By Laura Cattaneo for Data Language only

Artwork by Laura Cattaneo for Data Language only

Artificial Intelligence Engineering Maturity Model

Stage 4: 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.

Businesses may experience these problems at this stage

Expensive AI training overhead

Data Scientist resource wasted on non-differentiating features

Suggested Interventions

Use SaaS to lower the Total Cost of Ownership (TCO)

Surprisingly high Total Cost of Ownership (TCO) for Data Platforms

Suggested Interventions

Use SaaS to lower the Total Cost of Ownership (TCO)

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.

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.