A recent survey conducted by Deloitte and published in the Harvard Law School Forum on Corporate Governance revealed:
- Sixty-six percent of boards have limited or no knowledge of AI.
- Thirty-one percent of boards don’t have AI on their agenda.
- Forty percent of boards are rethinking board composition because of AI.
These findings underscore the urgent need for board-level fluency in AI.
For mid-market private companies, enterprise AI success requires commitment from the CEO and the board. How can the board effectively guide the CEO on this high-stakes journey?
Here are three lessons learned from the front lines as a practitioner in the past 20 years.
Board candidate pool. Expand the search to include people with senior executive experience in analytics and AI. Do not exclusively recruit people with nontechnical experience.
Value measurement. Include soft benefits, such as strategic value and culture influence. Do not exclusively focus on return on investment (ROI) only in financial terms and only in one to two years.
Appropriate level of engagement. Balance guidance and oversight. Do not stand by as an observer or interfere too much as an operator.
In this article, the definition of AI includes capabilities in data and analytics, machine learning (ML) models and rapidly growing new areas, such as natural language processing (NLP), video and image processing as well as generative AI (including large language models, agentic solutions for automation and multi-agentic systems).
Board Candidate Pool
Boards work for the owners of the company. The owner defines the vision, the board drives the strategic priorities and is responsible for oversight and guidance and the management team and the workforce are responsible for executing the strategies.
With the significantly increased impact and visibility of technology, data and analytics in today’s business operations, the board can more effectively guide the CEO and the management team on priorities and strategies when the directors understand the systemic approach and new developments in the changing world.
Proper alignment among four domains at the expertise level — technology, data, analytics/AI/ML and business operations — will accelerate AI adoption and value creation in a sustainable way.
Here is an example: A few years ago, a well-run family-owned company faced the challenges of shifting market dynamics and decreasing margins for the core business unit. This company had multibillion-dollar annual revenues in the transportation consumables distribution industry. The CEO and the board had intentionally recruited two new members for the board. The new directors each had extensive experience at other successful companies: one as chief analytics officer (CAO) for two decades and the other in a combined role as CFO and CIO. They started gently and gradually influencing the board’s agenda, providing guidance when the CEO initiated the enterprise-level data and analytics AI strategy to achieve two goals: growth and efficiency.
A few months later, I was hired as the first chief data and analytics officer to start the enterprise data/analytics/AI function. As the sponsor, the CEO led the business unit heads in this experiment, overcoming their hesitation. The board had quarterly meetings to review the AI program’s strategic progress, guiding and supporting the CEO along the journey.
Concrete results were achieved in two to three years. We achieved multiple times on ROI on a portfolio of several critical projects and created a clear road map to show a five-year break-even time frame for the internal start-up.
This shows how the board can help guide the CEO toward proactively leading the business strategy and driving the success of an enterprise data/analytics/AI journey.
Most CEOs and directors focus on business operations, finance and risk management, but tend to deprioritize the critical drivers for sustainable competitive advantage: data and analytics.
In the example above, if the board did not recruit the two independent directors, they would not have had the opportunity to influence and guide the CEO toward the accountability for starting the AI strategic initiative for the next three to five years and beyond.
Value Measurement
Enterprise AI success certainly requires technology, data and algorithms to support decision-making, but the more critical driving force is the human factor, including leadership, vision, culture and talent.
Once the framework and road map for an AI journey is understood, the next question becomes how one properly invests in AI strategic initiatives with a disciplined calculation of investment return.
AI value resides in two aspects:
- Internal use of data and analytics for efficiency gain.
- External commercialization of data and analytics for revenue generation.
Here are a few things to consider when creating measurements for value.
- Value in monetary terms, including front-line dollar value and current budget, expenses or investments.
- Efficiency gains, including time-saving, shifting to more productive use of human resources, less budget to sustain same volume or same budget to support much higher volume.
- Trendline and benchmark comparisons, including net promoter score and enterprise analytics maturity assessment score (conducted annually by qualified research vendors).
- Soft benefits, including an organizational culture that promotes collaboration, continuous learning, mutual appreciation and community support. This soft benefit functions as a sort of brand equity. With the enhanced organizational cultural brand, more talented professionals are attracted to the organization, lowering the recruiting cost and improving overall employee loyalty, engagement and productivity. Â
Like many innovative start-ups, an enterprise AI initiative needs committed sponsorship (with the board and CEO in the role of start-up investors) that believes in the strategic vision, including targeted market size, product fit, team talent and capabilities.
Internal start-ups take a few years to “storm, norm and form.” The board should guide the CEO to focus on the vision as well as financial return on investment beyond the first one to two years.
Appropriate Level of Engagement
The board generally practices the notion of “nose in, fingers out” to stay in its oversight and governance lane. On the enterprise AI journey, the board can most effectively guide the CEO by properly balancing “doing nothing at all” and “doing too much.”
Many boards take a “stand-by” approach — not showing interest, not demonstrating the appropriate level of understanding and not seeing the direct link between the long-term success of the company and internal AI capabilities. This approach risks the company missing the opportunity to transform itself into a sustainable business that thrives in a new environment.
Other boards may take a more hands-on approach. They could interfere too much by meeting with technical or functional leaders without the CEO’s knowledge or presence, digging too deep into details of projects and technical aspects.
The best way for the board to guide the CEO on the enterprise AI journey would be a balanced approach: Nose in, fingers out — frequently asking questions and always being available to guide or coach the management team.
The board can effectively guide the CEO to jointly drive the business’s AI strategy and governance, lead the shaping of corporate culture and ensure the company’s long-term sustainable growth and transformation.
Directors can directly improve the chance of long-term enterprise AI success if they cultivate the right mindset, drive to see opportunity over risk and properly mix growth, efficiency and innovation into their AI capabilities development.
The board can expand their candidate pool to include expertise in tech, data and analytics, create combined value measurements to guide investment decisions and stay engaged with the CEO along the journey.
Only a small percentage of companies will emerge as winners in the new AI capabilities development and value journey.