To Succeed in AI, Let The CEO Take Over
Welcome to a new edition of The Board: Distillation Aftershots (*).
This post is a replicate of the weekly newsletter where we share curious and interesting insights and data points distilled from enterprise technology to identify what’s notable. Subscribe to the newsletter here.
In this issue, we are going to address a slew of articles and research released in the last few weeks that examine who is making decisions about AI adoption in the enterprise.
We started with three lines of thought:
- It is a technology solution, let the CIO/CTO or tech-savvy executive make the decisions on how to tackle AI for the enterprise
- Let’s name an executive to be in charge of this – including bridging the needs of the business, compliance, and IT plans and mandates (some companies went as far as naming a chief AI officer)
- The board, with input from line-of-business and technology executives, made strategic decisions and recommendations to the CEO.
The problem with the first one is that the compressed time cycles for AI innovation made most technology executives (and even their staff) dated in their knowledge of AI. When the technology evolution cycles were reduced from 7-10 years to 3-5 years, there is limited time to take the traditional approach to learning enough to make a decision: relying on well-versed advisors and quickly learning about the technologies is paramount.
An executive in charge of making technology decisions without the power to implement them is a moot point in fast-moving technology cycles. Even if they were adequately educated, the market is severely talent-deficient. Further, their inability to align with other executives who may not be as well-versed in new technologies or cannot understand the long-term issues of early adoption of enterprise technologies.
All these lines of thought have pros and cons; the most recent research has shown mostly cons due to a lack of alignment with business strategic goals, insufficient AI education, a very fast-moving market, and uncertainty about the long-term of what AI can accomplish.
In an environment where 2/3 of the CEOs think they should replace board members because they lack the technical know-how to help make AI decisions, and where only 1/6 board members confess to understanding AI sufficiently to help make those decisions, CEOs are frustrated; CEO’s are stepping up to assume a leadership role in making decisions about AI adoption and strategies.
Here are some reading resources (this is a very early concept; most of the content is still in LinkedIn discussions):
- BCG’s 2026 AI Radar report that started it all: “72% of CEOs say they are the main decision maker on AI in their organization”
- Dataiku conducted a study of 500 CEOs, showing that 83% say their involvement in AI-related decision-making increased over the past year; 68% say they are involved in more than half of all AI initiatives.
- McKinsey correlates CEO involvement and better financial results, concluding that CEO oversight has the most impact on EBIT attributable to gen AI at larger companies.
- IBM supports executives making decisions by stating in a report that 59% of CEOs say success depends on leaders with authority to make critical decisions.
- KPMG argues that CEOs allocating larger budgets to AI is a strong proxy for CEO ownership because budget priority is one of the clearest expressions of decision control (maybe, but good data points).
- PwC confirms what we all know well, but with data: CEOs have AI at the center of their agendas, so they require more direct control over it.
- Accenture confirms that leading AI organizations place the CEO squarely in the middle when making AI decisions.
What’s your take? We are fostering a community of executives who want to discuss these issues in depth. This newsletter is but a part of it. We welcome your feedback and look forward to engaging in these conversations.
If you are interested in exploring the full report, discussing the Board’s offering further, or have any additional questions, please contact me at [email protected], and I will be happy to connect with you.
(*) A normal distillation process produces byproducts: primary, simple ones called foreshots, and secondary, more complex and nuanced ones called aftershots. This newsletter highlights remnants from the distillation process, the “cutting room floor” elements, and shares insights to complement the monthly report.
Esteban Kolsky spent the first part of his professional career working on the customer service front line and in management roles, culminating in early implementations of first-generation CRM tools. In 2000, he became a research analyst at Gartner, focused on CRM, customer service, customer experience management, enterprise feedback management, and voice of the customer, among topics that later became the basis for the current field of CX. During his tenure at Gartner, Kolsky assisted vendors and Global 2000 organizations with their customer strategies. He left Gartner to focus his career on strategic advisory services and influenced all the major software vendors, as well as many smaller vendors, in the CX industry and advised executive leadership teams across industries and countries…...
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