AI Governance

In most companies, the adoption of artificial intelligence is outpacing their ability to govern it.

Tools such as Microsoft Copilot, ChatGPT, OpenAI, and Claude have become part of the daily work of teams and professionals, in many cases driven directly by business units and without a common corporate strategy.

This progress opens up significant opportunities for productivity, automation, and innovation, but it also raises an increasingly urgent question: How can we maintain control as the use of AI grows in a decentralized manner?

The more tools, users, and use cases that emerge, the more necessary it becomes to establish common criteria regarding security, data, costs, responsibilities, and regulatory compliance. The challenge is no longer simply to adopt artificial intelligence, but to create the conditions for using and scaling it in a controlled manner.

In this article, we explain how to build an AI governance model that enables the organized use of artificial intelligence, reduces risks, controls costs, and ensures safe, efficient , and sustainableadoption.

When different departments implement AI solutions independently , the organization can lose visibility into the tools it uses, the data it processes, the associated costs, and the risks it is taking on.

The challenge, therefore, is no longer merely to adopt artificial intelligence, but to establish a model for AI governance, use, adoption, and training that enables the management of current and future tools based on criteria of security, cost-effectiveness, regulatory compliance, and strategic alignment.

An effective AI governance model does not seek to impose a single platform or hinder initiatives that are already generating value.

Its goal is to establish clear ground rules for harnessing the potential of AI in a safe, efficient, and sustainable manner.

What Is AI Governance?

AI governance is the set of policies, responsibilities, criteria, processes, and oversight mechanisms that determine how an organization uses and manages artificial intelligence.

However, AI governance goes beyond establishing rules of use or ensuring regulatory compliance. It involves defining how the organization intends to engage with artificial intelligence: which uses it permits, which it restricts, what data can be used, who makes the decisions, and how new tools and use cases are incorporated.

An AI governance model must also establish criteria for selecting and coexisting with different AI solutions, controlling costs, managing risks, training users, and measuring the value generated.

Ultimately, the goal is to create a common framework that allows organizations to harness the potential of artificial intelligence without losing visibility or control as its use spreads throughout the organization.

I think this last sentence is important because it directly addresses the problem you explained just before: decentralized adoption.

The Starting Point: Decentralized AI Adoption

In most organizations, artificial intelligence has not been deployed according to a centralized plan. Its adoption has emerged organically, driven by the needs of different teams and, in many cases, without common control mechanisms.

As a result, heterogeneous AI ecosystems now coexist within companies.

One department might use Microsoft Copilot because it works primarily within Microsoft 365; another might have adopted ChatGPT for creative, technical, or automation processes; while other professionals turn to Claude to work with extensive documentation or perform analytical tasks.

This coexistence is not necessarily a problem. The problem arises when there are no common criteria for deciding which tool to use, with what data, for which use cases , and under what conditions.

Risks of AI Adoption Without a Common Framework

Simultaneous use of different artificial intelligence tools without corporate criteria for allocation.
Lack of a policy regarding what information can be processed using external AI.
Licenses, subscriptions, and token-based usage distributed across departments without a consolidated view.
Use of personal accounts for professional tasks.
Risk of exposure of sensitive or confidential information.
Differences in AI maturity and knowledge across departments and professional roles.
Lack of knowledge regarding regulatory obligations related to the business use of artificial intelligence systems.

These types of situations tend to arise naturally during the early stages of adoption. However, they also signal a particularly opportune moment to begin building an AI governance model.

The tools are already in use, so the organization isn’t starting from scratch. There are real-world cases to work with, users who can contribute their experience, and specific needs that allow for the design of policies and criteria much better aligned with the company’s reality.

The priority, therefore, is to bring that adoption under control before the number of tools, use cases, costs, and associated risks increase.

The Approach to an AI Governance Model

An AI governance model should not be designed exclusively from a technological perspective. The central focus is not on determining which is the best AI tool on the market.

The fundamental question is how the organization intends to engage with artificial intelligence.

This involves answering questions such as:

What uses are permitted?
What uses should be prohibited?
What types of data can be processed using AI?
Who can approve a new tool?
Who should intervene when a particularly sensitive use case arises?
How are expenses controlled?
How is the right tool selected for each need?
How is regulatory compliance ensured?
And how is the model reviewed as technology and the organization’s needs evolve?

To answer these questions, AI governance must be built on a set of principles.

1. Co-Creation Rather Than Imposition

Policies governing the use of artificial intelligence should not be designed in isolation and then imposed on the entire organization.

It is advisable to involve the various stakeholders who will be responsible for implementing the model: technology, legal, human resources, operations, business leaders, and senior management, among others.

Building the rules collaboratively allows for the incorporation of different perspectives, the anticipation of potential conflicts, and the reduction of resistance to the model’s implementation.

2. Pragmatism Over Idealism

A governance model must be grounded in the reality of the organization.

If different tools are currently in use, it does not always make sense to immediately require their consolidation into a single platform.

The initial goal should be to establish clear criteria for properly managing that diversity and then analyze whether opportunities for consolidation exist.

3. Proportionality to Risk

Not all AI use cases have the same implications.

Generating an internal draft based on non-confidential information does not pose the same risks as using artificial intelligence in processes involving employees, customers, or personal data.

Restrictions and controls must be tailored to the level of data confidentiality, the potential impact of the decision, and the risk associated with the use case.

4. Sustainability of the Change

The model must be capable of continuing to function once implemented.

To achieve this, it is necessary to designate internal stakeholders, establish review mechanisms, set up update processes, and identify individuals capable of promoting best practices across different departments.

5. Financial Control as a Cross-Cutting Principle

Governing artificial intelligence also means controlling its cost.

Per-user licenses, API usage, tokens, various subscriptions, and tools contracted directly by departments can quickly generate expenses that are difficult to identify and manage.

Cost control must be part of the model from the outset.

6. Proactive Regulatory Compliance

The governance framework must also incorporate the obligations arising from the EU AI Act and other related regulations, particularly the GDPR when personal data is processed.

Compliance should not be added to the model later on. It must be integrated from the design phase.

How to Implement an AI Governance Model

An AI governance methodology can be structured into four major sequential phases:

Diagnosis and Situation Analysis → Design of the Governance Model → Training and Adoption → Activation and Evolution.

Each phase addresses a different need and lays the groundwork for the next.

Phase 1: Diagnosis and Situation Assessment

The first step is to understand before prescribing.

Before defining policies or restricting tools, it is necessary to have a realistic view of how artificial intelligence is currently being used within the company.

The assessment should identify the tools currently contracted or in use, active accounts, user profiles, the types of information processed, and the main existing use cases.

Interviews with managers from different departments are particularly useful for identifying practices that rarely appear in technology inventories.

It may also be helpful to conduct a self-assessment of users’ level of competence and maturity regarding artificial intelligence.

The result should enable the creation of a map that includes:

AI tools in use;
departments and user profiles that use them;
main use cases;
types of data processed;
existing accounts and subscriptions;
current risks;
opportunities for value creation;
users’ level of maturity.

It is also necessary to identify active risks, such as the use of personal accounts, the processing of sensitive information using non-corporate tools, or a lack of awareness of certain providers’ privacy policies.

At the same time, existing use cases should be analyzed from a regulatory perspective and preliminarily classified according to the risk levels established by the EU AI Act.

The objective of this first phase is to obtain a clear snapshot of the starting point.

Phase 2: Designing the Governance Model

Once the current situation is understood, the core of the process begins: designing the rules that will govern the use of artificial intelligence.

This phase must cover several areas.

Corporate Policy on AI Use

The organization needs to establish clear guidelines that determine which uses are permitted, which require authorization, and which are prohibited.

The policy must also include criteria related to the classification of information and the types of data that can be processed using each type of tool.

But to be truly useful, it cannot be limited to generic statements.

It must address practical situations.

For example:

Can an internal document be entered into an AI tool?

Can customer information be analyzed?

Can artificial intelligence be used to analyze resumes?

Can code be generated using an external platform?

Can a meeting be automatically summarized?

Working through these types of dilemmas helps transform abstract principles into applicable rules.

Roles and Responsibilities

The model must also establish who makes the decisions.

It is necessary to define which responsibilities fall under IT, Legal, Human Resources, Finance, Security, department heads, or the potential AI Governance Committee.

There must also be a procedure for handling exceptions and situations not directly covered by the policy.

New Tool Approval Process

The rapid emergence of new artificial intelligence applications makes it necessary to have a clear evaluation process in place.

Before implementing a new solution, the following aspects—among others—should be analyzed:

its purpose;
the data it will process;
privacy conditions;
security;
the authentication system;
integration with the corporate environment;
its cost;
possible overlap with existing tools;
regulatory risks;
and the expected value.

The goal is not to unnecessarily slow down innovation, but to prevent each department from having to evaluate these factors independently.

How to Manage the Coexistence of Different AI Tools

One of the most important aspects of AI governance is establishing criteria for selecting and integrating tools.

An organization can work simultaneously with Microsoft Copilot, ChatGPT/OpenAI, Claude, or other solutions.

It is not essential to force premature consolidation.

Coexistence can be reasonable as long as there are criteria to determine when it is appropriate to use each platform.

Among the key factors that should be analyzed are the following.

Integration with the Corporate Ecosystem

Microsoft Copilot may offer advantages for organizations whose productivity environment is based on Microsoft 365, due to its integration with applications such as Word, Excel, Teams, or Outlook.

Other solutions may offer APIs, extensions, agents, or other integration mechanisms that make them better suited for different scenarios.

Type of Task

Not all tools offer exactly the same capabilities, nor are they necessarily intended for the same purposes.

Certain solutions may be particularly well-suited for office productivity, others for code generation and automation, and still others for analyzing extensive documentation, reasoning, or writing.

Data Confidentiality

One of the fundamental criteria must be the level of protection required for the information.

The organization must review each provider’s terms of service, retention policies, data processing agreements, privacy settings, and applicable safeguards before allowing the processing of sensitive information.

Cost Model

There are also significant economic differences.

Some tools operate primarily through fixed per-user licenses, while others may incorporate pay-as-you-go or token-based models.

This affects both budget predictability and the cost-effectiveness of using each solution depending on volume.

User Profile

A tool integrated into office applications may be suitable for certain business users, while an API-based platform may be more appropriate for technical teams.

The selection process should also take into account the level of knowledge and the needs of each user profile.

Corporate Access Control

Centralized authentication systems, single sign-on, and corporate identity management help control who has access to each tool and reduce risks associated with individual accounts.

Shadow AI Risk

Tools used via personal accounts or contracted directly by users and departments can increase the risk of shadow AI.

Therefore, the governance model must provide corporate alternatives that are sufficiently useful to prevent employees from continually seeking solutions outside the established framework.

Cost Control as Part of AI Governance

Cost is one of the elements most likely to be overlooked in an artificial intelligence governance project.

However, the combination of fixed-price licenses and usage-based models makes it necessary to establish financial transparency from the outset.

A control model should include several mechanisms.

Centralized License Inventory

All active subscriptions, accounts, and licenses should be centrally tracked and identified by tool and department.

This makes it possible to track actual spending and detect duplicates.

Usage Dashboard

The organization should have a regular overview of spending by tool, department, and, where possible, use case.

Alerts can also be set up when usage exceeds certain thresholds.

Spending Thresholds

For consumption-based services, monthly limits can be set by department, along with approval processes when these limits need to be exceeded.

Periodic ROI Review

Expenditures should be compared to the value obtained.

Indicators such as time saved, increased productivity, reduced incidents, or improvements in specific processes can be used.

Economic Evaluation of New Tools

A new platform should not be adopted solely because it offers attractive functionality.

Its cost-benefit ratio must be analyzed, and it must be verified whether a solution already exists within the corporate ecosystem capable of addressing the same need.

Policy on Personal Accounts

The use of personal accounts for business purposes creates security issues, but also hidden costs that make it difficult to determine the total expense.

Periodic Consolidation of Tools

The AI portfolio should be reviewed periodically to identify redundant applications, functional overlaps, and opportunities for consolidation or migration.

AI Governance and the EU AI Act

Corporate governance of artificial intelligence must also align with the European regulatory framework.

The European Artificial Intelligence Regulation, or EU AI Act , establishes different obligations depending on the level of risk associated with each system or use case.

This means that organizations must do more than simply take inventory of their tools.

They must also understand how these tools are being used.

Phase 3: Training and Adoption

A governance model will not be effective if the people using artificial intelligence do not understand how to apply it.

That is why training must be part of the project from the design phase onward.

The most useful training should not be limited to explaining what artificial intelligence is or how a generative model works.

It should focus on real-world scenarios within the organization itself.

Training Tailored to Each Profile

Not all professionals need the same training.

It is advisable to differentiate at least between:

executive management;
middle management and department heads;
operational users;
technical staff;
Legal, privacy, or security, as applicable.

Executives need to understand strategic implications, risks, and responsibilities.

Department heads need to know how to handle use cases and questions from their teams.

Users must be familiar with the rules of use, the types of information permitted, and where to turn when an unforeseen situation arises.

Technical staff require a deeper understanding of integration, security, APIs, and controls.

Training Based on Real-World Cases

The examples identified during the assessment should be turned into training material.

This allows for training based on situations that employees immediately recognize and prevents the training from being perceived as overly abstract.

Quick guides can also be developed for each role, explaining:

what is permitted, what the key best practices are, and what procedure to follow when in doubt.

Create an Internal Network of AI Ambassadors

A particularly useful way to ensure the model’s sustainability is to create a network of internal AI ambassadors.

These individuals can come from different departments and participate in building the model from the earliest stages.

They then act as multipliers.

They help disseminate best practices, identify new use cases, resolve questions within their teams, and detect needs that should be brought to the attention of those responsible for AI governance.

The goal is to ensure that the organization does not remain permanently dependent on an external team or a small group of specialists to keep the model alive.

Phase 4: Activation, Pilot, and Model Evolution

AI governance should not be rolled out directly across the entire organization without first testing how it works in practice.

A recommended approach is to start with a controlled pilot involving a representative department or group.

This pilot allows the organization to verify whether the rules are sufficiently clear, whether any friction arises, whether the approval process works, and whether users understand how to respond to different situations.

The results can be used to refine the model before its full implementation.

Defining AI Governance KPIs

The model also requires metrics.

Some particularly useful indicators may include:

Adoption rate. This shows what percentage of users or departments are using corporate AI tools.

Incidents of misuse. This helps detect issues related to data, unauthorized tools, or policy violations.

User satisfaction. This helps identify whether the rules and tools facilitate or hinder work.

Estimated time saved. This helps estimate the value generated by adopting artificial intelligence.

These metrics provide a more comprehensive view than simply measuring the number of licenses purchased.

Establishing a Review Mechanism

AI governance cannot be static.

Model capabilities change, new tools emerge, costs evolve, new use cases arise, and regulatory obligations are updated.

Therefore, the model must incorporate a periodic review process from the outset.

During the early stages of implementation, it may be advisable to conduct quarterly reviews to analyze:

changes in use cases;
new tools requested;
incidents;
expenses;
ROI;
regulatory changes;
adoption;
user feedback;
possible policy changes.

In this way, AI governance becomes a living model.

What Should an AI Governance Model Include?

At the end of the process, the organization should have a set of concrete assets that enable the implementation of AI governance in day-to-day operations.

These should include:

A Diagnostic Report

Including a map of tools in use, user profiles, key use cases, and identified risks and opportunities.

A Corporate Policy on AI Use

With clear guidelines on usage, information classification, permitted uses, restrictions, prohibitions, and approval procedures.

A Tool Selection Guide

To help determine which solution to use based on the type of task, the data being processed, the user profile, the level of risk, and the cost.

Ideally, this guide can take the form of a simple decision tree to help employees choose the right tool.

A Cost Control Framework

With usage metrics, department-specific thresholds, designated personnel, and mechanisms for periodic ROI reviews.

A Map of Roles and Responsibilities

That clearly establishes who makes decisions, who approves exceptions, and which areas should be involved depending on the type of use case.

A Process for Approving and Onboarding New Tools

To prevent each new solution from being implemented without a prior assessment of security, privacy, cost, and business need.

Training Materials

Tailored to different profiles and based on real-world scenarios within the organization.

An AI Ambassador Program

Featuring individuals capable of scaling the model and promoting responsible adoption across different departments.

An EU AI Act Compliance Assessment

Including a classification of use cases, an analysis of potential gaps, and a compliance plan with designated responsible parties and deadlines.

An Evolution Roadmap

The model must conclude with concrete next steps and scheduled reviews that will enable the development of AI governance over the coming months.

Preparing Data for Agent-Based AI: The Foundation for Trustworthy Agents

Agent-based AI represents a step beyond generative artificial intelligence. AI agents do more than just answer questions: they can interpret objectives, consult different sources of information, coordinate tasks, and execute actions within business processes. But this greater autonomy also makes data quality much more important.

An agent can only make good decisions if it works with reliable, up-to-date, accessible, and properly contextualized data. When information is fragmented, contains inconsistencies, or lacks a common definition, these problems do not disappear simply by incorporating artificial intelligence. On the contrary, they can carry directly over into the responses and actions executed by the agents.

Therefore, preparing an organization for agent-based AI involves much more than selecting a model or developing an agent. It requires a robust data architecture, integration mechanisms that connect different information sources, and governance policies that ensure data quality, traceability, security, and access control.

It is also essential to address the semantic layer. Concepts such as “active customer,” “margin,” “revenue,” or “profitability” must have a consistent meaning throughout the organization so that people, analytical systems, and AI agents interpret the information in the same way.

In this context, data preparation becomes one of the key factors in moving from isolated AI trials to systems capable of operating in an integrated manner with the business.

Discover how to prepare data for agent-based AI and which aspects of quality, governance, architecture, and semantics a company should address before deploying AI agents at scale.

AI Security: Protecting Data Is Also Part of the AI Strategy

The adoption of artificial intelligence in business opens up new possibilities for automating processes, improving productivity, and making better use ofcorporate information. However, it also introduces new risks related to security, privacy, and data control that must be addressed from the design phase.

The security of an AI solution does not depend solely on the model used. Other factors also play a role, such as where data is processed and stored, what permissions the system has, how it integrates with corporate applications, what information users can enter, and what traceability and monitoring mechanisms are in place.

This aspect becomes even more important with the expansion of generative AI and AI agents. As these systems access databases, documents, APIs, and enterprise applications, their ability to generate value increases—but so does the need to establish clear limits on what they can access, process, or execute.

Therefore, an enterprise AI strategy must incorporate security, data governance, and AI governancecriteria from the outset . Having controlled environments, access policies, information isolation, auditing, and clear usage guidelines helps reduce risks such as the accidental exposure of confidential information or the use of unauthorized AI tools.

If you’d like to learn more about the key risks, controls, and criteria that organizations should evaluate before deploying these technologies, check out our guide on security in enterprise artificial intelligence.

From AI Adoption to a Governed Usage Model

Many organizations have already moved past the stage of deciding whether or not to start using artificial intelligence.

The question now is how to manage an adoption that is already underway across different areas, each with varying levels of maturity, tools, and needs.

An AI governance model allows you to bring order to this scenario without having to halt it.

First, by understanding which tools and use cases actually exist.

Next, by establishing clear policies, responsibilities, and criteria.

Next, defining which tools are appropriate in each context, how data is protected, how spending is controlled, and how regulatory obligations are met.

And finally, by bringing the model to life through training, ambassadors, pilot programs, metrics, and review mechanisms.

The goal is not merely to control artificial intelligence.

It is to have a framework that allows the organization to use it safely, efficiently, and sustainably—and in alignment with its strategy—using both current tools and those that may emerge in the future.