Chief Intelligence Officers? How Gen AI is rewiring the CxOs Brain

The executive playbook is being rewritten—line by line, prompt by prompt. What began as a buzzword is now being hardwired into the strategic core of some of the world’s most forward-looking enterprises.

Generative AI is no longer the domain of experimental labs or marketing teams; it’s in the boardroom, shaping high-stakes decisions with unprecedented speed and scale. But what does that actually look like behind the scenes?

To find out, we turned to Arun Chandrasekaran, Distinguished VP Analyst at Gartner, whose research maps the evolving relationship between CxOs and GenAI. From market simulations and AI-powered ideation to rethinking risk and leadership culture, Chandrasekaran offers a grounded, insider view of how the C-suite is learning to think alongside machines—and what it takes to lead responsibly in this new era of intelligence. 

Excerpts:

Based on Gartner’s research, how are CxOs currently integrating Generative AI into their strategic and operational decision-making processes?

Generative AI is having a huge impact in a variety of strategic and operational contexts, enabling CxOs to make decisions more quickly and based on more data.

Here are a few areas being advanced through the use of generative AI

Market Intelligence - As generative AI tools are advanced into deep research, they are capable of summarizing industry reports, synthesizing competitive intelligence, and "playing out" potential "what-if" scenarios. The outcome is quicker, wider insights generated to inform market entry , M&A, or portfolio strategy.

Innovation Workshops - AI-supported brainstorming to develop product ideas or strategic bets, validated with historical market data. The outcome is broader strategic thinking and supports faster hypothesis testing.

Customer Experience Optimization - Business leaders are using generative AI for generating bespoke marketing copy, chat responses, or journey maps, based on consumer behaviour patterns. This drives engagement and diminishes manual content generation.

Which industries or enterprise functions are emerging as early adopters of GenAI at the boardroom level, and why?

The three industries that are early adopters of Gen AI for Strategic use include High tech, Financial Services, and Life sciences. All these are knowledge industries with an emphasis on data-driven decision making with significant market pressure from upstarts to innovate or cut costs.

What decision areas (e.g., market forecasting, supply chain, workforce planning) are best suited for GenAI augmentation, and which ones remain reliant on human intuition?

Generative AI is making the most impact in areas like Marketing, Software Engineering, Customer Service, and Sales. These functions benefit from AI’s ability to process vast amounts of data quickly. On the other hand, Legal and HR departments see less GenAI adoption, as these areas require high levels of accuracy, predictability, and human judgment.

What frameworks or best practices would you recommend for evaluating the readiness of an organization to implement GenAI at the leadership level?

Business and tech leaders must prioritize business value when choosing AI use cases, focus on AI literacy and responsible AI, nurture cross-functional collaboration, and stress continuous learning to achieve successful outcomes.

How should enterprises balance speed and innovation with the need for explainability, ethical oversight, and risk mitigation when using GenAI for critical decisions?

Leaders need to clearly outline and share a vision for responsible AI, establishing straightforward principles and policies that address fairness, bias reduction, ethics, risk management, privacy, sustainability, and compliance with regulations.

They should also pinpoint the risks associated with Generative AI, such as privacy concerns, security issues, hallucinations, explainability, and legal compliance challenges, along with practical ways to mitigate these risks. When choosing and prioritizing use cases, it’s essential to consider responsible AI by filtering out those that carry unacceptable risks. Each Generative AI use case should have a designated champion responsible for ensuring that development and usage align with established policies. Finally, it’s crucial to continuously assess and test new tools to manage Generative AI risks, utilizing both the tools provided by model developers and additional augmented solutions.

What are the most significant misconceptions CxOs have about the capabilities or limitations of GenAI in executive decision-making?

“GenAI will give me the answer”. The reality is it’s not a decision-maker — it’s a thought partner.

“It knows our business” - Out-of-the-box GenAI has zero context. Most AI models are general-purpose technology that need to be steered and grounded with enterprise or domain data.

Execs think GenAI implementation is a tech deployment problem. Meaningful adoption depends on trust, change management, legal clarity, and user behaviour.

What trends do you foresee in GenAI’s evolution within the enterprise leadership stack - both in terms of product maturity and cultural readiness?

From generalist chatbots to copilots for CxOs (eg “Financeagent” or “Chief of Staff AI”) that comprehend org charts, KPIs, and quarterly reports, to multimodal GenAI tools that by default consume text, charts, spreadsheets, voice, and video, AI agents will be churning out autonomous intelligence, AI fluency will be a crucial executive differentiator ala digital fluency, post 2010, and boards will begin requesting clear AI ROI as a standing agenda item.