Expert Mode: Beyond Insight—The Rise of Agentic AI in Commerce Execution
This article was based on the interview with Bill Schneider and Himanshu Jain, at CommerceIQ by Greg Kihlström, AI and MarTech keynote speaker for The Agile Brand with Greg Kihlström podcast. Listen to the original episode here:
For years, we’ve been told that data is the new oil. We’ve invested millions in dashboards, analytics platforms, and data science teams, all in pursuit of the perfect insight. We’ve succeeded, to a point. Most enterprise marketing leaders I speak with aren’t suffering from a lack of information. They know which competitor is eating their lunch on a key search term, they know which product pages are underperforming, and they know a holiday weekend is an opportunity to curate a unique customer experience. The problem isn’t the knowing; it’s the doing. Recent research from Commerce IQ confirms this, noting that a staggering 80% of commerce leaders feel overwhelmed by data. The true bottleneck in modern e-commerce isn’t strategy—it’s the sheer operational drag of execution.
This gap between insight and action is where the most well-laid plans fall apart. It’s the chasm between a brilliant idea in a Monday morning meeting and its eventual, often diluted, implementation weeks later. The manual processes, siloed systems, and agency lead times required to update hundreds of product detail pages (PDPs) or adjust media bids across a dozen retailers create a state of perpetual catch-up. Into this operational quagmire steps a new paradigm: agentic AI. This isn’t just another flavor of automation. It’s a foundational shift from tools that follow rigid rules to intelligent agents that can plan, reason, and execute against business objectives. It’s about empowering your expert teams by handing off the tactical execution, allowing them to operate at the speed their insights demand.
From “If-Then” Automation to Strategic Planning
The term “automation” has been part of the marketing lexicon for decades, often referring to simple, rules-based workflows. If a competitor drops their price, you match it. If a keyword’s cost-per-click hits a ceiling, you pause the campaign. This is useful, but it’s a blunt instrument in a world of algorithmic retail. Agentic AI operates on a completely different level, moving from simple triggers to sophisticated, goal-oriented planning. It doesn’t just ask “what happened?” but “does it matter, and what is the optimal response given our strategic goals?”
Himanshu Jain, co-founder of Commerce IQ, breaks down this critical distinction with a practical example of a competitive price change. This is the difference between a knee-jerk reaction and a considered, strategic response executed in milliseconds.
“Automation is basically following the series of steps. If X happens, do Y… a dumb system will say they reduced the price, let me also price match them. Versus a agentic system will first say, does that even matter? The 30 cent price. Is that even the right competitor for me?..They also have memory, so what happened when last time this competitor dropped price? Did it actually affect my sales or not? Then they will actually look at 10 different systems…they will check the inventory system to say, if I do a price cut, do I have enough inventory? Do I have enough margin?..And then they will look at your business strategy. Am I here to increase my cash flow? Am I here to gain market share? And based on that, they will create a plan. And then they will simulate that plan.”
This is the crux of the agentic shift. An AI agent acts less like a simple script and more like a tireless junior analyst with access to every data silo in your organization. It contextualizes an event (a 30-cent price drop) against historical data, inventory levels, margin requirements, and overarching business strategy. It then game-plans multiple scenarios and presents a recommendation, complete with the supporting data. The human expert is then freed from the data-gathering and analysis and can apply their judgment to the final decision. This elevates the technology from a simple task-doer to a strategic partner, one that understands that not every action is the right action.
Unlocking the Long Tail of Opportunity
Every enterprise leader is familiar with the 80/20 rule. We focus our resources on the top 20% of SKUs and the top few retail channels that drive 80% of the revenue. It’s not an ideal strategy, but a pragmatic one born of limited human bandwidth. There simply aren’t enough hours in the day to give the same level of attention to your 500th best-selling product as your top five. This is where agentic AI doesn’t just make your team faster; it fundamentally changes the scope of what’s possible. By handing off execution to agents that can operate 24/7 across an entire catalog, brands can finally address the massive, untapped opportunity in the long tail.
This applies not only to products but to retail channels as well. The cost to win a keyword on a top-tier retailer might be astronomical, while the same opportunity on a smaller, regional platform could be far more profitable. Bill Schneider, VP Product Marketing at Commerce IQ, points out that this unlocks a new frontier of growth that was previously too resource-intensive to pursue.
“It’s also long tail SKUs, long tail retailers,” adds Himanshu Jain. “If the teams are focused just on top two retailers, what about retailer number three, four, five, six, seven that combined generate about 50% of the revenue?..If winning a bid on snack keyword on an Amazon or Walmart might cost you $10 per click. Versus on a Hy-Vee or an Ahold, it might only cost you two bucks. So when you’re spending that incremental dollar, where should you spend? Is a decision most brands and agencies are not making because they can’t go outside the top two retailers.”
This is a powerful concept for any marketing leader. It suggests that significant growth may not come from squeezing another half-percent of efficiency from your top channels, but from systematically activating the 80% of your portfolio and channels that have been on autopilot. Agentic systems can constantly monitor performance, optimize content, and manage bids across thousands of SKUs and dozens of retailers simultaneously. This isn’t just an incremental improvement; it’s a re-evaluation of your entire commercial strategy, turning what was once an acceptable loss into a new engine for growth.
The New Role of the Human Expert: From Doer to Director
The conversation around AI invariably turns to its impact on our teams. The doomsday scenarios are as common as they are unhelpful. The reality is far more nuanced and, frankly, more interesting. Agentic AI doesn’t make human experts obsolete; it makes their expertise more valuable than ever. The focus of their work shifts from manual execution—the endless cycle of logging into systems, pulling reports, and making updates—to strategic direction, oversight, and judgment. The most effective way to think about this new working relationship is to view the AI agent as a new member of the team.
Himanshu Jain offers a compelling analogy that demystifies this process, framing the AI agent as an intern or junior analyst who needs to be onboarded, trained, and managed.
“Agents are at a level of an intern to a junior analyst. So, when you hire an intern or a junior analyst, what do you do first? You say, look, let me onboard this person in one specific task…You provide the business context…you are teaching the that intern for few months…The same way you onboard an agent. You teach that agent on your business context, you tell him this is how I make decision…Now, once an analyst is onboarded, then for the first few weeks or months, you check its work…then you say, okay, 80% of that by within this threshold, you can make your own decision and act independently, 20% you do all the work, but give me the final output, so I will apply my own judgment.”
This model reframes the human role as that of a director. Your team’s value is no longer in their speed at performing a task, but in their ability to impart business context and strategic wisdom to the agent. Expertise in a particular domain becomes the critical skill, as it’s needed to train the AI, validate its outputs, and handle the high-stakes exceptions that require human judgment. This shift frees up your best people to focus on uniquely human tasks: negotiating with retail partners, developing creative brand strategies, and mentoring the next generation of leaders. The future of work in a world of agentic AI isn’t about humans competing with machines, but about experts orchestrating them.
Closing the Execution Gap
For too long, the operational friction of commerce has been a silent tax on our strategic ambitions. We have been armed with terabytes of insight but constrained by the human capacity for execution. This has created a reality where we are forced to prioritize the critical few and leave vast opportunities on the table. The emergence of agentic AI represents the most promising solution to this enduring challenge. It provides the operational horsepower to finally close the gap between knowing what to do and actually getting it done, at scale, across the entirety of your business.
This is more than a technological evolution; it’s an organizational one. The brands that will lead in this new era are not necessarily those with the most data, but those that can act on it most effectively. This requires a shift in mindset—viewing AI not as a tool to be piloted in isolation, but as a core component of the team that needs to be managed, trained, and directed. The future belongs to the leaders who empower their human experts to transition from doers to directors, orchestrating a suite of intelligent agents to execute with a speed and precision that was previously the stuff of science fiction. The execution bottleneck is finally breaking, and the opportunities for those who move quickly will be immense.
