As an e-commerce strategist with deep roots in customer engagement and operations management, Zainab Hussain has spent years helping brands navigate the messy intersection of technology and human behavior. She understands that in the current retail climate, the buzz around artificial intelligence is no longer just white noise; it has become a relentless drumbeat that leaders cannot ignore. In this conversation, Zainab breaks down why the industry is feeling a massive sense of urgency, the structural hurdles standing in the way of true innovation, and how the role of the retail professional is being fundamentally reshaped by machines that don’t just calculate, but decide.
Our discussion explores the growing tension between the 91% of retail leaders feeling pressured to innovate and the much smaller fraction who actually possess a roadmap for success. We touch on the readiness of the modern workforce to handle autonomous systems, the starkly different perspectives held by data officers compared to those on the front lines of digital sales, and why the first wave of implementation is hitting customer service and inventory management so hard. Zainab also highlights the critical difference between mere automation and strategic optimization, arguing that the future of the industry lies in a partnership where AI provides the reasoning and humans provide the commercial judgment.
With over 90% of retail leaders feeling immense pressure to adopt AI, yet less than half having a defined strategy, what do you believe is causing this significant gap between ambition and execution?
This disconnect stems from a high-stakes environment where 91% of retailers feel moderate to significant pressure just to stay in the game, often before they truly understand the rules. While 53% of these leaders identify AI as a top priority, they are frequently reacting to the 39% of their competitors who are already shifting their roadmaps, leading to a “ready-fire-aim” mentality. We see 42% of organizations identifying potential use cases but stalling because they cannot pinpoint the actual commercial value those projects will deliver. It is a stressful period where the desire to innovate is palpable, yet only 46% have managed to ground that enthusiasm in a well-defined strategy supported by clear value cases. This gap exists because it is far easier to buy a piece of software than it is to build the governance and decision-making frameworks required to make that software actually profitable.
The research highlights that only 27% of retail teams are fully prepared for agentic AI deployment. What are the most common organizational roadblocks you see that prevent a workforce from being ready for this transition?
The most glaring roadblock is that many organizations have spent years optimizing for human-led reporting rather than machine-led action, leaving a staggering 5% of retailers admitting they are not ready at all. When you consider that a quarter of the workforce is only “somewhat” ready, you realize there is a profound lack of the skills and operating models needed to support autonomous systems. Retailers are currently struggling to develop the necessary governance frameworks to ensure that when AI begins making day-to-day decisions, it does so within safe, brand-aligned parameters. There is also a sensory and emotional component here; employees often feel a sense of trepidation about their roles changing, which is why 2026 initiatives are focusing so heavily on the human-in-the-loop oversight model. Transitioning to a model where 83% of leaders expect AI to lead or automate decisions within the next year requires a level of trust and technical literacy that most legacy teams simply haven’t had the time to develop yet.
It is striking that only 9% of eCommerce Directors expect AI to automate most decisions, while 42% of Chief Data Officers are much more optimistic. Why do you think there is such a sharp divide in perspective between these roles?
This divide is a perfect reflection of the difference between those who manage the data and those who live with the consequences of its errors. eCommerce Directors are often the ones on the hook for conversion rates and brand reputation, so their caution—with only 9% favoring full automation—comes from a place of protecting the customer experience from potential “black box” glitches. On the other hand, 42% of Chief Data Officers and 35% of Chief Customer Officers see the efficiency gains on a macro level and are eager to move beyond simple reporting tools. The eCommerce team feels the visceral impact of a botched automated promotion or an incorrectly priced item, making them much more likely to support the 50% of leaders who want AI to lead decisions while humans provide the final strategic direction. This tension is actually healthy; it ensures that while the organization pushes for speed, the front-line experts are keeping a steady hand on the steering wheel to maintain brand integrity.
Customer service and inventory management are identified as the primary areas for agentic AI adoption. What makes these specific functions so attractive for early-stage automation compared to other departments?
These functions are the “engine room” of retail, where repeatable, high-volume operational tasks can be easily quantified and handed over to a machine. Currently, 42% of retailers see customer service as the top candidate because AI can handle the exhausting, routine nature of inquiries with a speed no human can match. Similarly, 37% are looking at inventory and replenishment because the technology excels at processing the vast data sets required to prevent stock-outs or overstock situations. These areas allow a business to turn AI into measurable business outcomes quickly, providing a proof of concept that can then be used to justify further investment. By automating these “heavy lifting” tasks, retailers can prove the technology’s reliability in a controlled environment before moving it into more nuanced areas like creative marketing or high-level strategy.
You have mentioned that the future of retail isn’t about AI replacing people, but rather providing the reasoning to help them make better decisions. How do you see this collaborative model working in high-stakes areas like pricing and loyalty?
In areas like pricing, promotions, and loyalty, the goal is to shift from basic automation to deep optimization where AI handles the millions of data permutations while the human provides the commercial context. AI is exceptionally good at identifying patterns, but it lacks the “commercial judgment” to know how a local community event or a sudden social trend might shift a customer’s emotional response to a discount. We are moving toward a model where the machine provides the insights and reasoning—acting as a decision-making partner—but the final call remains with the person who understands the brand’s soul. This approach creates a powerful synergy; it allows the 33% of retailers who want to automate trading to do so while still maintaining the “human-in-the-loop” safety net that 50% of the industry still demands. Ultimately, the winners will be the ones who use these tools to make better decisions faster, rather than those who simply try to remove the human element entirely.
What is your forecast for the retail AI landscape over the next two years?
Over the next two years, we will see the “innovation gap” widen significantly between retailers who have embedded AI into their operating models and those who are merely experimenting with it as a reporting tool. By the time we reach the 2026 awards cycle, the 83% of leaders who expect AI to lead or automate decisions will have either achieved a seamless “human-plus-machine” workflow or they will be struggling with fragmented, ineffective systems. I expect to see a massive shift in investment away from generic AI tools and toward bespoke governance frameworks that allow for agentic AI to handle the 42% of customer service tasks and 37% of inventory decisions with minimal supervision. The ultimate success stories will not come from the companies with the most expensive technology, but from those that have successfully retrained their workforces to move from being “doers” to being “strategic overseers” of an automated ecosystem. The retail world is moving past the stage of needing to be convinced that AI matters; the next 24 months will be a race to build the capability to actually make it work at scale.
