Board Actions in the Age of AI

Artificial intelligence (including its numerous branches, from machine learning models to عامل‌های هوش مصنوعی) is far more than just a new wave of technology. It is a general capability poised to touch nearly every sector, function, and role, with the power to transform how companies compete, operate, and grow. With trillions of dollars at stake and consequences that could be vital for companies, the use of AI on the board is a priority.

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More than 88% of organizations have reported using AI in at least one business function; however, board governance has not kept pace.

While interest in AI surged after the introduction of ChatGPT, by 2024 only 39% of Fortune 100 companies disclosed any board oversight of AI (whether through a committee, a AI-savvy director, or an ethics board.) Even more importantly, a global survey of executives found that 66% reported their boards had «limited knowledge or experience» in AI, and nearly one-third said AI didn't even appear on their agendas.

Enhancing Board Governance Through AI Approach and Models

The low rate ofAI adoption by boards might seem obvious at first glance, given the significant investments many companies have already made in AI and the limited returns so far. AI adoption has not yet led to significant performance improvements for most businesses , and companies have reported moderate levels of savings and new revenue. However, many of the problems plaguing AI initiatives (such as a lack of strategic coherence and unclear value dynamics) are precisely those that boards are best positioned to address.

The Role of Boards of Directors in the Age of Artificial Intelligence

Boards must develop a strong understanding of how AI can business transform (both positively and negatively). Therefore, boards must become proficient in Artificial intelligence , not necessarily as a technology, but as a catalyst impacting the competitive dynamics of their sector.

A 2025 MIT study found that organizations with digital and AI-savvy boards have 10.9% higher returns than their peers in return on equity. In contrast, companies lacking such a board are 3.8% below their industry average.

Two Key Priorities for Boards in the Age of AI

1. Defining the company's approach to AI adoptionMany organizations still lack a clear view of how AI fits into their strategy or transformation agenda. Without alignment between the board and management, oversight becomes superficial or paralyzing.
2. Tailoring the governance model to the company's AI approach: The board's task is to calibrate its role in areas of engagement, oversight, and speed of interaction.

Defining the business's AI posture

A business's AI posture clarifies how AI fits into that company's strategic ambitions and priorities. Not all companies will, or should, have the same approach to AI. But having clarity on AI's impact on the businessprovides a foundation for board members to make key strategic, governance, and investment decisions. to make key strategic, governance, and investment decisions.

Two strategic dimensions determine a company's approach to AI, and where companies sit on the spectrum of each of these dimensions defines their posture:

• Source of Value: Does AI help the company move beyond its core business model toward new products, experiences, and revenue streams (strategic expansion), or does its value primarily come from improving the existing model (internal optimization)?
• Extent of Adoption: Will AI be embedded across the entire organization (pervasive) or deployed in targeted use cases (selective)?

What is important is that the board aligns itself with the business's aspirational strategy, using a clear view of opportunities and risks, so it can tailor the governance approach accordingly. As the business's experience with AI grows, the board can shift its stance.

Four AI Approach Archetypes

Business Pioneers

AI is at the heart of the strategy, driving new offerings and redefining competition. Think of a medical device company that can evolve from selling equipment to providing AI systems that interpret scans and suggest appropriate treatments, thereby transforming from a manufacturer to a healthcare solutions provider.

Internal Transformers

AI becomes the backbone of operations, changing how a company functions. An example of this archetype is a mining company that uses AI to guide exploration, automate extraction, and optimize refining – thereby transforming a labor- and asset-intensive model into a data-driven one. Similarly, a media studio could embed AI across its production pipeline, generating faster, cheaper content at scale.

Functional Recreators

AI is used to improve specific workflows with proven returns. Companies treat AI as a regular, ROI-driven investment, not a transformative lever. For example, a healthcare system might deploy various AI scheduling, transcription, and workforce tools. Or a logistics provider could use route optimization and predictive maintenance to reduce costs.

Pragmatic Adopters

AI is deployed for targeted applications based on already proven market traction. This is essentially a fast-follower approach. For example, a consumer goods company might wait for off-the-shelf e-commerce recommendation tools to be proven in the market before adopting them to expand into new segments. Similarly, a fashion retailer might only begin using AI for offering clothing rentals and personalized styling after others in the industry have demonstrated its effectiveness.

6 Key Actions for Boards in the Age of AI

Research highlights 6 actions that Boards in the age of AI should put on their agenda, and the extent of their follow-up depends on the situation هوش مصنوعی در سازمان varies:

Aligning on an AI approach and annual review

The first agenda item is aligning with the stance the business should take on AI – without this clarity, none of the other actions matter. The board should then regularly review its AI stance in response to changes in competitive, regulatory, and technological environments. Proactive status reviews ensure that the company's position reflects today's realities, not yesterday's assumptions. This annual review should not replace frequent engagement on the topic (see action 4).

Defining AI oversight ownership across the board and management

Oversight without clear accountability fails. The board must explicitly define which topics should be reviewed and fully discussed in full board meetings (e.g., material investments to scale AI enterprise-wide), which topics belong to committees (e.g., risk frameworks and material vendor reviews), and which do not require significant board discussion (e.g., regular operational decisions). Without this clarity, ambiguity arises, and accountability is lost, or valuable agenda time is wasted.

Developing an AI governance policy framework

Most companies develop principles or ethical statements, but fewer than 25% of companies have structured, board-approved AI policies. A robust corporate governance framework should specify:

•  Scaling rules (when pilot projects receive funding for enterprise-wide scaling)
• Risk thresholds (what requires human approval and what safeguards should be in place)
• Vendor or data safeguards (intellectual property protections, third-party audit rights, security, and provenance standards)
• Escalation factors (what incidents reach the board and at what speed)

Broader and more frequent engagement with AI implementers

It is not enough to only engage with CEOs or CFOs about AI developments in the business. Board members should regularly be exposed to and directly interact with executives who are deploying AI in operations (e.g., chief data and analytics officers and business and functional leaders) to gain a deeper understanding of progress toward goals and its impact on competitive dynamics.

Linking AI investment to business value

to make key strategic, governance, and investment decisions. should encourage management to not only identify but also quantify the potential opportunities and risks associated with AI adoption. This perspective can help to make key strategic, governance, and investment decisions. guide businesses through short- and long-term trade-offs that balance opportunity and risk by leveraging their AI position. Effectively providing this guidance requires accurate reporting, but currently, only about 15% of boards receive AI-related metrics.

to make key strategic, governance, and investment decisions. should focus on impact metrics such as Return on Investment (ROI) By business unit, the percentage of processes where AI is active, resilience indicators (such as cancellation rates and backup exercise results), progress in workforce reskilling, and alignment with regulations. This information helps in reorienting and overseeing AI strategy in a manner similar to capital allocation and risk review.

Building AI literacy among board members

Directors do not need to be data scientists or deep technology experts, but they must have a sufficient understanding of how AI works and its role in creating opportunities and risks for business them. Building this knowledge base occurs through continuous education, regular briefings, external training, advisory panels, and receiving insights from external experts on emerging technologies, regulations, and risks. Board members They should feel comfortable using AI in their personal lives to prepare for meetings, review public information, and perform analysis on proprietary information, only in ways approved by general counsel.

جمع بندی

This article emphasizes that AI-related laws, risks, and expectations are rapidly evolving, and boards cannot assume that today's practices are sufficient to address new challenges and opportunities. boards must evolve with the speed and scope of AI-driven changes, while maintaining their traditional focus on providing strategic direction and overseeing senior management to create value and mitigate risk.