Enterprise AI success requires commitment from the CEO and the board of directors. This statement sounds simple and easy.  However, it is generally not on the top of mind for CEOs and the board. I know this from my observation and experience as a Chief Data Analytics Officer for the past two decades. I frequently interacted with and presented to CEOs and board members at three private companies as a direct report to the CEO or dotted line indirectly. 

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. 

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

Proper alignment among 4 domains of expertise (technology, data, analytics / AI / ML, and business operations) will accelerate AI adoption and value creation.  This alignment requires close interactions between the CEO and the CDAIO (chief data analytics AI officer).  

I see the following three patterns of CEO and Board of Directors behavior that can effectively drive enterprise AI success: 

Business Strategy 

A few years ago, a well-run family-owned company faced challenges of shifting market dynamics and decreasing margin for the core business unit.  This company had multi-billion-dollar annual revenue in the transportation consumables distribution industry.   

The CEO and the board of directors recruited two new members for the board. They each had extensive experience at other successful companies: one as Chief Analytics Officer (CAO) for two decades, and the other as a combined role of CFO and CIO.  The two new members started to influence the board’s agenda.  They provided 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 CDAO to start the enterprise Data/Analytics/AI function.  

We attained concrete results in three years where we achieved multiple times of ROI (return on investment) on a portfolio of several critical projects and created a clear roadmap to show a 5-year breakeven timeframe for the internal startup. This is an example of how CEO and the board can proactively lead the business strategy to drive the success of enterprise data/analytics/AI journey. Enterprise AI success certainly requires technology and data, but the more critical driving force is the human factor, including leadership, vision, culture, and talent.  

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.  Where the company is on the AI maturity curve depends on factors such as industry, company lifecycle, market dynamics, etc. The AI maturity journey requires AI readiness assessment, roadmap, resource planning, and deliberate collaboration to overcome inertia. 

The board of directors represents shareholders and makes capital expenditure decisions based on Return on Investment for 3-5 years or longer time horizons.  Unfortunately, in practice, the timeframe for ROI tends to be 1-2 years.  While ROI is not the only factor in AI strategy, it is the most frequently used excuse for not doing something that is perceived as uncertain or difficult.  

As demonstrated by the above example, one person can frequently miss a piece or pieces of the puzzle. They are limited by their  experience as practitioners to advise CEOs and board members on what works well and what does not work in similar industries and recent history.  However, two board members made the difference, along with the CEO’s firm commitment to AI value delivery.  

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 had decades of consumer research and marketing analytics experience.  He believed data and analytical AI capabilities were critical to the company’s success.  He embodied the culture of innovation and entrepreneurship. In this example, it is my belief that 80% of the mindset comes from the CEO’s experience, and 20% from external factors such as market dynamics or pressure from the board or the shareholders.  Importantly, the company culture highly valued experimentation and continuous learning.  We used large-scale US 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 workforce was composed of data and analytics professionals under the CAO leadership. We shortened the product development cycle and frequently tested new products directly with interested clients. We quickly learned from any mistakes and continued to improve.  Within five years, the company grew 100% in revenue despite the 2007-2009 Great Recession. The investors had a successful exit, and 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 a part of a hype cycle.  It has to be weaved into the fabrics of the company’s operations and daily activities. Incremental small wins work much better than a big-bang event.

Organizational Life Cycle  

Earlier in my career, another private-equity portfolio company focused on education loans with estimated rapid growth to ride the wave of federal student loan consolidation due to decreasing interest rates.  The CEO and the board had realized the importance of data-enabled decision making. They recognized that they could maximize marketing and operational return on investment by targeting prospects with the highest response and conversion rates along with a 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.   

We created a roadmap to leverage the current technology workflow, to clean up the data to make it directly relevant for business use cases. We developed predictive statistical models to target the best prospects with the highest value, and to implement the models and track performance for continuous improvement.  With limited direct-to-consumer marketing budget, we improved our campaign efficiency by 20% to 30%. In two years, we grew multiple times in business volume, went to IPO (Initial Public Offering) on NASDAQ, and were acquired by a top bank as a successful exit for the investors.   

The lesson here is that when a company is at an early stage to grow rapidly, the CEO and the board can act fast to ride the market waves upward and can leverage AI capabilities as a primary driver for success. Several companies where I worked in the past decades were led by CEO and boards that unfortunately did not have an unwavering commitment to AI. This might be related to their current company life cycle, or their hesitation to proactively change the business operations (because it is easier to stick to the things we know), as well as bias shaped by past success. 

Perceptions of CEOs and boards that fail in AI Success: 

Common Signals and Symptoms:  

From my experience, a common underlying issue is that many CEO and boards 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 structural and cultural change, 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.