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.

Early Interest and Consideration of AI - Maturity Stage

At this maturity stage, a business is potentially in a mixture of states of mind. Some teams may be overly excited, and others cautious.

Problems

Losing market share to more nimble competitors

Legacy inertia

Painful manual insight generation

Expert resources spending time on manual, static reporting processes

Hampered by slow, expensive, and painful innovation

AI Experimentation, hopeful in AI and its promise - Maturity Stage

At the AI Experimentation maturity stage, a business is "learning through doing" and trying new approaches. At this stage a business must work hard to avoid distractions and fads.

Problems

Innovation is slow and expensive

Data Scientist resource spent on non-differentiating tasks

Distraction caused by technology fads

Capabilites

Assets have structured, descriptive metadata

AI in early Production with success 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.

Whether the context is academia or enterprise, research or commercial alike, the adoption of Software Development best practices, tools and methodologies is the key for efficient delivery of AI outcomes.

At Data Language we have worked with a number of clients at different levels of this maturity model, and witnessed first hand how injecting software craftsmanship into Data Science can lead to profoundly transformational effects.