Question 1 of 21
Calculating your AI product readiness…
What Does AI Product Readiness Mean?
AI product readiness is not a single yes-or-no question. It spans seven areas that, together, determine whether an AI initiative is likely to succeed once it moves past the idea stage.
- Business problemA clearly defined problem, with an owner and a way to measure value, gives an AI initiative a reason to exist beyond novelty.
- AI use caseNot every problem needs generative AI, an agent, or a predictive model. The right AI approach follows from the problem, not the other way around.
- DataAI systems are only as good as the data behind them. Availability, quality, and access determine what's realistically possible.
- Product experienceAn AI capability still has to fit into how real people work. Where it sits, and who uses it, shapes the entire design.
- ArchitectureExisting systems, APIs, and infrastructure determine how much new work is required versus how much can be reused.
- SecuritySensitive data, access controls, and human oversight need to be designed in from the start, not added afterward.
- DeliveryOwnership, budget, timeline, and engineering capacity determine whether a good idea actually ships.
What Happens After the Assessment?
Your results point to one of several reasonable next steps, depending on where your business stands today. Not every result leads to the same recommendation, and not every result is ready for a sales conversation.
- Clarifying the use case, if the business case or use case still needs definition
- AI product discovery, if the opportunity is promising but underspecified
- Architecture planning, if legacy systems or technical gaps are the main constraint
- MVP development, if you're ready to test the idea with real users
- AI integration, if you have an existing product ready to extend
- Production hardening, if you already have something built and need it to scale reliably
