Today, we’re starting at the end — with the seven essential behaviors that define an AI-savvy CIO. These behaviors are derived from my conversations with two CIOs and four AI thought leaders.
The AI-savvy CIO does the following:
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Ensures every AI project aligns with long-term business strategy and goals.
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Drives initiatives with clear purpose and strong organizational trust.
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Understands how to embed AI into their enterprise’s vision and culture.
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Recognizes that data is produced, not merely collected, and probes its quality, origins and potential biases.
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Aims to augment humans rather than simply eliminate labor costs.
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Helps the organization grasp the fundamentals of AI and understand AI’s potential.
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Is a continuous learner and reaches out to experts to gain insights.
Together, the CIOs and AI thought leaders reveal what it takes to lead effectively in the AI era.
Let’s hear what the experts had to say.
What skills and leadership qualities define an AI-savvy CIO?
Pedro Martinez Puig, CIO at Sibelco Group:
“CIOs already bring critical strengths to AI adoption: the ability to align technology with business strategy, manage complex enterprise architectures, and enforce strong data governance. These skills create the foundation AI needs — clean data, secure infrastructure and clear ROI discipline.
“But, leading in the AI era demands more. CIOs must develop practical AI literacy to make informed decisions, champion ethical and responsible AI, and foster a culture of agility and experimentation.
“It’s about moving from long transformation cycles to rapid prototyping while managing new risks like bias and model drift. Those who combine strategic vision with these emerging capabilities will turn AI from a buzzword into a source of sustainable competitive advantage.”
Nicole Coughlin, CIO of the City of Cary, N.C.:
“Empathy, communication, and change leadership — those are the soft skills we’ve always valued, and they’re the ones that matter most right now. AI adoption isn’t just a technology shift; it’s a people and culture journey. CIOs must become translators, connecting the dots between policy, data, ethics, and technology. The CIOs who can simplify complexity, build trust across departments, and lead with transparency will help their organizations navigate this moment with confidence and purpose.
“We need to stay curious, ask better questions, and get comfortable with uncertainty. AI isn’t a project with a finish line. It’s a capability that keeps evolving, and we have to evolve with it.”
How can CIOs align AI investments with business value and data excellence?
Randy Bean, author, speaker, founder of New Vantage Partners:
“Technology is just another tool. All CIOs must appreciate that any and all investments in AI and data must deliver business value that can be measured in ways such as improved customer experience and satisfaction, greater operational efficiency, and/or improved revenue and profit growth.
Business and technology leaders must understand where and how they can most effectively and efficiently deploy AI and data to achieve these business results. Without measurable benefits from AI and data investments, CIOs will face an inevitable demand for accountability and a resulting backlash.”
Pedro Amorim, professor, University of Porto Business School:
“In my experience, many AI programs stall because they’re led with a traditional IT mindset. AI needs to be treated first and foremost as a business capability tied to P&L outcomes, not as a tooling rollout.
“I like to think of it as a two-speed model: AI is a sprint and data is a marathon. The AI work should be close to the business and vertical, and be fast-to-value.The data work should be holistic and durable, because it’s the platform that lets everything else scale.
“I’d also encourage CIOs to organize around products rather than projects — cross-functional teams that own a use case end-to-end — and to measure impact relentlessly. If a use case can’t show movement on a small set of outcome KPIs, you either fix it quickly or stop and redirect resources.”
Chris Child, VP of product, data engineering at Snowflake:
“The single most critical takeaway for CIOs is that a strong data foundation isn’t optional — it’s critical for AI success. AI has made it easy to build prototypes, but unless you have your data in a single place, up to date, secured, and well governed, you’ll struggle to put those prototypes into production. The team laying the groundwork for that foundation and getting enterprises’ data AI-ready is data engineering. CIOs who still see data engineering as a back-office function are already five years behind, and probably training their future competitors.
“What we’re seeing in this new era is that AI success is inseparable from data excellence. Smart CIOs treat their data engineers not as support, but as strategic enablers of transformation. They’re focused less on deploying siloed AI models and more on building AI-ready data ecosystems that unify structured and unstructured data, enforce governance, and power real-time intelligence.”
Jared Coyle, chief AI officer, SAP Americas:
“Your data will never be perfect. And it doesn’t have to be. It needs to be indicative of your company’s reality. But your data will get a lot better if you first use AI to improve the UX. Then people will use your systems more, and in the way intended, creating better data. That better data will enable better AI. And the virtuous cycle will have begun. But it starts with the human side of the equation, not the technological.”
Mastering AI fundamentals: Three AI domains
CIOs don’t need deep technical mastery such as coding in Python or tuning neural networks — but they must understand AI fundamentals. This includes grasping core AI principles, machine learning concepts, statistical modeling, and ethical implications.
Mastery starts with CIOs understanding AI as an umbrella of technologies that automate different things. With this foundational fluency, they can ask the right questions, interpret insights effectively, and make informed strategic decisions. Let’s look at the three AI domains.
Analytical AI
Analytical AI includes data science, statistics, modeling, machine learning and neural networks. It focuses on analyzing structured data to identify patterns and make predictions. Its core strength lies in predictive modeling — forecasting outcomes based upon historical data. According to Dresner Advisory Services’ 2025 research, common use cases include:
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Predictive maintenance.
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Quality assurance and risk management.
Generative AI
In contrast, generative AI has revolutionized how organizations analyze unstructured data. It can create new content — such as text, images, audio, video — by learning patterns and structures from existing information. It excels at processing unstructured data and generating relevant outputs. Key components CIOs should understand include the role and functionality of:
These technologies work together to generate contextually relevant, intelligent outputs. According to Dresner’s 2025 research, the top drivers of generative AI adoption include:
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Productivity and efficiency gains.
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Improved customer experience and personalization.
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Better search and decision making.
To better understand how Generative AI is transforming business and management, see “The HBR Guide to Generative AI for Managers” by Elisa Farri and Gabriele Rosani.
Agentic AI
Agentic AI represents the next stage of AI evolution. Agentic AI merges generative and analytical AI with low-code workflow automation, enabling autonomous agents to act, decide, and adapt with minimal human intervention.
In this model, analytical AI delivers optimum outcomes for these agents. Agentic AI goes beyond generating responses — it executes tasks and delivers results. Built on workforce/agent orchestration platforms, it creates digital agents and data-driven workflows.
Success with agentic AI correlates strongly with business intelligence (BI) maturity and industrialization, analytical AI adoption and strong data leadership, according to Dresner research. Key goals for artificial agents include:
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Improved customer experience and personalization.
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Enhanced decision-making.
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Increased productivity and efficiency.
Notably, organizations with tighter BI budgets tend to focus on productivity gains and efficiency rather than broad innovation. In contrast, organizations with greater data maturity take a wider view using agentic AI to drive real business transformation.
The following example shows how agentic AI can enable tangible transformation.
Jewelry retailer Pandora is using an agentic AI layer to make online shopping as personal and engaging as visiting a store. Its virtual shopping assistant, Gemma, helps customers find the perfect jewelry by learning about the occasion, recipient and budget. For example, when a shopper looking for a gift for their mother mentions she loves ballet, Gemma recommends pieces inspired by dance — sharing stories and details much like an in-store associate. The result is a guided, personalized experience that feels human and thoughtful.
Parting Words
CIOs deeply understand business transformation and what it takes to drive meaningful change. Now is the time for CIOs to grow and become AI savvy. By understanding AI’s umbrella of technologies and knowing how to apply them to real business problems, CIOs are uniquely positioned to lead their organizations into the future.
