Generative AI: How CEOs Can Meet Board Expectations

Written by Kenneth Holley

As generative AI captivates industries and public imagination alike, CEOs are finding themselves under growing pressure to incorporate this emerging technology into their strategic plans. With 46% of boards declaring generative AI their number one priority in a recent survey, the stakes are high for leadership to deliver. This board-driven mandate, combined with inflated expectations of AI’s business impacts, leaves many CEOs experiencing what Harvard Business Review dubbed “generative AI-nxiety.” 

However, while the hype around generative AI’s potential should be tempered with prudent evaluations, the technology’s capabilities should not be dismissed either. Used judiciously, generative AI can enhance competitiveness, bolster security, and add new dimensions of performance and functionality. The key for leadership is developing a measured, well-coordinated strategy for adoption suited to their specific organizational needs and risks. 

With boards impatient for progress, it is understandable CEOs feel pressure to plunge headlong into AI initiatives without laying proper groundwork. But methodical planning is essential, even if it means slowing pace. Deployed hastily and without governance guardrails, generative AI brings threats that likely outweigh benefits. However, approached deliberately through a phased rollout focused on high-impact use cases, backed by risk management protocols, generative AI can start paying dividends without undue disruptions. 

This article provides guidance for CEOs to meet mounting board expectations around generative AI through a controlled process that unlocks advantages while safeguarding interests. It synthesizes insights from 300 data leaders at organizations in various stages of AI adoption. Their perspectives make clear leadership plays a decisive role in driving successful generative AI strategies. By appointing empowered leaders, pacing rollouts judiciously, and enacting policies to govern usage, CEOs can steer their organizations skillfully into the AI future boards eagerly anticipate. 

Appointing Leadership to Drive Adoption 

According to the survey, 98% of companies actively utilizing generative AI today have a single empowered leader driving adoption. This central figure is vital for unifying strategy and rapidly advancing capabilities. Without clear leadership in place, generative AI projects easily become siloed and fractured across business units. 

Yet while data leaders and CTOs often oversee technical deployment, boards view CEOs as ultimately responsible for generative AI strategy. This means providing direction, removing roadblocks, and aligning teams under the leader appointed to spearhead rollout. Rather than delegating fully, CEOs must remain actively engaged as sponsors and stewards. 

This leadership role entails nurturing understanding and buy-in across the organization. As the public face of the company, CEOs are best positioned to explain generative AI’s promise and proactively address any fears or skepticism amongst employees or leadership. Their oversight also ensures adherence to policies and best practices governing ethical AI usage. 

Generative AI brings profound change, and leadership must shepherd their organization through uncertainties. By visibly empowering a central leader backed by cross-functional support, CEOs demonstrate commitment to a cohesive rollout. They send a clear signal that generative AI is an enterprise-wide strategic priority, not an isolated technical initiative. 

Starting Slowly to Learn Before Scaling 

Despite board impatience, plunging hastily into full-scale generative AI deployment is ill-advised. Organizations should start slowly, proven by the fact that even early adopters rate themselves as only exploring or experimenting so far. Beginning with low-risk pilots focused on targeted impact allows for gradual capability building while gathering insights to shape wider rollout. 

This measured approach allows time for proper governance foundations to be established in parallel with early technical testing. It also lets teams identify the most promising and appropriate uses of generative AI for their unique organization before attempting to scale broadly. 

According to the survey, the current most common applications are content generation, analytics insights, code development, and process documentation. While these provide helpful starting points, each company’s needs are distinct. Starting small allows carefully evaluating performance and potential risks versus benefits across a set of use cases tailored for one’s industry and objectives. 

It also allows space for organizational learning and adaptation. Generative AI remains an emerging technology, and best practices are still evolving across sectors. By taking time to learn before pursuing organization-wide integration, leaders can make more informed decisions about where to selectively apply generative AI versus relying purely on hype. 

This experimental stage also helps cement understanding of generative AI through tangible examples prior to larger commitments. Pilots focused on high-impact applications generate internal advocates who can then help “sell” expansion to skeptical stakeholders by pointing to measured successes. Starting slowly sets the stage for organic rather than forced adoption. 

Managing Risks and Governance 

With great technological capability comes great responsibility. An incremental, use case-focused introductory approach allows for the implementation of governance frameworks to manage generative AI’s very real risks around areas like data quality, security, and ethics. 

A measured rollout provides time to establish policies, controls, and oversight before generative AI becomes entrenched in operations. According to the survey, 77% of boards already report having ethical guidelines in place, while 75% are addressing regulatory compliance considerations. 

While starting small, leaders must proactively embed governance into AI systems and processes. Key policy areas include privacy, security, fairness, transparency, accountability, and impact management. Regular audits help ensure adherence and identify gaps to strengthen controls. 

Particularly crucial is delineating clear lines of responsibility and oversight for those empowered to build, deploy, and manage generative AI within business units. Documented procedures for risk assessment, monitoring, and mitigation are essential foundations for safe scaling. 

If internal skills are lacking to architect governance frameworks, trusted outside experts can advise and augment teams. The proven systems integrators and consultants that organizations rely on for other functions can help craft tailored policies and risk management approaches for generative AI assurance. 

The Window of Opportunity 

Generative AI’s moment is at hand, but CEOs who move prudently and intentionally can meet board expectations while avoiding pitfalls of haste. Appointing empowered leadership and advancing carefully from targeted pilots to governed broad deployment will unlock advantages while navigating risks skillfully. 

Rather than reacting reflexively to board impatience, CEOs must assert themselves as proactive stewards of a measured generative AI strategy. There will always be pressure for more and faster results. However, long-term interests are served best by laying foundations first for AI adoption that aligns with organizational values and needs. With sound leadership, prudent pacing, and instilling a culture of responsible innovation, CEOs can satisfy stakeholders while steering their organization confidently into the AI-enabled future.

Kenneth Holley

Founder and Chairman, Silent Quadrant. Read Kenneth’s full executive profile.