This article was based on the interview with Sai Koppala, CMO 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 any enterprise marketing leader, the last few years have felt like a relentless stress test. Economic headwinds, from tariffs to inflation and supply chain snarls, have put unprecedented pressure on the P&L. The traditional playbook in these situations is well-worn and, frankly, uninspired. It presents a binary choice: either pass rising costs directly onto consumers and risk alienating your most loyal customers, or absorb the hit and watch your margins evaporate. These are blunt instruments for a complex problem, forcing brands into a reactive crouch when agility and foresight are what’s truly required.
The conversation in the C-suite is shifting, however, and it’s being driven by a more intelligent, third path forward. This path isn’t about choosing between the consumer and the bottom line; it’s about using artificial intelligence to optimize for both simultaneously. This isn’t the speculative AI of keynote speeches, but the practical, in-the-trenches application that transforms data from a rearview mirror into a predictive guidance system. As we’ll explore with insights from Sai Koppola, CMO of CommerceIQ, leading brands are moving beyond the old playbook. They are leveraging AI to navigate economic uncertainty with a new level of precision—optimizing pricing, media spend, and operations not just to survive, but to build a more resilient and profitable commercial engine for the future.
The New P&L: From Price Hikes to Precision Profitability
When costs rise, the knee-jerk reaction is to raise prices across the board. It’s simple, it’s direct, but it’s also a remarkably clumsy way to manage profitability. It ignores the nuances of customer segments, price elasticity, and competitive positioning, treating all products and all shoppers as a monolith. The reality is that your customer base is not a single entity; it’s a collection of diverse segments with varying degrees of price sensitivity. AI is enabling brands to finally operate with this level of granularity, effectively rewriting the P&L playbook from one of broad strokes to one of surgical precision.
This involves unifying disparate data sources—retailer sales data, operational metrics, media performance, and real-time shopper signals—to build a dynamic understanding of the market. Instead of guessing where price increases will be tolerated, brands can now identify specific SKUs and customer segments with higher price elasticity and act accordingly, while protecting price points on more sensitive items. As Koppola explains, this isn’t theoretical; it’s happening now.
“If you actually look at what’s happening over the last year, we’re actually seeing brands rewrite the P&L playbook using AI. It’s not just how can you find profit with precision and not necessarily just price hikes… If you look at when we did analysis over by price bands, we looked at any category, like let’s say toys… the higher price bands, the prices have gone up like 10% plus over the last year. So significant price increase. But on the lower end, you are not seeing a very flat pricing… because we know the consumer is having difficulty at the bottom end.”
This is a fundamental shift. It’s the difference between using a sledgehammer and a scalpel. By applying AI, brands can protect margins where the opportunity exists (premium products) without penalizing price-sensitive shoppers who are crucial for volume (essentials). The same logic applies to the cost side of the ledger. Rather than making broad cuts, AI can identify inefficiencies with pinpoint accuracy. Koppola points to retail media spend as a prime example, where brands can use AI to analyze their organic search performance on a retailer’s site and automatically pull back paid spend on terms where they are already ranking highly, reallocating that budget to where it can have a greater incremental impact. This is the new P&L: a dynamic, intelligent system that continuously optimizes for profitable growth, not just growth at any cost.
The End of the Dashboard: From Reactive Analysis to Automated Action
For years, the promise of data was delivered in the form of a dashboard. We were told that if we could just visualize enough metrics on a single screen, we would unlock untold insights and make better decisions. The reality for most marketing leaders and their teams has been a state of perpetual analysis paralysis. You can have the most beautifully designed dashboard in the world, but you are still fundamentally looking at the past. By the time an analyst spots a trend, builds a report, and presents it for a decision, the market has already moved on. The time lag between insight and action is where margin is lost and opportunity is squandered.
The next evolution of commerce operations moves beyond this reactive model. The concept of an “AI teammate” is emerging, shifting the focus from staring at data to approving automated, data-driven actions. This approach recognizes that for a brand with hundreds of SKUs across dozens of retailers, manual optimization is a mathematical impossibility. AI is uniquely capable of managing this scale, monitoring thousands of signals in real-time and flagging not just problems, but recommended solutions.
“The way we build the systems is AI makes the recommendations. The human can then approve those recommendations to automate that. So we give the flexibility for brands to make the right decisions because I still believe you need, in many cases, human in the loop. But take the grunt work out of having to do those things.”
This “human-in-the-loop” model is critical. It’s not about handing the keys over to a black box. It’s about elevating the human role from tactical execution to strategic oversight. Consider an out-of-stock situation. The old way involves an analyst noticing a dip in sales on a dashboard, investigating the cause, and then manually pausing the associated ad campaigns. The new way involves an AI teammate that automatically detects the out-of-stock SKU, recommends pausing the media spend, and presents that decision for a one-click approval from a human operator. The team’s time is freed from chasing data to focusing on the strategic implications, a far more valuable use of their expertise. This shift transforms the operating rhythm of a commerce team from a weekly or monthly reporting cycle to a continuous, real-time optimization engine.
The Future is Collaborative: Agentic AI in the Commerce Ecosystem
Looking ahead, the impact of AI will extend beyond the four walls of a single brand. The next frontier lies in creating a more streamlined, intelligent, and automated collaboration between brands and their retail partners. The current process for joint business planning and execution is often mired in manual processes, spreadsheets, and PowerPoints. It’s slow, inefficient, and fraught with misaligned data and objectives. AI is poised to fundamentally reshape this relationship, moving it from a series of periodic, high-friction negotiations to a state of continuous, data-driven alignment.
The emergence of “agentic AI”—AI systems capable of performing complex tasks and interacting with other systems autonomously—will be the catalyst for this change. Imagine a future where a brand’s AI agent and a retailer’s AI agent can communicate and execute tasks together, governed by the strategic parameters set by their human counterparts. This isn’t science fiction; it’s the logical extension of the automation already taking place.
“I foresee a world where that collaboration becomes much faster and streamlined, where you have agents on the retailer side, you have agents on the brand side, and they will work together… The AI on the brand side will identify here are the 10 things you need to fix to improve your conversion on the retailer website… once they’re approved, it automatically hits the retailer site. On the retailer site, the retailer agent goes and updates the website. That’s where we’re headed.”
This vision represents a profound efficiency gain for the entire retail ecosystem. Tasks that currently take weeks of back-and-forth communication—like optimizing a product detail page for trending keywords—could be executed in minutes. For retailers operating on razor-thin margins and brands fighting for every basis point of profitability, this level of operational efficiency is not just a nice-to-have; it’s a competitive necessity. It allows both parties to focus less on the mechanics of execution and more on the strategic initiatives that drive mutual growth.
Summary
The narrative is shifting. For too long, technology in marketing has been about adding more complexity—more channels, more data, more dashboards. The intelligent application of AI represents a welcome reversal of that trend. It’s about using technology to absorb complexity, automating the tactical drudgery so that human talent can be focused on what it does best: strategy, creativity, and collaboration. The move from blunt price hikes to precision profitability, from reactive dashboards to proactive AI teammates, isn’t just an incremental improvement. It is a foundational change in how successful commerce organizations will operate.
As leaders, our role is not to become AI experts, but to understand its strategic potential and to cultivate teams capable of thriving in this new environment. As Koppola notes, this means fostering a culture of experimentation and recruiting for a “growth mindset.” The core competency is no longer the ability to manually pull levers, but the strategic judgment to know which automated workflows to approve and the curiosity to constantly seek out new efficiencies. The future of commerce won’t be run by algorithms alone, but by the leaders who understand how to orchestrate them to build smarter, more resilient, and ultimately more profitable brands.
