By Laura Cattaneo for Data Language only
Artwork by Laura Cattaneo for Data Language only
Maturity Model

Artificial Intelligence Engineering Maturity Model

Strong engineering principles are at the core of scalable and effective artificial intelligence products and services. This maturity model combines principles from our extensive experience, as well as some elements of other similar public models.
Operational: AI in Production with successes and challenges - Maturity Stage
A business has successfully moved an important AI capability into production at this maturity stage. The service is working, but might be more expensive than planned, and there are unforeseen challenges.
Emerging: AI Data Science and Ops Capabilities - Maturity Stage
At this maturity stage, AI is pervasively used for some business models, and there is an emerging data science and AI ops capability. There are still likely to be pain points around ML training overheads process inefficiencies.
Transformational: AI is part of Business DNA - Maturity Stage
At this maturity stage, AI is part of the business DNA, and is running in harmony as part of the software delivery culture. AI services delivery, alongside all platform engineering ops, are operating in a productive rhythm, and the business is investing only in core differentiators.