How ready is your organization to ship AI?

Grounded in the leading enterprise frameworks (Cisco, BCG, AWS, McKinsey), this assessment scores you across six dimensions and places you on a four-tier maturity scale, with concrete next steps. Pick a depth to begin.

In short

Bigcircle's AI readiness assessment scores six weighted dimensions: strategy and value, data readiness, technology and infrastructure, talent and skills, governance, and adoption. The result is placed on four tiers, from Exploring at 0 to 25 through AI-Native at 79 to 100, and each dimension's weight is printed beside its name. A quick scan takes about 2 minutes. The full assessment takes about 5 minutes and asks more questions in each dimension.

Whether the engineers you have can ship production GenAI is the team readiness assessment.

What the assessment measures.

Strategy & Value
16%
A clear AI strategy tied to prioritized use cases and measurable outcomes.
Data Readiness
18%
Whether your data is accessible, governed, and ready to ground AI and retrieval (RAG).
Technology & Infrastructure
20%
The platform, evaluation, and observability needed to ship AI to production, not just demos.
Talent & Skills
18%
In-house capability to build and ship production AI, and how you close gaps.
Governance & Responsible AI
12%
Policies and controls for AI-specific risk: hallucination, data leakage, security, compliance.
Adoption & Operating Model
16%
How deeply AI is embedded into real workflows, with leadership backing and change management.

Where you can land.

  • Exploring

    0–25

    AI is on your radar but not yet a built capability. There is interest and maybe some experimentation, but no repeatable path from idea to production. This is a normal and fixable starting point. The fastest move now is one focused, high-value use case taken all the way to a working system.

  • Building

    26–50

    You have pilots and pockets of capability, but getting AI reliably into production is still the bottleneck. Evaluation, data access, and senior engineering are the usual constraints. The priority is turning experiments into systems your business can depend on.

  • Scaling

    51–78

    AI is in production and delivering value in places. The work now is breadth and reliability: more workflows redesigned around AI, stronger evaluation and observability, and the talent capacity to keep pace. Senior engineering time goes furthest at this stage.

  • AI-Native

    79–100

    AI is embedded in how the business runs, with strong data, platform, governance, and talent foundations. The edge now comes from the hardest builds: deeper agentic systems, ambitious tool and MCP integrations, and staying ahead of the frontier. What decides the next one is a team that has already shipped systems like it.

Common questions

  • What does Bigcircle's AI readiness assessment measure?

    Six dimensions, with the weight printed beside each name. Strategy and value, data readiness, and technology and infrastructure ask whether use cases have measurable outcomes, whether data can ground retrieval, and whether evaluation and observability exist past a demo. Talent, governance, and adoption ask about in-house build capability, controls for hallucination and data leakage, and whether AI sits in real workflows. The introduction names Cisco, BCG, AWS, and McKinsey as the frameworks the instrument is grounded in.

  • How long does the AI readiness assessment take?

    Bigcircle offers two depths on the same six dimensions. A quick scan is about 2 minutes and reads across every dimension for a directional score. The full assessment is about 5 minutes, with more questions per dimension and richer recommendations. Both place the result on the same four-tier scale.

  • What do the four AI readiness tiers mean?

    Exploring, 0 to 25, means interest without a repeatable path from idea to production. Building, 26 to 50, means pilots exist and getting AI into production is still the bottleneck. Scaling, 51 to 78, means AI is in production and the work is breadth and reliability. AI-Native, 79 to 100, means AI is embedded in how the business runs.

  • What do you get after the readiness score?

    The page shows a next step with the tier. Exploring points at taking one use case through to a working system. Building and Scaling offer a one-hour review of the results with an engineer. AI-Native points at a fixed-scope build or a studio-led programme for harder agentic systems and tool integrations.