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Challenge addressed by this assessment tool is to turn a feeling of AI use into a structured, comparable, and actionable analysis. The framework used is empirical, experience-based, simple, and easy to integrate. The tool is transferable and will allow you to repeat the assessment yourself at regular intervals.
Typical symptoms observed in organizations and teams are as follows:
Useful but scattered AI applications, dependent on individual initiatives
Difficulty to segregate experimentation, tooling, orchestration, and integration
Real value & savings are poorly measured or difficult to replicate
Governance still needs to be formalized regarding data, responsibilities & validation, in an environment where technological confidentiality is critical
A potential gap between management ambitions and actual operational practices
Three practical reasons to collaborate with us on this topic:
A proprietary, neutral, and independent framework: the AI Maturity Cube.
It transforms a subjective discussion about "where are we with AI?" into a structured, data-driven, and transferable analysis.
A dual consulting and field-based approach:
we have been supporting leaders and executive committees in transformation and operational excellence for over 20 years, while our young data science engineering consultants bring expertise in AI tools and automation.
This expertises add-up nicely to avoid both a purely strategic, "detached" perspective and a purely technical approach lacking organizational or human grounding.
A stance that is compatible with any organization seeking to create value: confidentiality, vendor neutrality (no platform resale), and explicit knowledge transfer so that you can subsequently reuse the tools internally.
Challenge addressed by this assessment tool is to turn a feeling of AI use into a structured, comparable, and actionable analysis. The framework used is empirical, experience-based, simple, and easy to integrate. The tool is transferable and will allow you to repeat the assessment yourself at regular intervals.
Typical symptoms observed in organizations and teams are as follows:
Useful but scattered AI applications, dependent on individual initiatives
Difficulty to segregate experimentation, tooling, orchestration, and integration
Real value & savings are poorly measured or difficult to replicate
Governance still needs to be formalized regarding data, responsibilities & validation, in an environment where technological confidentiality is critical
A potential gap between management ambitions and actual operational practices
Three practical reasons to collaborate with us on this topic:
A proprietary, neutral, and independent framework: the AI Maturity Cube.
It transforms a subjective discussion about "where are we with AI?" into a structured, data-driven, and transferable analysis.
A dual consulting and field-based approach:
we have been supporting leaders and executive committees in transformation and operational excellence for over 20 years, while our young data science engineering consultants bring expertise in AI tools and automation.
This expertises add-up nicely to avoid both a purely strategic, "detached" perspective and a purely technical approach lacking organizational or human grounding.
A stance that is compatible with any organization seeking to create value: confidentiality, vendor neutrality (no platform resale), and explicit knowledge transfer so that you can subsequently reuse the tools internally.