Right now, 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 mission aligns with long-term enterprise technique and targets.
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Drives initiatives with clear goal and powerful organizational belief.
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Understands the right way to embed AI into their enterprise’s imaginative and prescient and tradition.
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Acknowledges that knowledge is produced, not merely collected, and probes its high quality, origins and potential biases.
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Goals to enhance people moderately 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 consultants to realize insights.
Collectively, the CIOs and AI thought leaders reveal what it takes to lead successfully within the AI period.
Let’s hear what the consultants needed to say.
What abilities 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 knowledge governance. These abilities create the muse AI wants — clear knowledge, 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 choices, champion moral and accountable AI, and foster a tradition of agility and experimentation.
“It is about shifting from lengthy transformation cycles to fast 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 smooth abilities 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 change into translators, connecting the dots between coverage, knowledge, 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 goal.
“We have to keep curious, ask higher questions, and get comfy with uncertainty. AI is not a mission with a end line. It is a functionality that retains evolving, and now we have to evolve with it.”
How can CIOs align AI investments with enterprise worth and knowledge excellence?
Randy Bean, creator, speaker, founding father of New Vantage Companions:
“Expertise is simply one other instrument. All CIOs should admire that any and all investments in AI and knowledge should ship enterprise worth that may be measured in methods corresponding to improved buyer expertise and satisfaction, larger operational effectivity, and/or improved income and revenue development.
Enterprise and expertise leaders should perceive the place and the way they will most successfully and effectively deploy AI and knowledge to realize these enterprise outcomes. With out measurable advantages from AI and knowledge 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 packages stall as a result of they’re led with a conventional IT mindset. AI must be handled before everything as a enterprise functionality tied to P&L outcomes, not as a tooling rollout.
“I like to consider it as a two-speed mannequin: AI is a dash and knowledge 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 every thing else scale.
“I might additionally encourage CIOs to arrange round merchandise moderately than tasks — cross-functional groups that personal a use case end-to-end — and to measure influence relentlessly. If a use case cannot present motion on a small set of end result KPIs, you both repair it rapidly or cease and redirect sources.”
Chris Little one, VP of product, knowledge engineering at Snowflake:
“The only most crucial takeaway for CIOs is {that a} sturdy knowledge basis is not optionally available — it’s vital for AI success. AI has made it straightforward to construct prototypes, however except you will have your knowledge in a single place, updated, secured, and properly ruled, you may battle to place these prototypes into manufacturing. The group laying the groundwork for that basis and getting enterprises’ knowledge AI-ready is knowledge engineering. CIOs who nonetheless see knowledge engineering as a back-office operate are already 5 years behind, and doubtless coaching their future opponents.
“What we’re seeing on this new period is that AI success is inseparable from knowledge excellence. Good CIOs deal with their knowledge 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 knowledge ecosystems that unify structured and unstructured knowledge, implement governance, and energy real-time intelligence.”
Jared Coyle, chief AI officer, SAP Americas:
“Your knowledge won’t ever be good. And it does not need to be. It must be indicative of your organization’s actuality. However your knowledge will get lots higher if you happen to first use AI to enhance the UX. Then folks will use your techniques extra, and in the best way meant, creating higher knowledge. That higher knowledge will allow higher AI. And the virtuous cycle can have begun. However it begins with the human facet of the equation, not the technological.”
Mastering AI fundamentals: Three AI domains
CIOs do not want deep technical mastery corresponding to coding in Python or tuning neural networks — however they need to perceive AI fundamentals. This contains greedy core AI rules, 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 proper questions, interpret insights successfully, and make knowledgeable strategic choices. Let’s take a look at the three AI domains.
Analytical AI
Analytical AI contains knowledge science, statistics, modeling, machine studying and neural networks. It focuses on analyzing structured knowledge to determine patterns and make predictions. Its core power lies in predictive modeling — forecasting outcomes based mostly upon historic knowledge. In line with Dresner Advisory Providers’ 2025 analysis, frequent use instances embrace:
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Predictive upkeep.
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High quality assurance and danger administration.
Generative AI
In distinction, generative AI has revolutionized how organizations analyze unstructured knowledge. It will possibly create new content material — corresponding to textual content, photographs, audio, video — by studying patterns and buildings from present data. It excels at processing unstructured knowledge and producing related outputs. Key parts CIOs ought to perceive embrace the position 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 embrace:
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Productiveness and effectivity beneficial properties.
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Improved buyer expertise and personalization.
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Higher search and determination 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 knowledge management, in accordance with Dresner analysis. Key targets for synthetic brokers embrace:
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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 concentrate on productiveness beneficial properties and effectivity moderately than broad innovation. In distinction, organizations with larger knowledge 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 purchasing as private and interesting as visiting a retailer. Its digital purchasing assistant, Gemma, helps clients discover the proper jewellery by studying concerning the event, recipient and funds. For instance, when a client on the lookout for a present for his or her mom mentions she loves ballet, Gemma recommends items impressed by dance — sharing tales and particulars very similar to an in-store affiliate. The result’s a guided, personalised 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 change into AI savvy. By understanding AI’s umbrella of applied sciences and realizing the right way to apply them to actual enterprise issues, CIOs are uniquely positioned to guide their organizations into the long run.
