Free Tool

AI Readiness Scorecard.

A 5-minute assessment across six dimensions of AI readiness. Honest, opinionated, and gimmick-free.

How to use this

Rate each statement from 1 (strongly disagree) to 5 (strongly agree).

We'll score you on the spot, broken down by category and with a plain-language verdict. No email required.

Progress:0 / 18
01

Leadership Alignment

Executive leadership is actively committed to AI adoption — not just curious about it.

Active commitment means budget, attention, and willingness to change. Curiosity alone doesn't ship.

Disagree
Agree

There's a named decision-maker who can sponsor AI initiatives end-to-end.

Without a single owner empowered to make calls, projects stall in committee.

Disagree
Agree

AI investments are tied to specific business outcomes, not generic "innovation" goals.

Concrete targets — revenue, cost, hours saved — separate AI projects from AI hobbies.

Disagree
Agree
02

Data Quality

Your core business data is clean and structured — not scattered across spreadsheets and PDFs.

AI is only as good as the data it works from. Messy data produces unreliable output.

Disagree
Agree

Your data sources are accessible and can be integrated without major engineering work.

Data silos and access restrictions add weeks (or months) to any AI build.

Disagree
Agree

You have enough historical data for the use cases you're considering.

Some AI applications need a lot of examples to work; others need very little. Either way, you should know which.

Disagree
Agree
03

Process Documentation

Your core workflows are documented — SOPs, playbooks, or written process docs.

If a process only lives in someone's head, you can't automate it. Documentation is the prerequisite.

Disagree
Agree

Processes are executed consistently across people and teams.

AI automates rules. If every employee does it differently, there's no rule to encode.

Disagree
Agree

You know the baseline metrics — time, cost, volume — for processes you'd want to automate.

Without baseline numbers, you can't tell whether AI moved them.

Disagree
Agree
04

Team Skills

At least one person on your team has data, analytics, or AI experience.

Internal capability accelerates implementation and gives you informed oversight.

Disagree
Agree

You have someone who can own an AI system day-to-day after it goes live.

AI systems need monitoring, tuning, and continuous improvement — not just a launch.

Disagree
Agree

Your team is comfortable adopting new tools and changing how they work.

The best AI system fails if the people meant to use it won't.

Disagree
Agree
05

Infrastructure

You run on modern cloud infrastructure — or could spin it up without a re-platforming project.

AI workloads typically need cloud compute. Legacy on-prem stacks add cost and time.

Disagree
Agree

You have security and compliance frameworks appropriate for the data you handle.

AI systems touch sensitive data. Security gaps become incidents.

Disagree
Agree

Your systems can integrate with external APIs and third-party services.

Most modern AI involves connecting to outside services. Closed systems are harder to extend.

Disagree
Agree
06

Vendor Landscape

You understand which AI tool categories are relevant to your business — versus all of them.

The AI tool space is overwhelming. Knowing the 3–5 categories that apply to you is most of the battle.

Disagree
Agree

You have a process for evaluating vendors before buying.

Without one, you'll buy on demos and regret it.

Disagree
Agree

You have budget allocated for AI tools and implementation.

AI requires real investment — tools, integration, ongoing maintenance. Underfunding kills momentum.

Disagree
Agree

Answer all 18 questions to see your score.