For CIOs, CDOs, CTOs, and leaders with AI initiatives
AI Governance: From uncontrolled adoption to a governed, traceable, and compliant model
We design, implement, and operate the AI Governance model that turns risk into a controlled competitive advantage aligned with ISO/IEC 42001 and the EU AI Act.
Trusted by organizations already governing their AI adoption with auditable evidence
The problem isn’t AI; it’s the lack of control
AI is in your organization; control is not—yet
Shadow AI and Incomplete Inventory
Teams adopt AI tools on their own: Copilot, ChatGPT, code generators, proprietary models. The organization doesn’t know which AI systems it has, who uses them, for what purpose, or with what data. Without an inventory, governance is impossible.
Regulatory Pressure: AI Act + ISO/IEC 42001
The EU AI Act classifies AI systems by risk level and imposes obligations for documentation, impact assessments, and human oversight. Without a governance model, you cannot demonstrate compliance to regulators, auditors, or strategic clients.
AI That Doesn't Scale Without Control
Isolated AI projects, without decision traceability, measurable ROI, or coordination between business, technology, data, and security. AI grows as accumulated technical and regulatory debt that hinders scalability and trust.
The common pattern: organizations prioritize the speed of AI adoption over control. When an audit, regulatory requirement, or incident occurs, they lack the inventory, evidence, and identified stakeholders to respond.
Regulatory Risk, Shadow AI, and Decisions Without Traceability
Of organizations lack a complete inventory of their AI systems in production
– Gartner 2025, without inventory there is no governance possible
| 35M |
Maximum EU AI Act fines, |
| $1B |
Projected AI compliance spending by 2030 |
| +38 |
U.S. states with proposed or enacted AI laws |
Where does the real cost appear in your organization?
Regulatory non-compliance. Exposure to sanctions from the AI Act, ISO/IEC 42001, and sector-specific regulations without auditable evidence or risk classification.
Uncontrolled Shadow AI. AI tools adopted without oversight, usage policies, data control, or identified stakeholders.
Decisions without traceability. Systems that make or assist in critical decisions without logs, documented human oversight, or explainability mechanisms.
Loss of trust and ROI. Without governance, it is impossible to measure the value of AI, prioritize investments by risk/viability, or justify ROI to the business and executives.
Lack of identified stakeholders. Without a RACI matrix or governance committee, no one has the authority or obligation to respond when something fails.
AI that doesn’t scale without control. Isolated projects, lacking coordination between business, technology, data, and security, which accumulate technical and regulatory debt.
AI Governance: Diagnosis · Design · Implementation · Operation
E2E AI Governance: Not just policies, but real operation
The MTP approach combines governance, compliance, risk, quality, and real operation into a single service. We don’t stop at defining policies: we operationalize the model through processes, roles, controls, evidence, KPIs, committees, and continuous improvement.
Beyond Policies: Real Operational Governance
Native Regulatory Alignment by Design
AI Governance Office with Operational Capability
No Vendor Lock-in, Adapted to Your Real Maturity
What You Get with AI Governance
From Uncontrolled Adoption to a Governed and Auditable Model
Inventory and Classification of AI Systems
A complete catalog of AI systems, models, data, and providers, classified by risk level in accordance with the AI Act and ISO/IEC 42001.
Responsible Use Policies and Criteria
AI usage policies, guidelines by use case, approval processes with gates, responsible use criteria, and operational ethical principles.
Roles, RACI, and Committee Model
A complete RACI matrix, AI governance committee proposal, Head of AI Governance profiles, and definition of decision rights by risk level.
Auditable Evidence per Cycle
Control catalog, evidence logs per initiative, impact assessment documentation, and traceability of human oversight decisions.
Governance KPI Dashboards
Governance dashboard with indicators for maturity, coverage, compliance, incidents, deviations, and the value of AI initiatives per cycle.
Prioritized Implementation Roadmap
A roadmap prioritized by risk and value, a training and awareness plan, and a continuous improvement model for AI governance, sprint by sprint.
FROM DIAGNOSIS TO OPERATION: MTP’S 6 STEPS TO AI GOVERNANCE
E2E process from initial Quick Assessment to continuous model operation
Maturity and Risk Diagnosis
Initial inventory of AI initiatives, maturity analysis of current governance, identification of priority risks, regulatory gaps, and implementation roadmap. Deliverable in 5 days.
Inventory and Classification of AI Systems
Complete inventory of AI systems, models, data, providers, and components. Classification by risk level according to the AI Act (unacceptable, high, limited, minimal) and ISO/IEC 42001.
Operating Model Design
Responsible use policies and criteria, ideation/approval/monitoring processes, RACI matrix, AI governance committee proposal, control and evidence catalog, and governance KPIs.
Implementation and Deployment
Deployment of the operating model within the organization: training for key roles, activation of use case approval processes, dashboard configuration, and integration with GRC, MLOps, and CMDB.
AI PMO Activation
Launch of the AI Governance Office or AI PMO: first committee meeting, first review cycle of active use cases, activation of governance KPIs, and first executive report.
Operation and Continuous Improvement
Review of new use cases, risk classification, evidence updates, deviation management, periodic monitoring reports, and continuous improvement of the governance model sprint by sprint.
Use Cases
AI Governance Implemented in Real Environments
Financial Sector
AI Governance for 40+ tools implemented in 3 months with audit-ready evidence
- 40+ AI tools identified, inventoried, and classified by AI Act risk level
- AI Governance operating model deployed in 3 months with an active committee and defined RACI
- Auditable evidence catalog ready for regulatory inspection in the first cycle
- Shadow AI reduced by 70%: unauthorized tools identified and managed
“We knew we had more AI tools than we could control. MTP helped us know exactly how many, which ones were critical, who was responsible for each, and how to demonstrate it to the regulator.”
— Chief Risk Officer, European insurance company
Financial Sector
Alignment with AI Act and risk classification of critical systems with auditable evidence in 90 days
- Complete classification of 28 AI systems under AI Act criteria (high risk, limited, minimal)
- Centralized AI Governance model activated with operational AI PMO and monthly reporting
- Responsible AI use policies approved by management and integrated into the operating model
- First AI governance maturity report presented to the executive committee with actionable KPIs
The AI Act created a lot of uncertainty about which systems were high-risk and what evidence we needed. In 90 days, we had the inventory classified, the governance model implemented, and the committee running.
— CIO, regional financial institution
Verified Operational Scale
12 years of experience in the most demanding environments
Issues Detected
Automated Test Cases
Testing Hours
Test Cases Designed
Evaluation Stage
Everything You Need to Know About AI Governance
What is AI Governance and why do I need an external service?
AI Governance is the set of policies, processes, roles, controls, metrics, and evidence that allow for the adoption of AI in a controlled, traceable manner compliant with the AI Act and ISO/IEC 42001. Most organizations already using AI lack an inventory, risk classification, clear stakeholders, or auditable evidence. A specialized external service accelerates implementation with a proven model and avoids costly errors when an audit arrives.
What does the initial implementation consist of and how long does it take?
The initial implementation takes approximately 3 months across three phases: Quick Assessment for diagnosis and maturity (weeks 1-2); operating model design with policies, processes, RACI, committees, controls, and KPIs (weeks 2-8); and initial deployment with training, support, and AI PMO activation (weeks 8-12). The scope is adapted to the number of AI initiatives and the starting maturity level.
What inputs does MTP need to start the Quick Assessment?
The following are required: an initial inventory of AI use cases or initiatives (even if incomplete); descriptions of AI systems in use or planned; current policies and procedures; a map of involved roles and areas; technological architecture and relevant tools; existing risk and compliance criteria; and known incidents or concerns. It is not necessary to have everything complete.
What deliverables will my organization receive?
Diagnosis and maturity report; AI system inventory and classification; risk and impact map; target AI Governance model; usage policies and guidelines; approval and monitoring processes; RACI matrix; committee proposal; control and evidence catalog; prioritized roadmap; KPI dashboards; and, during operation, periodic monitoring and continuous improvement reports.
What is the EU AI Act and how does it affect my organization?
The EU AI Act is the first comprehensive regulatory framework for AI, in effect since 2024 with staggered deadlines through 2027. It classifies AI systems by risk level and imposes progressive obligations: technical documentation, impact assessments, human oversight, and conformity logs for high-risk systems. Fines can reach 7% of total worldwide annual turnover or €35 million.
What does MTP's AI Governance service NOT do?
It does not replace the client’s responsibility for their decisions, internal policies, or final regulatory compliance. It does not act as independent legal advice or formal certification unless expressly contracted with authorized third parties. It does not develop the client’s AI models nor does it replace cybersecurity, privacy, MLOps, technical QA, or internal audit, although it can integrate with all of them.
Does the service include continuous operation of the governance model?
Yes. The service has two modules: implementation (diagnosis, design, and deployment) and operation (continuous management through the AI Governance Office or AI PMO). In the operational phase, MTP can review new use cases, classify risks, issue recommendations, update evidence, address deviations, and generate periodic reports. SLAs are adapted to the volume of initiatives and the criticality of the environment.
What differentiates MTP from other AI consulting firms?
MTP combines governance, compliance, risk, quality, and real operation into a single service. It is not limited to defining policies: it operationalizes the model through processes, roles, controls, evidence, KPIs, committees, and continuous improvement. It incorporates experience in QA and technical validation of AI systems (TMMi Level 5, the first company in the world), technological independence, and the capacity to operate centralized, federated, or hybrid governance models using Gartner methodology.
Let’s talk about your AI Governance challenges
The AI Governance Quick Assessment identifies your priority risks, regulatory gaps, and the shortest path to an operating model compliant with the AI Act and ISO/IEC 42001.