At the moment, we’re beginning on the finish — with the seven important behaviors that outline an AI-savvy CIO. These behaviors are derived from my conversations with two CIOs and 4 AI thought leaders.
The AI-savvy CIO does the next:
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Ensures each AI venture aligns with long-term enterprise technique and targets.
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Drives initiatives with clear function and powerful organizational belief.
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Understands the way to embed AI into their enterprise’s imaginative and prescient and tradition.
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Acknowledges that information is produced, not merely collected, and probes its high quality, origins and potential biases.
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Goals to reinforce people relatively than merely remove labor prices.
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Helps the group grasp the basics of AI and perceive AI’s potential.
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Is a steady learner and reaches out to specialists to achieve insights.
Collectively, the CIOs and AI thought leaders reveal what it takes to lead successfully within the AI period.
Let’s hear what the specialists needed to say.
What expertise and management qualities outline an AI-savvy CIO?
Pedro Martinez Puig, CIO at Sibelco Group:
“CIOs already carry essential strengths to AI adoption: the flexibility to align expertise with enterprise technique, handle advanced enterprise architectures, and implement sturdy information governance. These expertise create the muse AI wants — clear information, safe infrastructure and clear ROI self-discipline.
“However, main within the AI period calls for extra. CIOs should develop sensible AI literacy to make knowledgeable selections, champion moral and accountable AI, and foster a tradition of agility and experimentation.
“It is about transferring from lengthy transformation cycles to speedy prototyping whereas managing new dangers like bias and mannequin drift. Those that mix strategic imaginative and prescient with these rising capabilities will flip AI from a buzzword right into a supply of sustainable aggressive benefit.”
Nicole Coughlin, CIO of the Metropolis of Cary, N.C.:
“Empathy, communication, and alter management — these are the mushy expertise we have all the time valued, they usually’re those that matter most proper now. AI adoption is not only a expertise shift; it is a folks and tradition journey. CIOs should turn into translators, connecting the dots between coverage, information, ethics, and expertise. The CIOs who can simplify complexity, construct belief throughout departments, and lead with transparency will assist their organizations navigate this second with confidence and function.
“We have to keep curious, ask higher questions, and get snug with uncertainty. AI is not a venture with a end line. It is a functionality that retains evolving, and we have now to evolve with it.”
How can CIOs align AI investments with enterprise worth and information excellence?
Randy Bean, creator, speaker, founding father of New Vantage Companions:
“Know-how is simply one other software. All CIOs should recognize that any and all investments in AI and information should ship enterprise worth that may be measured in methods resembling improved buyer expertise and satisfaction, larger operational effectivity, and/or improved income and revenue progress.
Enterprise and expertise leaders should perceive the place and the way they will most successfully and effectively deploy AI and information to realize these enterprise outcomes. With out measurable advantages from AI and information investments, CIOs will face an inevitable demand for accountability and a ensuing backlash.”
Pedro Amorim, professor, College of Porto Enterprise Faculty:
“In my expertise, many AI applications stall as a result of they’re led with a standard IT mindset. AI must be handled in the beginning as a enterprise functionality tied to P&L outcomes, not as a tooling rollout.
“I like to think about it as a two-speed mannequin: AI is a dash and information is a marathon. The AI work needs to be near the enterprise and vertical, and be fast-to-value.The information work needs to be holistic and sturdy, as a result of it is the platform that lets all the pieces else scale.
“I might additionally encourage CIOs to prepare round merchandise relatively than tasks — cross-functional groups that personal a use case end-to-end — and to measure affect relentlessly. If a use case cannot present motion on a small set of consequence KPIs, you both repair it shortly or cease and redirect assets.”
Chris Youngster, VP of product, information engineering at Snowflake:
“The one most crucial takeaway for CIOs is {that a} sturdy information basis is not non-compulsory — it is vital for AI success. AI has made it simple to construct prototypes, however until you’ve your information in a single place, updated, secured, and properly ruled, you will wrestle to place these prototypes into manufacturing. The staff laying the groundwork for that basis and getting enterprises’ information AI-ready is information engineering. CIOs who nonetheless see information engineering as a back-office perform are already 5 years behind, and possibly coaching their future opponents.
“What we’re seeing on this new period is that AI success is inseparable from information excellence. Sensible CIOs deal with their information engineers not as help, however as strategic enablers of transformation. They’re centered much less on deploying siloed AI fashions and extra on constructing AI-ready information ecosystems that unify structured and unstructured information, implement governance, and energy real-time intelligence.”
Jared Coyle, chief AI officer, SAP Americas:
“Your information won’t ever be excellent. And it would not need to be. It must be indicative of your organization’s actuality. However your information will get lots higher for those who first use AI to enhance the UX. Then folks will use your techniques extra, and in the best way supposed, creating higher information. That higher information will allow higher AI. And the virtuous cycle may have begun. Nevertheless it begins with the human aspect of the equation, not the technological.”
Mastering AI fundamentals: Three AI domains
CIOs do not want deep technical mastery resembling coding in Python or tuning neural networks — however they have to perceive AI fundamentals. This consists of greedy core AI ideas, machine studying ideas, statistical modeling, and moral implications.
Mastery begins with CIOs understanding AI as an umbrella of applied sciences that automate various things. With this foundational fluency, they will ask the precise questions, interpret insights successfully, and make knowledgeable strategic selections. Let’s take a look at the three AI domains.
Analytical AI
Analytical AI consists of information science, statistics, modeling, machine studying and neural networks. It focuses on analyzing structured information to determine patterns and make predictions. Its core power lies in predictive modeling — forecasting outcomes primarily based upon historic information. In line with Dresner Advisory Companies’ 2025 analysis, frequent use instances embody:
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Predictive upkeep.
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High quality assurance and threat administration.
Generative AI
In distinction, generative AI has revolutionized how organizations analyze unstructured information. It will probably create new content material — resembling textual content, photographs, audio, video — by studying patterns and constructions from current data. It excels at processing unstructured information and producing related outputs. Key elements CIOs ought to perceive embody the function and performance of:
These applied sciences work collectively to generate contextually related, clever outputs. In line with Dresner’s 2025 analysis, the highest drivers of generative AI adoption embody:
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Productiveness and effectivity features.
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Improved buyer expertise and personalization.
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Higher search and choice making.
To raised perceive how Generative AI is remodeling enterprise and administration, see “The HBR Information to Generative AI for Managers” by Elisa Farri and Gabriele Rosani.
Agentic AI
Agentic AI represents the subsequent stage of AI evolution. Agentic AI merges generative and analytical AI with low-code workflow automation, enabling autonomous brokers to behave, determine, and adapt with minimal human intervention.
On this mannequin, analytical AI delivers optimum outcomes for these brokers. Agentic AI goes past producing responses — it executes duties and delivers outcomes. Constructed on workforce/agent orchestration platforms, it creates digital brokers and data-driven workflows.
Success with agentic AI correlates strongly with enterprise intelligence (BI) maturity and industrialization, analytical AI adoption and powerful information management, in line with Dresner analysis. Key targets for synthetic brokers embody:
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Improved buyer expertise and personalization.
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Enhanced decision-making.
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Elevated productiveness and effectivity.
Notably, organizations with tighter BI budgets are inclined to deal with productiveness features and effectivity relatively than broad innovation. In distinction, organizations with larger information maturity take a wider view utilizing agentic AI to drive actual enterprise transformation.
The next instance exhibits how agentic AI can allow tangible transformation.
Jewellery retailer Pandora is utilizing an agentic AI layer to make on-line buying as private and fascinating as visiting a retailer. Its digital buying assistant, Gemma, helps prospects discover the proper jewellery by studying concerning the event, recipient and finances. For instance, when a client in search of a present for his or her mom mentions she loves ballet, Gemma recommends items impressed by dance — sharing tales and particulars very like an in-store affiliate. The result’s a guided, customized expertise that feels human and considerate.
Parting Phrases
CIOs deeply perceive enterprise transformation and what it takes to drive significant change. Now’s the time for CIOs to develop and turn into AI savvy. By understanding AI’s umbrella of applied sciences and figuring out the way to apply them to actual enterprise issues, CIOs are uniquely positioned to steer their organizations into the long run.
