Expert Mode: Navigating the Chasm Between AI Hype and On-the-Ground Reality
This article was based on the interview with Lena Moriarty, Head of Marketing at eTail by Greg Kihlström, AI and E-commerce thought leader for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
The return from any major industry conference, particularly one as sprawling as NRF, is always accompanied by a familiar sensation: a blend of inspiration, urgency, and a slight case of buzzword fatigue. Every year, a dominant theme emerges from the noise, and for the past few, that theme has been, unequivocally, AI. But this year feels different. The conversation has matured, evolving from a general, almost abstract discussion of “AI’s potential” into a far more specific, and frankly, more daunting, dialogue. The new term of art is “agentic AI,” and it represents a fundamental shift in how we must think about commerce, marketing, and the very nature of our customer relationships.
This isn’t just another incremental step in automation. Agentic AI promises a future where autonomous agents act on behalf of consumers, making purchasing decisions, negotiating prices, and navigating complex digital ecosystems without direct human intervention. As leaders, we’re faced with a critical tension. On one hand, the industry is buzzing with trillion-dollar investments and a technological arms race to build the dominant consumer agent. On the other, many of our teams are still grappling with the practical application of first-generation AI for tasks like content generation and SKU optimization. This creates a chasm between the future we’re being sold and the operational reality we manage daily. In a recent conversation with Lena Moriarty, Head of Marketing at the retail conference and community eTail, we dissected this very challenge, exploring how marketing leaders can bridge that gap and prepare for a future that is arriving faster than our current playbooks can account for.
From Task Automation to Strategic Agency
The first and most critical hurdle for marketing leaders is to grasp the conceptual leap from today’s AI to tomorrow’s. For the past 18 months, our focus has been on using AI for task-oriented automation—generating copy, optimizing product descriptions, and personalizing campaigns at scale. These are efficiency plays. Agentic AI is a strategy play. It moves beyond executing defined tasks to handling complex, multi-step processes with a degree of autonomy. This shift is not just a technological upgrade; it’s a paradigm shift.
Moriarty notes that this was the dominant conversation on the ground at NRF, a clear signal of where the industry’s thought leaders are focused.
“A lot of dominated conversation was about task-oriented AI and like moving away from the task automation to smarter, replacing more complexities within your day-to-day. And I think that whoever is cracking that code is really coming out on top and saving that time. And not just cracking that code, but also getting their talent and employees to adopt that AI proficiency, that AI use.”
Her point about adoption is key. It’s one thing for a vendor to announce an agentic platform; it’s another entirely to integrate it into your marketing stack and, more importantly, into the workflows and skillsets of your team. This is where the disconnect often occurs. While the tech world is racing toward full agency, many marketing departments are still working to build foundational “AI proficiency.” Leaders must recognize this. The challenge isn’t just to buy new technology, but to cultivate a culture of continuous learning that can evolve alongside the technology. It’s no longer enough to have a few AI power-users; the entire organization needs to develop a baseline competency to even begin contemplating the complexities of an agent-driven world.
The Uncomfortable Reality of the Implementation Gap
Let’s be candid. While the keynote stages are filled with visions of autonomous shopping agents, the daily reality for most e-commerce and marketing teams is far more grounded. The immediate, tangible applications of AI are still centered on optimizing existing processes, not inventing entirely new ones. This isn’t a failure; it’s the natural progression of any transformative technology. However, it’s crucial for leaders to have a clear-eyed view of where their organization truly stands on the adoption curve.
Moriarty provides a healthy dose of realism, highlighting where she sees most retailers actually applying AI today, which serves as a valuable benchmark for the rest of us.
“What I think that I’m seeing the most retailers and e-commerce experts adopt it is things like optimizing SKU pages and making sure that all of your description pages are have no bugs, are matching up… which is still very task-oriented, you know what I mean? And I think that like where we’re seeing a lot of beginner dabbling is the personalization at scale… Again, I think that there is a big knowledge gap that we’re wanting to fill with where we’re actually using AI in the day-to-day and where we want to be using AI in the day-to-day.”
This “knowledge gap” is the strategic minefield leaders must navigate. There’s immense pressure from boards and the market to be “doing AI,” but misallocating resources on advanced, agentic solutions before mastering the fundamentals is a recipe for wasted investment and team burnout. The prudent path involves a dual strategy: first, relentlessly optimize the “here and now” with task-oriented AI to drive measurable efficiency and ROI. Use these wins to build credibility and internal expertise. Second, dedicate a separate, focused effort—a “skunkworks” team or a strategic task force—to explore and pilot the next wave of agentic technologies. This approach allows you to deliver immediate value while simultaneously preparing for the inevitable disruption ahead, without confusing the two missions.
The Crumbling Funnel and the Future of Measurement
For decades, the marketing funnel, in its various incarnations, has been our north star. It provided a logical, linear framework for understanding the customer journey and measuring our effectiveness at each stage. Agentic commerce doesn’t just add a new stage to the funnel; it shatters the entire construct. When an AI agent can go from need-identification to price comparison to purchase in a fraction of a second across countless channels, our traditional models of awareness, consideration, and conversion become almost meaningless.
This forces a difficult but necessary conversation about measurement and KPIs. If the buyer’s journey is no longer human-led and observable through conventional analytics, how do we measure success? Moriarty astutely points to the cyclical debate around the funnel and how new technologies are finally forcing the issue.
“I do think that the typical KPIs are going to be harder to follow because you’re going to have people bouncing around channels like crazy… And then now when we’re throwing the ability to live shop with personalized recommendations from an agentic AI assistant, that really throws a wrench in it, too.”
The “wrench” she describes requires a new toolbox. Marketers will need to shift their focus from tracking linear paths to influencing the algorithms and data sets that power the agents themselves. Success might be measured less by click-through rates and more by your brand’s “discoverability score” within an agent’s ecosystem, the quality of your product data feeds, or your ability to win in an automated, agent-to-agent negotiation. This is a profound shift from demand generation to data optimization. Leaders must begin asking their teams and technology partners new questions: How do we ensure our brand is a preferred choice for an AI shopping assistant? What data infrastructure do we need to provide agents with the real-time information they require? Answering these questions now will determine who wins in a world where your best customer may no longer be a person, but a piece of code acting on their behalf.
The transition to an era of agentic commerce is not a distant sci-fi concept; it is the next strategic frontier for every marketing leader. The conversations happening at industry bellwethers like NRF and ETail are no longer theoretical. They are the early tremors of a seismic shift that will redefine the rules of customer engagement and digital marketing. The path forward requires a delicate balance of pragmatism and ambition. We must continue to master the tools of today—driving efficiency and proving value through task-oriented AI—to earn the right to invest in the technologies of tomorrow.
The ultimate challenge lies in building organizational agility. It means fostering a culture that is comfortable with ambiguity and experimentation. It means investing in talent that can bridge the gap between marketing creativity and data science. And as Moriarty suggests, it means being brutally honest with ourselves and our technology partners about our goals and capabilities. The leaders who succeed will not be those who simply buy the latest AI platform, but those who build an operating model that can adapt as the very definition of a “customer” evolves. The agent is coming; the only question is whether we’ll be ready to engage with it.
