AI readiness assessment: how ready is your organization?
Six questions, two minutes, and an AI maturity score across six dimensions with the one thing to fix first.
Question 1
How is your organization currently using AI?
This helps us understand where you are on the adoption curve.
The six questions and how they are scored
Every answer maps to a level from 1 to 5. Your score for a dimension is the level you pick; your overall score is the average of the six. The full rubric is below, so you can see what each level means before you start.
AI Usage: How is your organization currently using AI?
This helps us understand where you are on the adoption curve.
- Level 1. No AI usage yet. We're exploring what's possible.
- Level 2. Using off-the-shelf tools like ChatGPT or Copilot for individual productivity.
- Level 3. Running AI pilots or proof-of-concepts for specific business problems.
- Level 4. AI in production for specific workflows, generating measurable ROI.
- Level 5. AI integrated across multiple business functions with established governance.
Data Readiness: How would you describe your data infrastructure?
AI performance depends directly on data quality and accessibility.
- Level 1. Data scattered across spreadsheets and siloed systems.
- Level 2. Centralized storage but limited integration between systems.
- Level 3. Data warehouse or lake with some automated pipelines.
- Level 4. Well-structured data pipelines with quality monitoring and documentation.
- Level 5. Enterprise data platform with governance, lineage tracking, and real-time access.
Team Skills: What AI expertise exists on your team?
Sustainable AI requires internal capability, not just vendor dependency.
- Level 1. No dedicated AI or ML expertise on the team.
- Level 2. Some team members are self-learning AI tools and concepts.
- Level 3. Data analysts who are comfortable working with AI tools and APIs.
- Level 4. Dedicated data science or ML engineering team.
- Level 5. Cross-functional AI team with MLOps, model monitoring, and continuous improvement.
Investment: What's your AI investment readiness?
Understanding your investment stage helps us recommend the right approach.
- Level 1. Exploring. No allocated budget for AI initiatives.
- Level 2. Budget approved for an initial pilot project.
- Level 3. Funded initiative with a clear timeline and success metrics.
- Level 4. Ongoing AI budget with proven ROI from previous investments.
- Level 5. Strategic AI investment portfolio across the organization.
Organization: How does leadership approach AI adoption?
Organizational readiness is often the biggest factor in AI success.
- Level 1. Leadership is skeptical or uninformed about AI's potential.
- Level 2. Executive interest but no clear champion or mandate.
- Level 3. Executive sponsor identified with change management planned.
- Level 4. AI governance in place with cross-departmental buy-in.
- Level 5. AI-first culture with continuous learning and experimentation programs.
Tech Stack: What does your current technology stack look like?
Your existing infrastructure determines the integration path for AI systems.
- Level 1. Primarily legacy systems with limited APIs or integration points.
- Level 2. Mix of legacy and modern systems with some integration capabilities.
- Level 3. Modern stack with API-first architecture and cloud services.
- Level 4. Cloud-native with microservices, good observability, and CI/CD.
- Level 5. Fully modern, cloud-native with infrastructure-as-code and automated testing.
What the AI readiness assessment measures
AI readiness is not one number. An organization can have strong executive backing and unusable data, or clean data and no one who can act on it. The assessment scores you separately on six dimensions so the result points at your actual constraint:
- AI usage. How far AI has already spread in your day-to-day work, from individual experimentation to systems in production.
- Data readiness. Whether the data AI would need is accessible, reliable, and centralized enough to build on.
- Team skills. Whether your people can evaluate, integrate, and operate AI systems, or would be starting cold.
- Investment. Whether AI has budget and executive sponsorship or runs on borrowed time.
- Organization. How your company responds to new tools and process change.
- Tech stack. Whether your current systems expose the APIs and integration points AI implementation depends on.
What your score means
Scores map to three stages, each with a different right next move. Exploring (2.0 or below) means the biggest risk is investing in the wrong initiatives; strategy comes before implementation. Building (2.1 to 3.5) means the foundation is in place and the challenge is execution: turning pilots into production systems. Scaling (above 3.5) means you have production AI and proven ROI, and the opportunity is spreading that capability across the business while building internal ownership.
Why AI readiness matters
Most enterprise AI projects stall between pilot and production, and the failure is rarely the model. It is a readiness gap that was visible before the project started: data that was not accessible, a stack with no integration points, or an organization with no owner for the result. Measuring readiness first is how you avoid paying for that lesson.
The assessment is the entry point to our AI implementation framework, a five-phase methodology that starts by assessing exactly these dimensions in depth. Your score tells you where to start; the framework is how the work gets done.
If your score points at investment rather than capability, the AI ROI calculator estimates the return on a specific project. If it points at your stack, the AI compatibility checker tests whether your systems expose the integration points an AI implementation needs.
Common questions about AI readiness
- What is an AI readiness assessment?
- An AI readiness assessment measures whether an organization has the foundations to put AI into production: usable data, people who can operate the systems, budget and sponsorship, and a technology stack with integration points. This one asks six questions, one per dimension, and returns a scored profile rather than a single pass or fail. The result shows which constraint to address first.
- What is the difference between AI readiness and AI maturity?
- The two terms describe the same measurement from different directions. An AI maturity assessment scores how far your organization has already progressed against an AI maturity model, from early experimentation to production systems with proven ROI. Readiness asks what that position means for your next project. This tool does both: it scores your maturity across six dimensions and maps the result to a recommended next step.
- How is the AI maturity score calculated?
- The assessment asks six questions, one for each dimension: AI usage, data readiness, team skills, investment, organization, and tech stack. Each answer maps to a score from 1 to 5, and the overall score is the average of the six, rounded to one decimal place. Scores of 2.0 or below map to the Exploring stage, 2.1 to 3.5 to Building, and above 3.5 to Scaling.
- How long does the assessment take?
- About two minutes. There are six multiple-choice questions, each with five answers describing stages of adoption. You can go back and change an answer before seeing your results.
- What happens with my results?
- Results appear immediately on the page: an overall score, a radar chart of the six dimension scores, and recommended next steps for your stage. Entering an email address is optional and sends a detailed report to your inbox; if you do, Convective also receives your score and contact details. You can retake the assessment at any time.
- What questions are on an AI readiness assessment?
- This one asks six, one per dimension: how your organization currently uses AI, the state of your data infrastructure, what AI expertise exists on your team, your investment readiness, how leadership approaches adoption, and what your technology stack looks like. Each has five answers describing a level from 1 to 5. The full rubric is published on this page, above the FAQ, so you can read every level before you start.
- Who should take the assessment?
- Anyone accountable for an AI decision: a CTO or VP of engineering scoping a first project, an operations leader deciding whether to automate a process, or a CEO deciding what to fund. It assumes no technical background. The questions are about your organization, not about models or algorithms.
Ready to act on your results?
We build the AI systems that turn readiness into results.