Our retail expert, Zainab Hussain, is an e-commerce strategist who has spent years at the front lines of customer engagement and operations management. She has seen firsthand how billion-dollar companies can lose their way when they prioritize internal spreadsheets over the actual humans they serve. As organizations navigate the complexities of 2026, Zainab brings a grounded, tactical perspective on how leadership can successfully integrate artificial intelligence without losing the soul of their business. In this discussion, we explore why many high-end CRM investments fail to deliver clarity, how the math of tiny daily improvements can transform a culture, and the vital importance of “visceral knowledge” in an era dominated by filtered data.
Many organizations invest heavily in high-end CRMs and dashboards yet still struggle to identify future customer needs. How can leadership bridge this gap between having massive datasets and gaining actionable insights? Please describe the specific cultural shifts needed to move beyond just collecting information.
The disconnect usually happens because organizations fall into a “dashboard trap” where they prioritize internal metrics like budgets and quarterly targets over true customer visibility. I recently spoke with a CMO of a $400 million services business who had every tool imaginable—a top-tier CRM, custom dashboards, and high-level consulting reports—yet she still felt completely blind to what her customers would actually want a year from now. This is the great paradox of 2026: we have an infinite supply of data but a starving shortage of clarity. To bridge this gap, leaders must execute a cultural pivot that moves the focus from “what the data says” to “what the customer is feeling.” It requires moving away from filtered, sanitized reports and intentionally looking for the friction points and surprises that standard dashboards tend to smooth over. When you shift the culture to value the “why” behind the numbers, you start seeing the signals in the noise that indicate shifting market trends before they show up as a dip in your revenue.
Executives who personally engage with emerging technology are significantly more likely to succeed with innovation. Why is strategic knowledge insufficient compared to “visceral knowledge” gained by using tools firsthand? What specific tasks should a CEO perform to build this intuition without getting bogged down in technicalities?
Strategic knowledge is essentially learning from a distance, whereas visceral knowledge is something you feel in your gut because you’ve personally touched the process. Data suggests that C-level executives who are deeply and personally engaged with AI are twelve times more likely to be among the top 5% of companies winning with innovation. If a CEO only reads reports about AI, they are leading in a vacuum, much like a retail executive who never walks the floor of their own stores. To build this intuition, a CEO should start by taking a handful of raw customer complaints—perhaps the last 10 that came through the door—and personally use an AI tool to summarize them and identify patterns. They should try feeding the system their latest NPS verbatims to see if the AI picks up on emotional cues that their management team might have missed. By performing these small, tactile tasks, a leader learns where the technology hums with potential and where it fundamentally misses the mark, which is the only way to build the judgment needed for a true transformation.
Large-scale transformation roadmaps often stall while competitors make small, daily gains through experimentation. How does the “Atomic Habits” framework apply to building an AI-ready organization? Could you walk us through how a 1% daily improvement in data literacy actually compounds over a year?
The mistake most firms make is treating AI readiness as a massive, one-time project when it is actually a matter of systemic habits. As James Clear famously noted, you do not rise to the level of your goals; you fall to the level of your systems. If you commit to a mere 1% daily improvement in how your team interacts with data and AI tools, that progress compounds so significantly that you become 37 times more capable by the end of the year. Conversely, if you let those skills stagnate while the rest of the market moves forward, your competitive relevance effectively drops to zero. In practice, this means moving away from the 12-month roadmap that sits on a shelf and moving toward a culture where every employee is encouraged to spend a few minutes a day exploring a new tool or refining a data query. It’s the difference between a one-off “AI workshop” and a daily environment where experimentation is as common as checking your email.
While many firms feel their strategy is ready, most admit they lack the necessary infrastructure and talent. What daily practices can leaders implement to improve data quality and literacy? How can an organization shift from viewing data readiness as a one-time project to a recurring cultural discipline?
Data readiness is a muscle that must be exercised every single day, not a finish line you cross. We see that while 42% of companies believe their strategy is ready, a staggering 75% of data leaders admit their employees desperately need upskilling. A powerful way to fix this is to weave data into the very fabric of daily meetings, much like how the Managing Director at Canon Medical Systems ANZ mandated starting every meeting with a real customer story. You can evolve this by making it a habit to ask, “What data did we use to make this specific decision, and how fresh is it?” or “What is this data not telling us?” When you move the conversation from “cleaning the database” to “questioning the data in real-time,” you start to build what I call data muscle memory. This recurring discipline ensures that data quality becomes everyone’s responsibility rather than just a task for the IT department, leading to much more sustainable growth, similar to the 12.5% revenue increases seen in organizations that prioritize these behavioral shifts.
Many leaders intentionally slow down implementation due to fears of technical errors or hallucinations. What are the long-term risks of this “waiting for the right moment” approach? How can teams create a safe environment where they can fail small and iterate quickly without jeopardizing the brand?
The risk of “waiting for the right moment” is that the gap between you and the early adopters is compounding invisibly every single day, eventually becoming an unbridgeable chasm. While 60% of CEOs admit they’ve slowed down due to fear of errors, companies like Kodak and Nokia prove that the most dangerous strategy is the one that prioritizes safety over adaptation. The key is to create “safe-to-fail” zones where teams can experiment with AI on internal processes or non-critical tasks before any of it touches the customer. You have to encourage the mindset that the value isn’t in the first attempt—which will likely be imperfect—but in the fifth or tenth iteration. By celebrating the learning that comes from a failed experiment, leaders remove the stigma of error and allow their teams to find the breakthroughs that only come through trial. If you wait until the technology is 100% perfect, your competitors will have already built the cultural agility to move past you.
Roughly 80% of an initiative’s value comes from redesigning work rather than the technology itself. How should leaders restructure internal roles to support an AI-driven identity? Please share examples of how shifting from a “prescriptive” to a “volunteer” model encourages wider adoption across different departments.
Successful transformation requires shifting from a “we use AI” mindset to a “we are an AI-ready organization” identity. This involves moving away from top-down mandates and instead inviting the workforce to participate in the change, which is exactly what happened at Rosen Group. Rather than prescribing a solution, leadership presented transparent findings about their organizational challenges and simply asked, “What matters most?” This resulted in 34 volunteers from 14 different departments stepping up to drive change because they felt ownership of the outcome. When you allow employees to volunteer for AI task forces or experimental squads, you tap into a level of intrinsic motivation that a memo from the CEO can never achieve. This “volunteer model” ensures that the people who are actually doing the work are the ones redesigning it, which accounts for that critical 80% of value that tech alone cannot provide.
What is your forecast for AI leadership?
I believe we are entering an era where the divide between “AI-native” and “AI-lagging” organizations will be determined solely by cultural speed rather than technical budgets. My forecast is that by the end of 2027, the companies that thrive will be those where every leader, from the board to the front line, has developed a “visceral” understanding of AI through thousands of small, daily experiments. We will see a massive shake-up where 4% of firms—those that have truly integrated data literacy into their identity—will capture the vast majority of market value, leaving the rest to struggle with the high cost of catching up. The most successful leaders will be those who stop asking “How do we implement this tool?” and start asking “How do we build a system that learns as fast as the technology?” Ultimately, AI leadership will be less about the software you buy and more about the habits you build, specifically the habit of using every bit of data to make life tangibly better for the customer.
