AI success requires a full-stack CIO

CIOs must become full-stack leaders who bridge boardroom strategy and technical execution to succeed with AI, according to Afsheen Talasaz, former CIO of Colonial Pipeline and executive in residence with the Practitioners for Practitioners community. Talasaz argues that speed is not the real issue; organizations pulling ahead execute more effectively by ensuring clarity from executives to engineers. The full-stack CIO understands how every layer of the enterprise—from strategic priorities to engineering decisions—influences the next.

Every CIO I speak with today is wrestling with some version of the same question: How do we move faster with AI and deliver on our commitments? It’s an understandable concern. Boards and CEOs are asking about AI https://www.cio.com/article/4171959/ceos-top-priorities-for-it-leaders-today-2.html . Business leaders are experimenting with use cases. Employees are discovering tools daily, while technology vendors promise unprecedented gains in productivity, innovation, and competitive advantage. After hundreds of conversations with technology executives over the past year, I’ve become convinced that speed isn’t the real issue. The organizations pulling away from the pack aren’t necessarily adopting AI faster than everyone else. They’re executing more effectively — a subtle distinction that represents one of the defining leadership challenges of the AI era. Technology has never been the hardest part of transformation. People, priorities, culture, and operating models are the biggest challenges. The ability to translate bold boardroom aspirations into thousands of thoughtful decisions made every day by architects, engineers, product managers, analysts, and business leaders is where competitive advantage is created. AI may be accelerating the pace of change, but it hasn’t changed that fundamental truth. I’ve met plenty of executives who are exceptional in the boardroom. They know how to frame a vision, influence a board https://www.cio.com/article/272180/relationship-building-networking-how-to-wow-your-board-of-directors.html , and build confidence among investors and business leaders. I’ve also met remarkable technologists who instinctively understand the architectural decisions, engineering tradeoffs, and implementation details that determine how great ideas become reality. Modern CIOs, however, must move comfortably between both worlds. Afshean Talasaz is one who stands out among this rare breed. Long before becoming CIO of Colonial Pipeline, Talasaz built his career from the ground up as a business professional, data scientist, and technologist. He has designed enterprise platforms, built AI capabilities, led technology organizations, and partnered closely with executive leadership teams on business transformation. Today, as an executive in residence with our Practitioners for Practitioners P4P community, he helps CIOs and business leaders navigate one of the most significant technology shifts of our generation. While Talasaz brings deep knowledge of data and AI to the table, his greatest strength is his ability to create strategy and connect it with execution. He can spend the morning discussing enterprise reinvention with the board and the afternoon debating architectural principles with the teams responsible for bringing that vision to life. That versatility gives Talasaz a unique lens on how CIOs can deliver value with AI https://www.cio.com/article/4178006/state-of-the-cio-2026-cios-set-the-course-for-ai-roi.html . Software companies have a term for engineers who understand every layer of the technology stack: full-stack developers. Listen to Talasaz and it becomes evident that the AI era requires something similar from technology leaders: a full-stack CIO. A full-stack CIO understands how every layer of the enterprise influences the next. They recognize that every strategic priority becomes a portfolio investment, every investment shapes an operating model, every operating model influences architecture, every architecture choice informs product decisions, every product decision shapes engineering priorities. The best CIOs understand both ends of that journey. The extraordinary ones understand everything in between. And those who execute best lead with clarity, Talasaz says. “Everyone, from executives to middle managers to the people writing code, should be able to explain what we’re trying to achieve,” he emphasizes. “Clarity isn’t that we’ve handed out the PowerPoint. It’s that people genuinely understand where we’re going and can articulate it in their own language.” One of the unintended consequences of the AI boom is that organizations are beginning to confuse activity with alignment. They have AI councils, AI governance committees, AI innovation labs, AI centers of excellence, AI pilots, and AI roadmaps. Yet if you stop ten people in the hallway and ask a deceptively simple question, What business problem are we actually trying to solve? you’ll often hear ten different answers. As a result, architects optimize for one objective while product teams optimize for another. Business units pursue opportunities that seem perfectly reasonable from their perspective. Engineers make thoughtful technical decisions based on the information available to them. Individually, none of those decisions are necessarily wrong. Collectively, however, they create organizational drift. AI doesn’t create that problem. It simply accelerates the consequences. And while AI can be a force multiplier for the positive when every decision is guided by a shared understanding of where the organization is headed, it can also be a force multiplier for the negative, resulting in an organization simply moving faster in different directions. “When we have the fundamentals right, the tech infrastructure, the operating models, the nuances of how our business actually runs, we get the impacts of AI in a positive way,” Talasaz says. “When we don’t have those in place, AI can amplify the gaps or mute the benefits.” At a time when so much of the conversation surrounding AI is focused on algorithms, agents, and automation, it’s an important reminder that organizations don’t execute strategy; people do. Most executives are familiar with the concept of VUCA that characterizes today’s business environment. But Talasaz stresses the importance of turning this concern inward: “If the world outside our organizations is becoming more volatile, uncertain, complex, and ambiguous, what are we, as leaders, doing to the inside of our organizations?” Leaders spend enormous amounts of time helping their organizations respond to external disruption but comparatively little time asking whether they are inadvertently re-creating those same conditions internally in response to those external needs. Are we reducing uncertainty or introducing more of it? Are we simplifying work or adding unnecessary complexity? Are we helping people focus on what matters most, or asking them to navigate competing priorities and shifting expectations? Talasaz refers to this phenomenon as double VUCA — something I’ve witnessed repeatedly while working with CIOs over the past decade. Organizations often assume they’re struggling because of technology limitations when the real constraint is organizational friction. Teams wait for decisions. Priorities shift faster than roadmaps. Governance grows heavier. New committees are formed to solve problems created by existing committees. Everyone is working harder, yet the organization somehow feels slower. AI amplifies both outcomes. Organizations with clarity become dramatically more effective because AI accelerates good decisions. Organizations without clarity simply accelerate confusion. AI governance is one way to achieve greater clarity, but as Talasaz says, governance shouldn’t primarily exist inside policy manuals that few people read. Instead, AI governance should be embedded in the daily rhythms of the organization, shaping how teams collaborate, how decisions are made, how products move from ideas into production, and how innovation happens safely without requiring constant escalation. In other words, it’s all about your operating model. “If you had to pick one thing that isn’t technology, your operating model is the most important element for executing data and AI at scale,” he says. The best operating models create enough clarity that capable people can make thousands of decisions independently and confidently, without having to wait for permission. By embedding good governance into the way it works, the organization becomes faster. This advice echoes something I’ve heard repeatedly from some of the world’s most respected CIOs: High-performing organizations aren’t built on tighter control; they’re built on greater trust, supported by clear principles, shared expectations, and operating models that enable responsible decision-making at every level of the enterprise. Talasaz points out that technology leaders tend to speak in terms of transformation . He suggests CIOs consider a different word: reinvention. As he explains, transformation implies replacing what exists today with something new. Reinvention starts with a more clear-eyed and practical premise: Some things absolutely must change; others represent years, sometimes decades, of accumulated expertise, customer trust, operational discipline, and competitive advantage. Reinvention is about building on those strengths while also creating new ways to deliver value. The leaders making the greatest progress in their AI journeys seem to recognize that it’s less about abandoning the past than thoughtfully preparing the organization for the future. Full-stack CIOs must be able to map out the various layers of execution and planning that need to be done at every level of the organization to be successful. To help with this, Talasaz has developed a data and AI framework that draws on his own experiences “from the keyboard to the boardroom.” As Talasaz sees it, too many organizations have been doing good work in isolation. “They’re doing a lot of the right things,” he says. “They’re just not connected.” Boards may be discussing growth while business leaders redesign customer experiences. Product teams may be prioritizing new capabilities while architects modernize platforms. Data teams may be improving quality while engineers focus on delivery. Every group makes meaningful progress within its own domain, yet somewhere between strategy and execution, the connective tissue begins to disappear. Talasaz’s framework brings those connecting points to the forefront. Crucially, the framework doesn’t begin with technology or AI or even with data. It begins with the experiences the organization hopes to create for its customers, employees, or partners. Many AI initiatives start with the question, “What can this technology do?” And indeed, we need to be inspired by the possibilities and challenged to think differently by what the technology can do. But, Talasaz emphasizes, we also need to ask what experiences we need to deliver for our business and how the technology can make that a reality. The framework challenges CIOs to answer that question first. Only after the experiences are clearly defined does the conversation move to the capabilities required to deliver it, the business activities that support those capabilities, the AI and data products that enable them, and finally the data foundation that makes everything possible. This shift in perspective ensures that, rather than allowing technology investments to search for business value, the business experience defines the technology required to deliver it. For CIOs, that’s more than a planning exercise. It’s a fundamentally different way of leading. Over the coming months, the P4P community will be convening a series of small CxO roundtables to explore these issues and work more deeply with Afshean Talasaz’s 6×6 Data and AI Framework. CIOs and other enterprise leaders interested in participating are welcome to reach out to me directly.