Chloe Maraina has spent her career at the intersection of complex data systems and human-centric design, transforming raw big data into narratives that drive corporate evolution. As a Business Intelligence expert with a deep focus on data science, she has become a leading voice in how organizations must rethink their relationship with technology management and integration. In an era where artificial intelligence is fundamentally altering the corporate landscape, Maraina advocates for a vision where leaders move beyond the “stack” to develop a more intuitive, fluid relationship with emerging tools. Her insights focus on the transition from traditional technical oversight to a model of digital fluency that permeates every level of an organization, ensuring that innovation is not just a department, but a shared cultural instinct.
The following discussion explores the evolving requirements for modern technology leadership, specifically how the rapid acceleration of AI has rendered old delegation models obsolete. We delve into the critical distinction between possessing deep technical credentials and achieving true digital fluency, while examining why only a small fraction of companies currently feel prepared to generate measurable value from their investments. The conversation further highlights the rise of agentic AI and the practical steps leaders can take—from personal experimentation to low-stakes pilots—to foster an environment where adaptation is rewarded over mere execution.
How do you distinguish between technical expertise and digital fluency when evaluating leadership candidates, and why has this distinction become so critical in the current landscape?
Technical expertise remains the bedrock for any leader in this space, as you still need that fundamental ability to evaluate architecture decisions and engage credibly with engineering teams. It acts as a baseline expectation; if you can’t assess a vendor’s claims or understand the underlying structure of your data, you’re essentially flying blind. However, digital fluency is what truly separates the visionaries from those who are simply maintaining the status quo. It is the nuanced ability to assess, adopt, and integrate new tools as they emerge, coupled with the instinct to recognize whether a new technology represents a genuine business opportunity or just a passing, shiny trend. In today’s compressed timeline, a leader who relies solely on their past credentials will likely struggle to keep up with the pace of change that AI has introduced.
In the past, major disruptions like cloud computing or mobile technology were often confined to specific departments, but you suggest AI is different. How should leaders adapt their management style to reflect this cross-functional reality?
Previous waves of disruption, like the shift to cloud or the rise of mobile, followed a fairly recognizable pattern that allowed leaders to delegate the heavy lifting to specific technical teams while they remained focused on the broader business. AI has completely shattered that model because its impact isn’t concentrated in a single function; it touches marketing, finance, customer service, and product development simultaneously. If a CEO decides to delegate the entirety of their AI strategy to a CTO, or if a division head leaves adoption decisions solely to IT, they are effectively putting their entire organization at a massive competitive disadvantage. Digital fluency has now become a cross-functional requirement for leaders at every level, requiring a working familiarity with these tools so they can ask the right questions and spot emerging risks. The organizations that are currently pulling ahead aren’t necessarily the ones with the deepest pockets, but rather those whose leaders understood what these tools could do early enough to take decisive action.
The launch of ChatGPT in November 2022 seemed to catch many off guard, leading to a gap where only 26% of companies feel they can move beyond proofs of concept. What is the root cause of this struggle, and how does timing play into leadership decisions?
That 26% figure is a sobering reminder that the gap between experimentation and measurable value isn’t primarily a technology problem, but a leadership and timing problem. When ChatGPT launched, it caught organizations by surprise, and those that had treated AI as a “future consideration” suddenly found themselves frantically trying to understand a landscape their competitors were already navigating. Timing an adoption cycle is one of the hardest disciplines because moving too early means wasting capital on unproven tools, while moving too late means watching the market leave you behind. The leaders who successfully navigated this wake-up call weren’t those who predicted the specific technology, but those who were paying close enough attention to recognize its potential the moment it arrived. This delay in building internal knowledge and leadership alignment is exactly why so many companies are still stuck in the proof-of-concept phase today.
You mention that being aware of AI is no longer a differentiator but a baseline expectation. What does it actually look like for a leader to move from observing technology to becoming truly “AI-native”?
An AI-native leader is someone who refuses to observe the evolution of technology from a safe, academic distance and instead chooses to get their hands dirty with the tools themselves. This means integrating AI into their own daily workflows, pressure-testing its outputs to find the flaws, and building an instinctive sense of where the technology adds real value versus where it falls short. If you only rely on briefings and high-level presentations, you will never develop the organizational judgment required to make high-stakes decisions about AI integration. It’s about moving beyond the abstract potential of the technology and developing a genuine, hands-on fluency that informs how you lead your people. Leaders who fail to do this will find themselves unable to guide their teams through the complexities of the next decade, as they lack the practical intuition that only comes from direct experience.
As we move toward a future defined by agentic AI, how will the role of the technology leader change when it comes to designing human-AI collaboration?
The shift toward agentic AI—systems capable of planning and executing multi-step tasks with autonomy—requires a fundamentally new skill set that focuses on organizational design rather than just technology investment. We are already seeing AI agents handle everything from IT service desk tickets to monitoring contracts and synthesizing competitive intelligence in real time without human handoffs. For a leader, the challenge is no longer just “buying” the tech, but designing the specific ways in which these autonomous agents and human teams will work alongside each other. This kind of high-level judgment only develops through sustained, hands-on familiarity with what these systems can actually achieve. Those who can navigate this shift will be the ones creating efficient, hybrid workforces where technology handles the routine and humans are freed up for higher-level strategic thinking.
Beyond personal skill sets, what concrete actions can leaders take to ensure their entire organization develops the digital fluency needed to stay competitive?
Building a digitally fluent organization starts with visible engagement from the top, because employees tend to mirror the curiosity and adaptation they see in their leaders. Practically, this involves carving out dedicated time for personal experimentation and bringing technology discussions into general leadership forums rather than keeping them isolated in IT reviews. I strongly recommend creating low-stakes pilot programs that give teams the psychological safety to test and learn without the immediate pressure of showing a return on investment. Furthermore, you have to look at your performance frameworks; if you reward learning and adaptation alongside execution, you send a clear message that staying current is everyone’s responsibility. When employees understand how a new tool connects to a competitive outcome or helps avoid a risk, they become much more effective advocates for change.
What is your forecast for the next decade of technology leadership?
I believe the next decade will belong to the “Fluency-First” leaders—those who recognize that technological adaptation is a shared instinct rather than a top-down directive. We will see a shift away from massive, multi-year technology roadmaps in favor of more fluid, iterative environments where the ability to pivot is the greatest asset an organization possesses. Success will not be defined by who has the largest budget, but by who has done the hard work of building a culture where every team member feels empowered to experiment with the tools reshaping their industry. The leaders who define this era will be those who bridge the gap between human intuition and machine capability, creating organizations that are as resilient as they are innovative. Ultimately, the next ten years will reward those who view technology not as a specialized function to be managed, but as a language to be spoken fluently by everyone.
