Enterprise AI success requires commitment from the CEO and the board of directors. This statement sounds simple and easy. However, it is generally not at the top of mind for CEOs and the board.

I know this from my observation as a CDAO in the past two decades, when I frequently interacted with and presented to CEOs and board members at three private companies, and reported to the CEO directly, dotted line, or indirectly.

In this article, my definition of AI includes capabilities in traditional Data and Analytics, Machine Learning Models, and the rapidly growing new areas such as natural language processing (NLP), video and image processing, and generative AI (GenAI, including large language models and multi-agentic systems).

I see the following 3 patterns of how the CEO and the Board of Directors can effectively drive enterprise AI success:

• Through leading the business strategy

• Through influencing the corporate culture

• Through proactively bending the organizational life cycle upward

1. Business strategy

A few years ago, a well-run family-owned company with multi-billion dollar revenue faced challenges of shifting market dynamics and decreasing margin for the core business unit.

The CEO and the board of directors recruited two new members for the board. Both have had extensive experiences at other successful companies — one as Chief Analytics Officer (CAO) for two decades and the other as a combined role of Chief Financial Officer (CFO) and Chief Information Officer (CIO). The two new members started to influence the board’s agenda and provided guidance when the CEO initiated the enterprise-level data and analytics AI strategy.

A few months later, I was hired as the first CDAO to start the enterprise  Data/Analytics/AI function.

The result: In three years, we demonstrated multiple times return on investment (ROI) on a portfolio of several critical projects and created a clear roadmap to show cash-positive status for the internal startup in four to five years.

This is an example of how the CEO and the board can proactively lead the business strategy to drive the success of the enterprise data/analytics/AI journey.

Enterprise AI success certainly requires technology and data, but the more critical driving force is the human factor.

Most CEOs and board members focus on business operations, finance, and risk management, but tend to deprioritize the critical driver for sustainable competitive advantage: Data, analytics, and AI.

The board of directors represents shareholders and makes capital expenditure decisions based on ROI for three to five years or a longer time horizon. Unfortunately, in practice, the timeframe for ROI tends to be 1-2 years.

A sustainable business growth strategy requires the board and CEO to have a mindset of long-term strategic and systemic thinking. With the increased awareness of AI’s impact, many CEOs and board members are interested in learning more about AI and investing in AI.

Proper alignment among the four domains of expertise — technology, data, analytics/AI/ML, and business operations — will accelerate AI adoption and value creation.

As demonstrated by the above example, one frequently missing piece of the puzzle is the experience of practitioners. They can advise CEOs and board members on what works well and what does not work in similar industries and recent history.

The two board members made the difference, along with the CEO’s firm commitment to AI value delivery.

2. Corporate culture

In 2007, I was hired as Chief Analytics Officer (CAO) by the CEO of a private-equity portfolio company to help the company launch a growth strategy into new product lines and new market segments.

The CEO has decades of experience in consumer research and marketing analytics.  He believes data/analytics/AI/ML capabilities are critical to the company’s success. He embodies the culture of innovation and entrepreneurship.

The company culture was strong on experimentation and continuous learning. We used large-scale U.S. consumer segmentation data and analytics methodologies (including statistical and econometric models) to create new analytics solutions for banking and consumer marketing industries.

More than 10% of the company’s employees are data and analytics professionals under the CAO leadership. We shortened the product development cycle and frequently tested new products directly with interested clients.

The result: Within five years, the company grew 100% in revenue despite the 2007-2009 Great Recession. The investors had a successful exit. The company was acquired by an industry leader for $120 million.

This is an example to show how the CEO can build a data-enabled culture for long-term AI success. This journey requires accountability, collaboration, orchestration, and continuous learning.

AI success is not just a proof of concept (PoC) or a pilot program; it cannot be just an experiment or part of a hype cycle. It has to be woven into the fabric of the company’s operations and daily activities.

3. Organizational life cycle

Earlier in my career, another private equity portfolio company focused on education loans planned to grow rapidly to ride the wave of federal student loan consolidation due to decreasing interest rates.

The CEO and the board realized the importance of data-enabled decision-making so that they can maximize marketing and operational return on investment by targeting prospects with the highest response and conversion rates as well as the higher consolidation loan amount.

The company hired me to lead data science and machine learning model development in the role of Chief Analytics Officer as a member of the senior management team.

The result: In two years, we grew multiple times in business volume, went to IPO (Initial Public Offering) on NASDAQ, and got acquired by a top bank as a successful exit for the investors.

The lesson here is that when the company is at an early stage of growing rapidly, the CEO and the board can act fast to ride the market waves upward and identify AI capabilities as a primary driver for success.

Several companies where I worked in the past decades did not have the unwavering commitment to AI by the CEO and the board. This might be related to their current company life cycle, or their hesitation to proactively improve the business operations and make data-informed decisions.

It is not the case that the CEO or the board does not know the importance of AI success. Here are my perceptions:

• They do not show a strong commitment or a deep level of understanding of how to maximize the chance for AI success.

• They do not proactively nurture emerging AI talent.

• They do not openly advocate for analytics strategy or data-enabled culture.

• They do not design new ways to overcome organizational inertia.

The symptoms include:

• “Wait and see.”

• “Test the water.”

• “If we make some progress, great; if we do not make enough progress, we can just hire a new CDAO or do another round of re-org.”

• “Let the various forces fight out, so the winner with a bigger ego in the power dynamics would emerge.”

• “We do not disrupt the cash-flow model while it is going well, where the new way of doing business is vague and uncertain, and requires hard work for a long time to show benefits.”

They focus on survival, not on bending the growth curve upward or starting a new growth curve.

AI success for many companies is a process of fundamental changes, not superficial or cosmetic changes. Only the CEO and the board can effectively lead the lifecycle-changing, transformational process including organizational structure design, operational design and execution, and incentive design.

In summary, by driving the business strategy, leading the corporate culture, and proactively bending the organizational lifecycle curve upward, the CEO and the board of directors can directly improve the chance of long-term enterprise AI success.

Based on what we have seen in the past decades, only a small percentage of companies will emerge as the winners in the new AI capabilities development and value journey.

Note: The article was first published on the author’s LinkedIn blog. It has been republished with consent.

About the Author:

“Mr. Ge” Gary Cao advises CEOs and board of directors on analytics and AI strategy and serves as a fractional Chief Data and Analytics Officer (CDAO) or Chief AI Officer (CAIO). With 20 years of experience as a CDAO and serial founder of internal analytics startups, Cao has had a strong track record at 8 companies with revenue between US$40 million and US$120 billion.

Cao’s journey spans industries including healthcare (provider and payor), distribution, retail and ecommerce, financial services, banking, marketing, and credit/insurance risk. He is an expert advisor at the International Institute for Analytics and Rev1 Ventures startup studio and has been a speaker or panelist on various events and podcasts.