AI according to Trencadis :
Drive usage in a relevant way.
Build AI that is useful, independant & above all, human.
Generative AI is already in your teams.
is it being used in the right place, with the right model, the right level of control, and the right level of human expertise?
At Trencadis, we help leaders, technology managers, business teams and transformation managers to assess their AI use cases in order to secure the value created, control costs, reduce risks and preserve critical know-how.
Meaningful AI
✳︎
Useful AI
✳︎
Human AI
✳︎
Independant AI
✳︎
Relevant usage of models
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Meaningful AI ✳︎ Useful AI ✳︎ Human AI ✳︎ Independant AI ✳︎ Relevant usage of models *
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.
The right AI model usage isn't one where AI is exploited to its fullest potential. The right model is one where the organization maintains a degree of robustness to absorb ambiguity, verification, rework, and human oversight.
We assess your AI use cases using an empirical model based on experience in AI projects and industrial resources management.
We enable you to map each use case on this curve and identify the actions needed to return to the optimal position..
Our model analyzes the key variables that determine the actual performance of an AI use case:
Personal and collective maturity level vs. AI tools
Task complexity
Effective model capability
Quality of the human-AI exchange protocol
Effective human expertise
Quality of output
Estimated true end-to-end total cost
Human capability debt
The results that you obtained
Following the asessment, you will have an actionable view:
Mapping of your AI use cases
Usage pressure score per activity
AI governance recommendations
Prioritized action plan
Plan for preserving human capabilities.
Are you a leader / an executive?
The diagnostic answers the strategic question:
Does AI truly increase the value produced by the organization without creating new critical dependencies?
You gain a clear understanding of the risks, hidden costs, potential gains, and governance trade-offs.
Objective: make AI a true leverage for a sustainable performance, not a cause for fragility
Are you responsible for technology or in charge of a digital transformation?
The asessment helps you streamline your AI architecture:
Which model for which use case?
Which tasks should be automated, assisted, or left under human control?
What controls should be implemented?
How could you limit our reliance on large, general-purpose models?
How could you build a scalable, learning, and controlled architecture?
Objective : choose the most sober models that are truly capable, with the right level of governance and security.
2026
Paris - Montreal - Moscou
Choisir le régime adéquat d’usage de chacun de vos applications IA
“Une vraie valeur ajoutée, au-dela des consultants classiques, qui se limitent aux traditionnelles approches “process” et “organisation”. Les modèles semblent simples, mais ils permettent d’explorer assez profondément et précisément les enjeux prioritaires à traiter pour la durabilité de notre modèle d’affaires.”
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