Floating Cubes

Problem

Innovation is slow and expensive

Solved by these Interventions

Create a Proof of Value prototype to test for value

Unlike a "proof of concept" project, the concept has already been proven elsewhere, and a "proof of value" project will test whether the approach has value in a specific new setting.

Technologies used

KGaaS - Knowledge Graph as a Service

Information Architecture (IA)

Knowledge Graph Creation Tools

Improved by these Interventions

Deliver a Linked Media Content Graph Platform

Move the management of all media assets into a knowledge graph to enable the creation, curation and management of rich, interconnected, valuable content assets.

Technologies used

Metadata Management Software as a Service (SaaS)

Knowledge Graphs

Video Key Moments

Create a Proof of Value prototype to test for value

Unlike a "proof of concept" project, the concept has already been proven elsewhere, and a "proof of value" project will test whether the approach has value in a specific new setting.

Technologies used

KGaaS - Knowledge Graph as a Service

Information Architecture (IA)

Knowledge Graph Creation Tools

Prevented by these Capabilities

Ease of systems integration

Ability to integrate quickly and easily with apps and business systems

All core business assets and metadata are available via API

All staff, teams, and machines are able to access assets and data via API, conforming to a common model. All teams are working to this common domain model.

Inhibits these Capabilities

Maintain a low cost of incremental innovation

To be competitive, and to have the ability to pivot rapidly, it is essential to keep the cost of innovation low.

Leads to these Problems

Staff become frustrated with arduous and inefficient legacy technology

Slow to capture new revenue opportunities

Caused by these Problems

Inability to drive meaningful user experiences with data

New products and services are created with incompatible data structures

Metadata changes are slow, expensive and painful

Knowledge Graph Platform becomes complex and expensive

Legacy support costs are growing

Data silos slow down speed to market

New products take too long to deliver

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