MODEL
Internal catalysts
Catalysts are professionals within your company who become internal agents of AI transformation.
What a Catalyst is
- They accelerate transformation without being consumed in the process — like in chemistry, they remain after the reaction
- They discover use cases in their areas, coordinate knowledge across verticals and ensure the capability remains when MUTATIA leaves
- They enable changes that would not happen on their own and can be reused for multiple transformations
- Natural leaders with deep business knowledge and curiosity about AI — they don’t need to be technical
What a Catalyst is NOT
- They are not technical profiles hired from outside
- They are not consultants who leave and take knowledge with them
- They are not AI evangelists with no understanding of the business
- They are not an IT role isolated from the real business
Ideal profile
Professionals with deep business knowledge in their areas, plants or functions. Natural leaders with the ability to influence their teams. Curiosity about AI and transformation — they don’t need to be technical experts.
PHASES
The engagement process
Phase 0
Alignment
2 weeks
Validate the fit between MUTATIA and the organization. Understand the real context, key stakeholders and expectations. We don’t start without genuine alignment.
- Initial diagnostic session
- Stakeholder and expectations map
- Tailored engagement proposal
- Agreement on principles and working methods
Phase 1
Activation
4 weeks
Identify and train internal Catalysts — the change agents within the organization. Establish the AI committee and governance channels.
- Internal Catalysts identified and trained
- AI committee established
- Initial governance framework
- Communication channels set up
Phase 2
Discovery and prioritization
4 weeks
Exhaustive mapping of AI opportunities across the organization. Rigorous prioritization with explicit criteria: real impact, feasibility and strategic alignment.
- AI opportunity map
- Prioritization matrix with scoring
- Selection of 2–3 pilot use cases
- Business case for each pilot
Phase 3
POCs and governance
6 weeks
Execution of proofs of concept with strategic oversight. Hypothesis validation, results measurement and go/no-go decisions based on evidence, not enthusiasm.
- POCs executed with clear metrics
- Results report [FACT] / [HYPOTHESIS]
- Evidence-based scaling decision
- Refined governance model
Phase 4
Semantic layer and scaling
4 weeks
Building the semantic layer: shared language, taxonomy and decision frameworks that allow AI to scale coherently across the entire organization.
- Organizational AI taxonomy and glossary
- Decision framework for new use cases
- Scaling plan with milestones
- Autonomy test (client’s ability to continue independently)
GOVERNANCE
Three levels of decision-making
MUTATIA’s governance model distinguishes three levels of decision-making to avoid bottlenecks and maintain coherence.
Holding / Group
AI policies, ethical principles, security standards, strategic investments
Platform / Corporate
Shared infrastructure, approved vendors, cross-functional training
Company / Business Unit
Specific use cases, pilots, adaptation to local context
RADICAL HONESTY
Honesty labeling system
Every statement in our reports carries a radical honesty label.
[FACT]
Verified with data. We have seen or measured it.
[HYPOTHESIS]
We believe this is so, but we haven’t validated it yet. It needs testing.
[BENCHMARK]
Based on external reference. Applicable with nuances to the specific context.
SUCCESS METRIC
We don’t measure by hours billed. We measure by client autonomy.
Autonomy test: June 2026
The autonomy test evaluates whether the organization can make informed decisions about AI without depending on MUTATIA. If the client always needs us, we have failed.