We’re witnessing a pivotal shift in how businesses operate, and it’s powered by generative AI. For C-level executives, this isn’t just another tech trend. It’s a moment that demands bold leadership and strategic thinking. AI is not only transforming internal operations but also reinventing how companies engage with customers and deliver value.

But here’s the kicker: You can’t lead tomorrow’s AI-powered enterprise with yesterday’s mindset. To stay ahead, executives must embrace the opportunities and proactively manage the risks AI introduces.

As AI use continues to grow, so does the responsibility for effective AI governance. Strong AI governance is essential not only to ensure compliance and avoid legal issues but also to build trust and credibility in the marketplace.

But, what is AI Governance (you might be thinking)?

If your organization develops or deploys AI tools, you need to introduce AI Governance. AI Governance refers to the policies, procedures, and ethical guidelines that oversee the artificial intelligence systems within an organization. Its primary goal is to ensure that AI technologies are utilized responsibly, ethically, and in compliance with legal standards. This involves establishing operational frameworks that promote transparency, accountability, and fairness.

To help guide this journey, the conversation can be broken down into two core areas:

 

1. What Every Executive Needs to Know About Generative AI

 

This section covers the essential concepts, risks, and compliance considerations surrounding generative AI—from potential organizational harm and legal exposure to the evolving regulatory landscape and the foundations of responsible AI governance. It’s designed to equip leadership with the strategic insight needed to make informed, forward-looking decisions.

 

2. What actions should you take as a C-level executive?

 

Here, we move from insight to action. This section outlines the practical steps C-level leaders can take today to embed AI governance in their organizations building the right teams, creating policies, monitoring compliance, and shaping a culture of accountability and innovation.

 

 

1. What Every Executive Needs to Know About Generative AI

 

1.1. AI Risks and Harms

 

Companies are competing to dominate the market, often leading to the launch of AI systems that may be flawed, irresponsible, or even harmful. Since humans design these AI models, our personal biases, values, and ethical perspectives inevitably shape their behavior. These influences can impact AI decision-making in ways that have serious consequences for individuals and society.

The harms associated with AI can be substantial and may affect:

1. individuals,

2. communities,

3. organizations, and

4. the environment.

Before implementing AI in an organization, C-level executives need to be aware of the potential negative impacts it may have.

In this course, we focus only on the organizational harms associated with AI adoption:

 

a. Penalties for non-compliance

 

AI systems must comply with existing laws and regulations. Failure to meet these legal obligations can result in regulatory fines, legal challenges, or even the suspension of AI applications.

The EU AI Act establishes a tiered penalty system for non-compliance, with fines of up to €35 million or 7% of a company’s global annual turnover, depending on the severity of the violation and the classification of the AI system (e.g., prohibited or high-risk uses).

However, the EU AI Act penalties are not the only applicable sanctions. Improper development or deployment of AI systems may also breach the General Data Protection Regulation (GDPR) – particularly when personal data is involved. In such cases, penalties can reach up to €20 million or 4% of global annual turnover, whichever is higher.

 

b. Damage to the organization’s reputation

 

The deployment of AI systems can result in significant damage to an organization’s reputation and credibility. This harm can lead to a loss of customers, a decline in the ability to attract new customers, an increase in customer concerns and inquiries, a drop in share value, or negative ESG (Environmental, Social, and Governance) ratings. Additionally, it could result in the departure of investors or stakeholders who no longer view the organization as reliable or responsible.

 

c. Damage to third parties resulting in economic damage to the organization

 

AI systems can unintentionally embed or amplify biases, which may lead to cultural or societal harm. For example, if an AI model reflects biases present in its training data, it may make decisions that disproportionately affect certain social or cultural groups, reinforcing stereotypes or unfair practices within the operation of your organization.

This can harm both internal workplace culture and broader societal trust in both AI technologies and companies using or developing them. This includes the costs of litigation, potential class actions, or punitive damages resulting from AI system failures or breaches. Organizations may also face the financial burden of remediating the harm caused by AI, such as reimbursing customers, providing compensation, or implementing corrective actions internally to fix errors or prevent future problems.

These risks are far from theoretical. Real-world examples such as Amazon’s biased recruiting tool, the Workday class-action lawsuit alleging age discrimination, and the 2023 settlement between the U.S. EEOC and iTutorGroup over AI-driven hiring bias demonstrate how flawed AI systems can lead to legal consequences, reputational damage, and substantial financial liability.

 

d. Damage to the organization’s IP and Confidential information

 

Damage to the organization’s IP and confidential information occurs when AI tools, especially third-party or cloud-based systems, inadvertently expose sensitive internal data such as proprietary algorithms, source code, or strategic documents through improper use, weak safeguards, or integration flaws. For example, in 2023, Samsung employees unintentionally leaked confidential source code and internal data by inputting it into ChatGPT, raising serious concerns about corporate data security and IP protection.

 

1.2. AI Governance: How to Tailor It for Your Business

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Unfortunately, there is no “one-size-fits-all” approach to building an AI governance framework. Effective AI governance starts with a clear understanding of:

1. The size and structure of your organization
2. The organization’s AI portfolio: how AI is currently being developed or implemented
3. Whether the datasets used contain personal data or sensitive information related to the organization or third parties such as client
4. The industry your organization operates in and the level of regulation within that industry
5. The leadership’s tolerance for risk
6. Jurisdictions in which the company operates (further discussed in “AI legislative framework”).

 

For smaller organizations, AI governance responsibilities may need to be assigned to existing roles or departments. This could involve expanding the scope of current teams in areas like legal, compliance, or privacy to include AI oversight.

Larger organizations may have to choose to develop dedicated structures for AI governance. This can include creating specific offices or committees focused on AI and establishing formal processes to oversee generative AI models.

Organizations in heavily regulated industries such as healthcare, insurance, and banking will have to align their use of AI with existing compliance requirements. In many regions, including the United States, these organizations receive direction from regulatory bodies on how to manage risks specific to AI. This guidance continues to play a key role in shaping how different sectors develop their AI governance frameworks.

Whether it’s in a B2B or B2C setting, both existing services that include AI features and brand-new products built around AI need thoughtful risk assessment and regular oversight. The level of attention should match how complex AI is and how much impact it could have.

So, where do you start?

At Whisperly, we divide the AI governance implementation process into four foundational pillars:

• Establishing procedures and policies – including the development of internal rules and supporting documentation,
• Creating an AI register – a centralized inventory of all AI systems in use,
• Defining the procurement and contracting process for AI solutions, and
• Monitoring and ongoing oversight to ensure compliance and performance management.

 

1.3. Understanding the role of your organization in AI’s lifecycle

 

From a governance perspective, the roles involved in the development, deployment, and use of AI have different responsibilities. Key roles in the AI life cycle include:

• Developers (or “providers” in some regulations) are those who create or build the AI system.
• Deployers are entities that put the AI system into use and manage it.
• Users are people or organizations that interact with or are affected by the AI system.

You may find a full list of roles with the explanation under the EU AI Act here.

A developer can also be a deployer, and multiple entities can serve as developers and deployers throughout an AI’s lifecycle. The organization’s role in AI governance will depend on this.

Therefore, your process starts with the following questions:

a. Are you developing an AI system?​

b. Are you using the AI system someone else provides?​ If yes, then:

• • How thoroughly has your team checked the risks linked to this AI system?
  • What steps has the AI system provider taken to ensure the program follows relevant rules and regulations?

 

Determine the role under the EU AI Act with our AI Act Checker.

 

1.4. AI legislative framework

 

Apart from the specific legislation applicable to AI (such as the EU AI Act), it’s important to remember that all existing laws for a sector or jurisdiction continue to apply when using AI. This includes laws related to employment, housing, health, privacy, product safety, and anti-discrimination. This is particularly crucial for regulated industries like finance, transportation, and human resources.

You might be wondering, how do you ensure AI compliance if your organization operates in multiple jurisdictions?

To comply, you must develop a strategy based on the strictest requirements from the applicable regulations and integrate them into a unified compliance framework.

 

a. EU AI Act

 

As a C-level executive, it’s crucial to stay informed about the regulations that affect the use of AI, including emerging laws. A key example is the EU AI Act, the world’s first comprehensive regulation governing artificial intelligence. Similar to the EU General Data Protection Regulation’s (GDPR) impact on the processing of personal data worldwide, the EU AI Act is expected to have a global impact.

Notably, this regulation applies not only to companies with a corporate presence in the EU but also takes an extraterritorial approach, meaning it applies to any AI system placed on the EU market or used within the EU, regardless of where the provider or deployer is located. For example, a U.S.-based company offering AI-driven services to EU clients must ensure compliance with the EU AI Act.

Also, an organization might rely on services from a provider based in the European Union. Even if the organization is not directly covered by the EU AI Act because it operates outside the European Union, there may still be obligations such as information sharing or meeting certain requirements built into standard business practices, contracts, or service agreements.

The EU AI Act introduces upcoming deadlines and sets out key obligations for the development and use of AI systems, including generative AI. It places responsibility on users to understand how these systems work and their potential impacts, such as bias or errors. Users may be held liable for outcomes, especially when risk management measures are not followed. A major focus is on identifying and managing high-risk use cases, including those related to employment.

Although the EU AI Act’s prohibition of certain high-risk systems has already started with the application in February 2025, most of the EU AI Act obligations to enterprises will start with the application on August 2, 2025, and August 2, 2026.

 

b. ISO standards

 

ISO standards play a key role in AI governance by providing internationally recognized frameworks for managing risk, ensuring quality, and promoting transparency in the development and use of AI systems. Adopting these standards helps organizations align with best practices, support regulatory compliance, and build trust with users and stakeholders.

Currently, the following ISO standards are applicable to AI governance:

1. ISO 22989: Defines terminology and key concepts related to AI.
2. ISO 42001: Provides guidance on managing AI systems responsibly and effectively.

You can choose to have your company certified under these ISO standards. Certification involves an assessment process by an accredited third-party organization to verify that your company meets the specific requirements outlined in the standards.

There are software solutions that can prepare your organization for ISO certification.

 

1.5. How do current laws apply to AI systems?

 

a. Intellectual Property

 

As AI continues to develop, it often outpaces existing legal frameworks, leaving intellectual property laws struggling to adapt to the new realities it creates.

There is significant uncertainty around the data scraping and collection practices used to train generative AI systems. Important legal questions remain unanswered. For example, if AI cannot be recognized as an inventor under current patent laws, how much human involvement is needed for an invention to qualify for patent protection? Similarly, it is still unclear when the use of certain data for training purposes requires a license and when it might be allowed under fair use exceptions. When an AI system creates a piece of content, it is not clear who holds the copyright — the person who built the system or the one who used it.

C-level executives must work closely with their legal departments to understand how intellectual property laws affect their use of AI. They can help assess how existing laws apply and whether those laws might be challenged or interpreted differently in the context of AI.

 

b. AI and Personal Data Protection

 

AI and data protection laws often overlap because AI systems rely on large amounts of personal data to function effectively, making compliance with data protection regulations essential. Laws like the GDPR and CCPA impose strict rules on how personal data can be collected, processed, and stored, which directly impacts how AI systems are developed and deployed.

Key requirements such as obtaining explicit consent, ensuring transparency in data usage, minimizing data collection, and safeguarding privacy by design are crucial for organizations to follow when using AI (read our blog about the data privacy requirements when developing and deploying AI). Failure to comply with these data protection laws can lead to legal penalties, as AI systems that handle personal data must align with these principles to protect individuals’ privacy and rights.

 

c. AI in Services Contracts with Your Clients

 

As AI becomes more integrated into business operations and decision-making, companies must rethink how contracts address the risks tied to its use. This is especially important when offering AI-powered services to clients.

In business relationships involving AI, contracts play a key role in managing liability and clearly defining each party’s rights and responsibilities. For example, if an AI tool provides flawed recommendations or misclassifies data, legal responsibility becomes a central issue.

• The client company will typically try to shift liability to the vendor through warranties or indemnity clauses.
• Meanwhile, the vendor will aim to limit their responsibility for outcomes that result from the unpredictable nature of AI systems.

Every C-level executive must make sure their legal team is involved in the negotiation so that the proper contractual safeguards are in place.

 

d. Product Liability and AI

 

Several jurisdictions are beginning to adapt their product safety regulations to cover AI technologies. How these rules apply to AI systems varies depending on the region. In the EU, the following legislation might be applicable:

• The Product Liability Directive allows people harmed by software (including AI software) to receive compensation from the software manufacturer (replacing Directive 85/374/EEC)This updated directive explicitly includes software, including AI systems, within its scope, allowing individuals harmed by such technologies to seek compensation from manufacturers under a strict liability regime. The directive came into force on 9 December 2024. EU Member States are required to transpose it into their national laws by 9 December 2026. Until then, the previous directive remains applicable to products already on the market
• The General Product Safety Regulation 2023/988/EU, which applies from December 13, 2024.

 

 

e. AI, Employment Law, and Non-discrimination

 

As the use of AI becomes more common in the workplace, it is being applied in various areas related to employee monitoring and management, including:

• Biometric identification or video surveillance
• Tracking working hours and attendance through monitoring software such as tools that take screenshots or track keyboard activity
• Analyzing employees’ emotional states
• Monitoring electronic communications and remote work activities
• Recording conversations
• GPS tracking for company vehicle usage
• Screening and evaluating candidates during the hiring process.

 

Organizations using AI in these ways must ensure full compliance with employment laws and must ensure they do not unfairly disadvantage any group of applicants. Under the EU AI Act, many of these workplace applications are considered either high-risk or not allowed, especially when they involve monitoring behavior or making decisions that affect people’s careers. Employers need to assess the legal impact of these tools and make sure their use is necessary, and justified and respects employee rights.

 

2. Actions C-Level Executives Can Take Today

 

2.1. Build Your AI Governance Team

 

In this section, we look at who should be part of the team that leads AI governance in your organization.

The successful implementation of Artificial Intelligence (AI) within an organization goes far beyond the development and deployment of sophisticated algorithms or systems. It requires a collaborative approach that involves multiple stakeholders across different functions. Each stakeholder brings a unique perspective and expertise, ensuring that AI is not only technically sound but also ethically responsible, legally compliant, secure, and aligned with the organization’s strategic objectives.

AI systems can significantly impact various aspects of the business, from enhancing operational efficiency to transforming customer experiences. However, without proper governance, oversight, and a comprehensive understanding of the potential risks and opportunities, AI implementation can lead to unintended consequences, such as security breaches, biased decision-making, and non-compliance with regulations. Therefore, bringing together a diverse team is essential to ensure that AI is deployed responsibly, securely, and in a manner that benefits the organization as a whole.

 

Role	Competence
Head of IT	Ensures the technical infrastructure supports AI, including data storage, processing power, and integration with existing systems. Responsible for technical feasibility and system performance.
AI Governance Officer	Drives responsible AI use by defining policies, frameworks, and ethical standards. Monitors AI risks, fairness, and transparency, ensuring alignment with company values and strategy.
HR Manager	Manages the workforce impact of AI, ensuring ethical use in people-related decisions, facilitating reskilling, and leading employee adoption of AI tools.
Legal Counsel	Oversees compliance with laws and regulations (e.g., GDPR, AI Act), ensuring contracts are well-structured and managing legal risks associated with AI, such as liability and IP.
Chief Information Security Officer (CISO)	Protects AI models and data from security risks, ensuring secure deployments, access controls, auditing, and resilience against cyber threats.
Chief Data Officer (CDO) / Data Protection Officer (DPO)	Manages data quality, governance, and privacy, ensuring data is ethical, compliant, and trusted. Supports data-driven decision-making and mitigates data-related risks.
Chief Compliance Officer	A Chief Compliance Officer (CCO) in the context of AI adoption is responsible for ensuring that an organization’s AI systems comply with relevant laws, regulations, and ethical standards.

 

 

2.2. Enact AI Governance Procedures and Policies

 

a. Enact a General AI Policy

 

The first step before developing, deploying, or using any AI system should be drafting an AI policy that outlines the rights and responsibilities regarding artificial intelligence within an organization, focusing on employees and responsibilities. This policy ensures ethical, responsible AI usage, protects stakeholders, and aligns with legal frameworks.

 

b. Create an AI Inventory and Establish AI Project Approval Criteria

 

Your organization must create an AI inventory, which involves documenting all AI applications, systems, and tools used within an organization. By creating this inventory, organizations can track all AI systems in use, assess their risks, and ensure that they are properly managed and aligned with governance policies.

Establishing AI project approval criteria involves defining a set of guidelines and requirements that AI initiatives must meet before being approved for development and deployment.

 

c. Enact Other AI-related Policies

 

To ensure responsible and compliant use of AI systems, organizations should create specific documents based on the classification, use cases, and impact of those AI systems.

Read here how our AI Governance software can help.

 

2.3. Train and Educate

 

Companies should provide training programs to enhance employees’ understanding of AI technologies. This includes explaining basic AI concepts, the implications of AI on their work, how AI decisions are made, and the risks associated with AI use (e.g., bias, privacy issues).

Senior leaders and decision-makers must have a solid understanding of AI’s capabilities and risks to make informed strategic decisions. This includes understanding AI’s potential business value but also its ethical, legal, and societal implications.

 

2.4. Maintain and Monitor Compliance

 

You can implement AI governance and maintain compliance manually in smaller or early-stage organizations. However, as you scale or face more regulatory pressure (like the EU AI Act), governance software becomes a valuable tool for consistency, efficiency, and accountability.

AI governance software automates risk scoring, impact assessments, and monitoring while also creating audit trails and centralized documentation hubs. It provides dashboards for transparency and oversight, sends alerts for non-compliance or high-risk AI systems, and enables continuous monitoring rather than relying solely on one-time reviews. Software solutions also enable easier certification with ISO standards.

Read here how our AI Governance software can help. Whisperly helps you monitor, manage, and stay compliant with evolving AI regulations – effortlessly.