Navigating the AI Landscape: What the Board of Directors must ask in 2025

INTRODUCTION

As the initial wave of the Artificial Intelligence (AI) hype begins to cool down, a clearer picture is emerging of the gap between sky-high expectations and grounded reality. Despite an estimated $1 trillion in AI investments, the practical adoption of AI remains limited. Only a small fraction of industrial companies is effectively deploying AI across their value chains. In this demanding context, the role of the Board of Directors (Boards) becomes critical.

Boards face a particularly challenging task in this phase of AI evolution. On one hand, they must ensure their management teams are pursuing the right opportunities and unlocking the undeniable benefits AI can bring to their organizations. On the other hand, they must safeguard their companies’ risk profiles, protecting sensitive information, intellectual property, and long-term sustainability.

However, most Boards lack the expertise to navigate this complex and rapidly evolving landscape. Few Boards have enlisted part-time advisors with the necessary expertise, and only a minority have dedicated AI (or technology) specialists as members. This leaves many Boards wondering: How can they fulfill their governance duties and effectively guide management teams when AI remains largely foreign territory?

This article aims to provide clarity and actionable insights for Boards. It outlines the most important questions Boards must ask in 2025 to ensure their organizations remain competitive, innovative, and secure for this AI-driven change. These recommendations are especially relevant for non-tech-native companies, such as those in industrial or service sectors, which collectively form the backbone of most economies. They are also particularly important for small and mid-cap companies, which often lack the resources and bandwidth to engage with relevant AI experts.

TEN KEY QUESTIONS FOR BOARDS IN 2025

While many AI-related topics fall squarely within the management team’s operational responsibilities, the strategic significance of AI demands the Board’s active involvement. As stewards of the company’s long-term success, the Board has a duty to make sure that AI is effectively integrated into the organization’s fabric — not just as a tool, but as a transformative enabler of growth and innovation. This requires a deeper level of engagement and oversight. To guide these efforts, here are ten critical questions every Board should address with their management teams, categorized in four major areas:

Strategy & Governance

1. How is AI embedded into the company’s long-term strategy for sustainable growth? (AI long-term strategy)

2. What short-term opportunities can AI create, and how well-defined is the company’s opportunity map? (AI short-term opportunities)

3. What are the key risks of AI models, and how is the company (and the Board) addressing them? (AI risk management)

Ecosystem & Integration

4. Is the company’s technology and data infrastructure ready to support AI initiatives? (AI integration with technology and data)

5. What impact will AI have on the company’s ESG commitments and overall footprint? (AI alignment with ESG commitments)

6. How will AI influence the company’s financial performance, both top and bottom line? (AI influence on financial performance)

People & Organization

7. What internal capabilities are the company building, and what is the partner ecosystem? (AI internal capabilities and partner ecosystem)

8. Is the company applying sufficient change management to ensure smooth AI adoption? (AI change management)

9. What training programs are in place to prepare employees for AI integration? (AI training programs)

Board Readiness

10. Is the Board itself prepared to utilize AI in governance and decision-making?(AI Integration in the Boardroom)

Graph 1: Overview of ten key focus areas for Boards in 2025

Each question will be explored in detail in the following section, with the aim of equipping Board members with a set of topics for a Board agenda topic on AI.

1. How is AI embedded into the company’s long-term strategy for sustainable growth?

There is no question that AI will be a cornerstone of every company’s strategy in the long run, shaping industries over the next 10 to 15 years. As one of the most transformative technological trends of our time, AI must be deeply embedded into long-range planning — both to unlock productivity gains and to drive top-line growth. However, this is no easy task. We are still in the initial stages of AI adoption, where the full potential and challenges of this technology remain uncharted.

To navigate this complexity, Boards should dedicate focused attention to AI during their annual strategy meetings. Consider carving out a significant block of time — 2 to 3 hours, and for larger corporations even a full day — to dive deep into the topic. The journey begins with understanding AI well enough to contextualize it within your specific industry, which is critical for both business executives and Board members to grasp its implications.

Inviting a guest speaker can provide valuable insights and fresh perspectives. For instance, you might bring in a technology expert from an AI leader like Nvidia, AWS, Google, or Microsoft, or a market analyst from research firms such as Gartner. These experts can offer a tailored view of how AI intersects with your industry, setting the stage for more informed discussions.

Once the groundwork is laid, the real value lies in scenario planning. Use a two-by-two matrix to explore four potential futures for your company. One axis might represent positive versus negative long-term business developments, while the other reflects positive versus negative impacts of AI. Each quadrant unveils a distinct scenario, sparking solid discussions with both the Board and management team.

The management team can then refine these insights, focusing on the most plausible scenario to align the company’s long-term strategy. This structured approach not only equips the Board with a clear understanding of AI’s opportunities and risks but also positions the organization to lead in a rapidly evolving landscape.

2. What short-term opportunities can AI create, and how well-defined is the company’s opportunity map?

Shifting the focus to the present and near future, most management teams by now should have identified key business opportunities where AI can make a tangible difference. If this has not happened yet, it is urgent for your management team to focus on it now. The competitive edge is at stake, as new market entrants can leverage AI to break down market entry barriers, lower costs, or introduce innovative AI-enabled products and services that disrupt the status quo. The time to act is now — delaying could mean falling behind.

As a Board, it is essential to evaluate the company’s initial approach into AI, particularly over the last two years since generative AI (GenAI) became widely available after the launch of ChatGPT. This phase likely involved pilot projects designed to explore AI’s potential without excessive investment but with a focus on tangible outcomes. These initiatives may have ranged from enterprise-wide solutions (such as deploying Microsoft’s Copilot or internal ChatGPT-like tools) to specific value chain applications (like a chatbot trained on internal knowledge bases).

The results of these use cases should be thoroughly documented. The Board should expect a comprehensive report that includes lessons learned, recommendations for the next steps, and a business case with a clear return on investment (RoI). This review will provide critical insights into the company’s early AI efforts and lay the foundation for future planning.

Looking ahead, the Board’s next task is to review the opportunity map for the next 2–3 years of AI adoption. This should focus on identifying business areas along the value chain with the greatest potential for AI-driven impact. It should also consider foundational elements — such as technology infrastructure, data strategy, and capability development — necessary to support these opportunities.

At this stage, it is recommended to approach the opportunity map independently of other capital allocation discussions. Mixing AI investments with broader business initiatives too early can detract from a focused exploration of AI’s transformative potential. By maintaining this initial separation, the Board and management can prioritize AI opportunities without being distracted by competing financial considerations, ensuring a clear and strategic path forward.

3. What are the key risks of AI models, and how is the company (and the Board) addressing them?

One of the core responsibilities of any Board is to manage enterprise risks, and GenAI introduces a new frontier in risk oversight. Typically, the management team presents the Board with an updated risk register and corresponding mitigation plans at least once per year. Whether GenAI-related risks appear in that register — and how prominently — will depend on the company’s risk appetite and its adoption of AI technologies.

The most commonly associated risks with GenAI include:

• Bias and Ethics Risks: High-profile lawsuits and investigations, such as cases of AI-driven hiring tools producing discriminatory outcomes, have highlighted the need for Boards to scrutinize AI use in HR, marketing, and other functions.
• Cybersecurity Risks: GenAI is increasingly leveraged in cyberattacks, including automated phishing schemes and deepfake frauds, making it a priority area for cybersecurity briefings.
• Transparency in Decision-Making: Black-box AI models that lack explainability can lead to decisions that are difficult to justify, potentially exposing the company to regulatory scrutiny and reputational damage.
• Compliance with Evolving Regulations: For example, failure to align AI systems with new laws like the EU AI Act could result in significant fines and operational disruptions.
• Data Privacy and Intellectual Property Risks: Breaches of sensitive data or misuse of proprietary algorithms can lead to legal liabilities, financial losses, and diminished stakeholder trust.

Given the many unknowns and uncertainties surrounding AI’s evolution, it is tempting to fall into a negative spiral when discussing AI risks at the Board level — especially when there is a lack of technology and AI expertise among its members. This is why it is critical to foster a balanced discussion. Boards with a more risk-oriented perspective should challenge an overly optimistic management team to develop a comprehensive AI risk strategy. Conversely, if the Board sees only opportunities, it should encourage management to articulate the associated risks and how they will be managed.

This balanced approach is useful as both opportunities and risks are actively addressed, positioning the company to harness the benefits of GenAI while safeguarding its resilience and reputation.

4. Is the company’s technology and data infrastructure ready to support AI initiatives?

As a Board member, and even more so for your management team, the influx of AI firms pitching software solutions with promises of transformative potential will be relentless. While many AI tools are indeed becoming more seamless and easier to deploy, their effectiveness depends on two critical factors: access to high-quality, readable data sources and a solid foundational technology platform.

A significant limitation of most GenAI solutions is that they are trained on publicly available datasets. To achieve tailored, business-specific outcomes, companies must train AI models on their proprietary data. However, this brings its own challenges. GenAI solutions are prone to “hallucinations” — generating inconsistent or even incorrect answers for the same query. Selecting the right GenAI model for a given task is therefore key, as is defining and curating the necessary data with precision.

This preparatory phase requires internal expertise. Business and technology subject matter experts should guide the training process, ensuring the AI’s output aligns with the company’s specific business context. Human oversight during this stage is critical to guarantee the accuracy and relevance of AI-generated insights.

Once internal data sources are identified, the next step is developing a supporting technology platform strategy. Key questions include: Should all internal data be consolidated in a central data warehouse before being used by GenAI models? Where will this data platform be hosted, and what security and compliance requirements must be addressed? Given the vast data volumes required for training GenAI models, the platform will likely involve a cloud-based solution, whether internal or external. Often, these platforms must integrate with external GenAI models, adding another layer of complexity.

While these considerations may seem operational, they have significant strategic implications. The Board’s primary role should be to ensure the company has a solid data and technology strategy for AI. This is essential, as these foundational elements often drive costs and can evolve into lengthy, complex projects, potentially delaying the business impact of AI initiatives. A proactive approach at this stage will help the company to avoid such pitfalls and maximize its AI investments effectively.

5. What impact will AI have on the company’s ESG commitments and overall footprint?

AI has the potential to significantly influence a company’s ESG (Environmental, Social, and Governance) commitments. While this impact may not always be immediately obvious, it is a critical consideration, particularly as the Board often plays a key role in overseeing ESG goals.

On the favorable side, AI can support data collection and predictive analytics, enabling companies to monitor and improve ESG performance with greater precision and scale. Business-specific AI applications can generate operational efficiencies, such as optimizing energy use and reducing waste, directly supporting environmental objectives. In terms of social equity goals, AI can help identify and mitigate biases in hiring processes, promote diverse talent, and improve workplace safety through predictive insights.

However, there are also challenges to consider. The high energy consumption of AI systems, particularly generative AI models like large language models, can conflict with environmental goals if not managed sustainably. These models require significantly more energy than traditional AI systems, a concern that has drawn substantial criticism. Additionally, AI models must be carefully managed to avoid perpetuating biases in decision-making, even in broader corporate governance contexts. Ethical considerations are critical, as unchecked AI applications can exacerbate social inequalities and create unintended consequences.

The Board’s role in overseeing both ESG and AI requires careful judgment and assessment. It may be worthwhile to consider a joint Board session on both ESG and AI implications all together. To navigate these intertwined areas effectively, Boards must have access to expertise — either within the Board itself or through external advisors. The long-term impact of AI on ESG goals, and vice versa, could significantly affect a company’s competitiveness and market position. Addressing these challenges proactively and strategically ensures that the Board creates a framework for leveraging AI in a way that aligns with the company’s ESG commitments and drives sustainable growth.

6. How will AI influence the company’s financial performance, both top and bottom line?

One of the most critical questions from a shareholder perspective is the financial impact of AI. As representatives elected by shareholders, Boards have a fiduciary duty to safeguard the financial interests of shareholders. While evaluating the financial performance of technology investments has always been challenging — and remains primarily an operational responsibility of management — AI demands a more strategic level of oversight. Boards should ask probing questions about how financial assessments of AI pilots, projects, and large-scale implementations are conducted. Key considerations include the cost methodologies employed, the measurement metrics for outcome success and failure, the percentage of capital expenditure allocated to AI, and the long-term financial estimates associated with these investments.

Beyond the direct investment case, Boards must focus on the return on investment (ROI) for AI, considering both top-line growth and bottom-line savings. AI, as a general-purpose technology, has the potential to impact every aspect of the P&L, but measuring these impacts can be complex and is often indirect. Boards should evaluate how management is quantifying the financial benefits of AI, such as identifying where the largest gains are expected and determining how these benefits are measured. For instance, productivity improvements might translate into cost savings through workforce adjustments or simply create time savings without immediate financial impact. It is essential for the Board to challenge management with these assumptions and provide clarity on how AI’s contributions to the P&L will be tracked and realized.

As the initial hype around AI decreases, the financial performance of AI investments will come under greater scrutiny, too. Boards should dedicate time to this subject, such as through the Finance and Audit Committee, to dive deeper into the financial implications of AI initiatives. This proactive approach will help prevent sunk-cost investments, a common pitfall when new technologies emerge. Maintaining rigorous oversight and focusing on tangible financial outcomes allows Boards to drive AI investments that deliver sustainable value for shareholders.

7. What internal capabilities are the company building, and what is the partner ecosystem?

There is no doubt that every company will need some degree of internal AI expertise in the long run. The critical question is how much of this expertise should be developed in-house and where the company should leverage the extensive external ecosystem of AI providers.

From a Board perspective, addressing this question requires clarity on two key factors:

1. The strategic importance of AI: Does the executive team and Board view AI as a strategic differentiator for the company’s future?
2. Industry-specific applications of AI: Is the company’s product or service offering highly specialized, or is it more commoditized?

If the answers to both questions are “high,” it is recommended to prioritize building internal AI capabilities. This could include elevating AI to a strategic position within the executive team, and potentially even introducing AI expertise to the Board. Conversely, if both answers are “low,” the company should lean towards a “buy” strategy, relying on external AI and technology partners to meet its needs.

Another important consideration is the type of AI expertise to internalize. Lessons from the early 2010s digitization wave revealed that overinvestment during the initial hype phase often led to later cutbacks during periods of financial constraint. The Board should assess that internal capability-building aligns with areas where early AI pilots have demonstrated clear business value. At a minimum, companies should develop in-house expertise in two foundational areas: cloud infrastructure and data platforms. These capabilities are essential for guiding internal operations and effectively managing external partnerships.

Regarding external partnerships, the Board should assess how management has structured the company’s external AI ecosystem. This ecosystem is likely to include:

• Core technology / cloud partners (e.g., Google, Amazon, Nvidia, or Microsoft).
• Data platform providers (e.g., Snowflake or Databricks).
• Delivery partners (often system integrators leveraging offshore capabilities, such as Accenture or Cognizant).
• Business-specific AI solution providers (e.g., startups or niche AI companies tailored to the company’s needs)

The Board should review this partner map to make sure it supports the company’s strategic goals. Particular attention should be paid to long-term partnerships, as they often come with significant financial commitments that can impact on the company’s flexibility and resources in the future.

By fostering a balanced mix of internal capabilities and external partnerships, the Board can help the company strike the right balance between agility and control, ensuring it is well-positioned to leverage AI’s transformative potential.

8. Is the company applying sufficient change management to ensure smooth AI adoption?

Lack of effective change management is often cited as the primary reason for unsuccessful business transformations. This is particularly true for transformations driven by technology improvements, which frequently come with high expectations — especially from senior management — that new technologies will eliminate inefficiencies and compel employees to adopt new ways of working. However, when employees expected to use these technologies fail to incorporate them into their workflows, the likelihood of rejection increases significantly. Effective change management, implemented from the outset, can help avoid such outcomes.

The importance of change management becomes even more pronounced with a transformative technology like AI. First, AI has entered the realm of general consumer tools through applications like ChatGPT, leading employees to expect similar seamless, intuitive experiences in the workplace. They will want AI tools that are easy to use and directly applicable to their day-to-day tasks. Second, AI is a general-purpose technology that spans multiple business process areas and can potentially impact on all knowledge work across a company. This broad applicability creates interest and demand to leverage AI across various business functions, making the orchestration of these efforts — and the associated change management -very important.

Change management must start with the company’s leadership, including the Board. The Board’s willingness to embrace AI, ask informed questions, seek education, and make AI a strategic priority sets the tone for the organization’s readiness to undertake an AI-driven transformation. A proactive Board not only empowers the management team to act but also demonstrates its own commitment to understanding and overseeing the change management efforts required for successful AI adoption.

Key questions for the Board to consider include:

• How is the management team planning to embed AI into the company’s culture and day-to-day operations?
• What is the strategy to equip employees with the skills and mindset to adopt AI-driven tools?
• How will the success of AI implementation and change management be measured, and what metrics will indicate progress?
• Is there a plan to address potential resistance to AI within the organization?
• How will the Board stay informed about ongoing AI-driven transformation efforts?

Finally, the Board’s role extends beyond oversight to actively fostering a culture of openness and adaptability. By leading with curiosity and setting an example, the Board can create an environment where employees feel supported in embracing AI as a key enabler of business success. In doing so, the Board helps pave the way for a transformation that is not just about implementing technology but about empowering people to unlock their full potential.

9. What training programs are in place to prepare employees for AI integration?

One of the critical factors for successfully implementing a general-purpose application like GenAI is providing sufficient, tailored training for employees. The key question for the Board to consider is whether the organization is delivering the right training to the right user groups for the right AI tools.

The first consideration is the right training. Employees need to understand how GenAI tools work and how they differ from traditional corporate applications, like search engines. Training should emphasize the importance of phrasing inputs effectively — commonly referred to as prompting or prompt engineering — as the quality of GenAI outputs depends heavily on the way questions are framed. Additionally, if the internal GenAI tools differ in functionality from tools employees use in their personal lives, these differences must be addressed to allow smooth adoption and effective use.

The second area focuses on the right user groups. Training should be tailored to meet the needs of specific groups within the organization. While a general introduction may be useful for all employees, specialized training might be required for those with technical roles or niche responsibilities. Senior management, including the Board, should also participate in targeted training to understand both the risks and opportunities of GenAI tools. This equips leadership to advocate effectively for change and to guide the organization’s transformation efforts.

The final consideration is the right AI tools. Employees need clarity on which AI tools are approved for use within the company, and which are restricted. Many organizations limit access to open AI tools like ChatGPT to protect internal knowledge and intellectual property. Training programs must include guidelines on company policies, ensuring employees understand what they can and cannot do when using AI tools. This alignment reduces risks and reinforces responsible use of technology.

By addressing these three fundamental areas — training, user groups, and tools — and integrating them with effective change management, the company can make sure its investments in AI are fully leveraged. While both training and change management can be difficult to measure in terms of immediate impact, they are critical success factors for realizing the potential of AI. The Board should evaluate whether the company is delivering well-designed AI training programs that allow employees to use these transformative tools to their fullest potential.

10. Is the Board itself prepared to utilize AI in governance and decision-making?

Boards should not overlook the opportunity AI presents to enhance their own operations and decision-making processes. Far too often, Boards are late adopters of new technologies, either because individual Board members are hesitant to embrace change or due to concerns about the confidentiality of Boardroom information and discussions. Fortunately, these barriers can be addressed by leveraging specialized Boardroom technologies, such as Sherpany, OnBoard, Diligent Boards and other secure platforms, which are increasingly embedding AI functionalities for safety and efficiency.

AI use cases in the Boardroom are diverse and transformative. They include, for example:

• Agenda Optimization: AI can analyze past meetings, Board materials, and stakeholder priorities to suggest structured agendas that maximize time utilization and allows focus on the most critical issues.
• Preparation and Insights: AI tools can summarize lengthy documents, highlight key trends, and flag potential risks or opportunities, enabling Board members to be better prepared for discussions.
• Decision Support: Predictive analytics, scenario planning, and benchmarking powered by AI can provide data-driven insights to support informed decision-making.
• Compliance Monitoring: AI can automatically identify compliance risks, regulatory updates, and inconsistencies in documentation, ensuring adherence to legal and ethical standards.
• Inclusive Collaboration: Virtual assistants and real-time language translation tools can support diverse, global Boards by fostering inclusivity and enabling smoother communication.
• Post-Meeting Follow-Up: AI can support writing up minutes, tracking action items, send reminders, and monitor progress against agreed goals, promoting accountability and alignment.
• Cybersecurity and Privacy: AI can improve meeting security by encrypting data, safeguarding virtual meeting environments, and preventing unauthorized access.

Boards should make 2025 the year AI becomes a standard feature in the Boardroom. To achieve this, appoint one Board member as a change advocate for AI adoption. Leverage the same AI applications that the company uses to improve its operations and apply them in the Board’s own processes. Experiment, pilot, and implement these technologies — mirroring the proactive innovation that Boards expect from the organizations they oversee. By embracing AI within the Boardroom, Boards can demonstrate leadership, improve efficiency, and set an example for the entire company.

CONCLUSION

The year 2025 is set to become a pivotal year for AI. As the initial excitement fades, many companies will confront the challenging reality of embedding a sophisticated yet immensely promising technology into their core operations. This transition will separate the companies that harness AI effectively from those that struggle to adapt. Board members will play a critical role in this evolution — not only by fostering their own personal understanding of AI but also by ensuring the organizations they oversee are well-prepared for the future.

The responsibility extends beyond governance; it requires Boards to function as stewards of innovation, guiding their companies through both the opportunities and challenges AI presents. This includes asking the right questions, demanding measurable outcomes, and setting the tone for thoughtful and strategic adoption. As Albert Einstein famously said, “In the middle of difficulty lies opportunity.” Boards that embrace this mindset will position their companies to thrive in the AI-driven landscape of tomorrow.

About the author:

Achim Plueckebaum is active on Boards and provides Board Advisory; he is also a CIO/CDO and a Business School Lecturer & Researcher

For advisory inquiries, support or feedback:

Email: achim.plueckebaum@gmail.com

LinkedIn: https://www.linkedin.com/in/achimplueckebaum/