Problem

Data Scientist resource wasted on non-differentiating features

A frequent problem, in all sectors where organizations are building software, is that data science resources are spent on non-core differentiators. Teams often spot a problem, and start building machine learning and AI capabilities where there are SaaS alternatives that do the job better and for a lower total cost of ownership.

Solved by these Interventions

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

Using SaaS (Software as a Service) products saves you from spending resources re-inventing the wheel, it reduces the cost of management, reduces time-to-market, and enables your team to focus on your core differentiators. If it is available to buy or rent, then don't spend your competitive advantage on rebuilding it.

Technologies used

Large Language Model (LLM) AI

KGaaS - Knowledge Graph as a Service

Enterprise Technical Architecture

Seen in these Maturity Models

Artificial Intelligence Engineering Maturity Model

Stage 2: 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.

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.