once someone already owns the function.
And we give named ownership its own dimension instead of folding it into a generic “Talent” or “Culture” bucket, which is what every framework above does. The data says ownership specifically, not skills training generally, is what predicts whether a pilot ships: the share of enterprises naming a dedicated AI agent owner went from 11% in 2024 to 56% in 2026, and that rise tracks the small share of organizations actually reaching production (Digital Applied, 2026). A pillar called “Talent” that really measures training hides that signal; we score it directly instead.
Score each dimension 0 to 4 using the criteria below. Be honest about where you actually are, not where the roadmap says you will be next quarter — an inflated score just produces a next action that does not match your real gap.
1. Executive sponsorship and mandate. Is there a named executive who owns AI or agent strategy, with budget and a mandate tied to a business outcome?
| Points | What it looks like |
|---|---|
| 0 | No executive owns AI; it is grassroots IT experimentation only. |
| 1 | An executive is aware and supportive but has not allocated budget or headcount. |
| 2 | Budget is approved for pilots but is not tied to a measurable business outcome. |
| 3 | A written strategy exists, owned by an executive, tied to specific outcomes and a budget. |
| 4 | A named leader owns AI strategy full time, reports to the CEO or board on it, and controls budget and hiring. |
2. Data foundations. Is the data an agent would actually touch accurate, accessible, and governed?
| Points | What it looks like |
|---|---|
| 0 | No inventory of the data agents would need; quality and access are unknown. |
| 1 | Data exists but is siloed or needs manual cleaning before any use. |
| 2 | Priority datasets are identified and partly cleaned; access is manual or ad hoc. |
| 3 | Priority datasets are documented, access-controlled, and refreshed on a known schedule. |
| 4 | Data agents touch is inventoried, quality-checked, access-governed, and its lineage is tracked. |
3. Infrastructure and tooling. Can you deploy, monitor, and roll back an agent in production, not just prototype one?
| Points | What it looks like |
|---|---|
| 0 | No AI platform decision has been made; there is no sandbox to test in. |
| 1 | A single-vendor pilot sandbox exists (an API key) with no path to production. |
| 2 | A chosen platform supports pilots; the production deployment path is undefined. |
| 3 | A production-capable stack (logging, versioning, rollback) runs at least one live agent. |
| 4 | A standardized, monitored platform runs multiple agents in production against defined SLAs, including model updates. |
4. Governance and risk controls. What happens when the agent is wrong, or touches sensitive data?
| Points | What it looks like |
|---|---|
| 0 | No AI-specific policy; agents would run under general IT policy only. |
| 1 | A policy exists on paper with no enforcement or review mechanism. |
| 2 | New use cases get a review gate, but live agents are not monitored afterward. |
| 3 | A cross-functional process reviews new agents before launch and audits live ones periodically. |
| 4 | A staffed governance function (legal, security, and business) reviews and monitors every agent against documented risk tiers. |
5. Named ownership and accountability. Is one person accountable for whether this works, with the authority to fund or stop it?
| Points | What it looks like |
|---|---|
| 0 | No individual owner; decisions default to whoever raises the issue that week. |
| 1 | An owner is named informally but has no authority to fund or stop a project. |
| 2 | An owner has authority over one function's agents, not the company-wide roadmap. |
| 3 | A single leader owns the company-wide agent roadmap as a defined, if fractional, part of their role. |
| 4 | A full-time leader, or a fractional one with a clear written mandate, owns agent strategy, execution, and outcomes company-wide. |
6. Culture and change readiness. Will the organization actually adopt what gets built, or route around it?
| Points | What it looks like |
|---|---|
| 0 | No communication to staff about AI or agents; adoption would be a surprise. |
| 1 | Leadership has announced intent, but no training or workflow redesign has started. |
| 2 | Pilot teams are trained on specific tools; there is no company-wide change plan. |
| 3 | A change plan exists with training tied to specific workflow changes. |
| 4 | Staff are trained, workflows are redesigned around agent output, and adoption is tracked as a metric. |
Add the six scores together for a total out of 24. There is no weighting: every dimension blocks progress equally, because a 4 on strategy does not offset a 0 on governance — it just means you are well-funded to build something you cannot yet control.
A 19–24 total means you are ready to size and hire. A 12–18 total means one or two named gaps are holding you back. A 6–11 total means a single foundational dimension is likely blocking everything else. A 0–5 total means get a sponsor and an owner before spending further.
| Score | Band | What it means | Do next |
|---|---|---|---|
| 19–24 | Deploy-ready | You have the ingredients; the constraint is sequencing, not readiness. | Size the hire or the build |
| 12–18 | Ready with named gaps | One or two dimensions are the actual blocker, not everything at once. | Scope a fixed audit to prioritize the gap |
| 6–11 | Foundational work first | Data, governance, or ownership is likely blocking the rest. | Fix that one dimension before adding a pilot |
| 0–5 | Not yet | No one is accountable and no budget is committed. | Get a sponsor and a named owner first |
If you scored in the top band, size the actual cost of the hire or build with the cost estimator, which uses sourced 2026 market data for full-time, fractional, and placement pricing. If you scored in either middle band, a scoped diagnostic is usually faster than guessing which gap matters most — the Agent Readiness Audit scores your specific use cases, maps the governance gaps, and ends with a named owner and a 90-day roadmap, for a fixed price rather than an open-ended engagement. If you scored in the bottom band, the fastest fix is rarely a tool purchase: named ownership is the single trait that most separates the roughly 12% of agent pilots that reach production from the 88% that do not (Digital Applied, 2026) — see our breakdown of why AI agent pilots fail for the full picture.
How long does this assessment take?
About 15 to 20 minutes if you score honestly across all six dimensions. It works best with input from whoever owns budget, whoever owns the data, and whoever would run point on the first agent — one person's guess is less reliable than three people's agreement.
Is a 0-24 score comparable to Cisco's 0-100 AI Readiness Index?
No. Cisco surveys thousands of companies to build percentile bands (Pacesetters, Chasers, Followers, Laggards); this is a self-scored rubric for one organization, not a benchmark against a global sample. Use this for a fast, directional read, and Cisco's or Microsoft's tool if you want a benchmarked score.
Do we need a data team to fill this out?
No, but you need someone who can honestly answer the data-foundation questions — whoever owns your data warehouse or BI stack, or whoever would be asked to build one if it doesn't exist yet. Guessing on that dimension defeats the point of the exercise.
What if we score low on every dimension?
That's common, and it's useful information rather than a bad outcome. A 0-5 total means the highest-leverage next step is getting a named executive sponsor and a single accountable owner in place — everything else on this list is premature until that exists.
Should we run this before or after hiring a Head of AI?
Before, ideally. Your two lowest-scoring dimensions usually tell you what the first hire needs to fix in their first quarter, so run this before writing the job description, not after the person starts.