Consumer-packaged-goods (CPG) businesses are leaving growth on the table. Many are already turning to agentic AI, but mostly to cut costs and speed up existing work. Investors in CPG companies tend to reward top-line growth more than they reward margin expansion, and the opportunity to recapture that growth lies in the increasingly complex relationship between CPG brands and retailers.
A sales relationship that was built around annual negotiations and a promotional calendar now runs continuously, spanning retail media, e-commerce, pricing, promotions, assortment, and in-store execution. Growth depends on coordinating across these levers, rather than on optimizing each independently: A retail media campaign built around a promotion loses most of its punch if the promoted item is out of stock in store; a deep discount can draw shoppers to the shelf, but if the promoted item isn’t stocked, they leave with a competitor’s product instead. Few CPG commercial organizations are equipped for this kind of coordination: Data sits in disconnected systems, and planning still runs on cycles set months in advance.
Agentic AI is the clearest path to closing that gap, but only if it’s deployed across the full commercial system, including innovation, marketing, revenue growth management (RGM), and execution, and is used primarily as a growth accelerator rather than a productivity tool. Are CPG companies ready to harness agentic AI’s power? In this article, which draws on a survey of 41 senior commercial leaders in the United States and Europe about their current adoption of agentic AI,1 we describe what an agentic future could look like for CPG commercial organizations and how they can seize the opportunity.
AI can free up CPG commercial teams to do higher-value work
Commercial teams are spending too much of their time on work that doesn’t require their expertise. In our survey, more than half said drafting a customer plan takes over five days; nearly all (38) said end-to-end approval cycles take more than three weeks. Much of that time goes to data gathering, reconciliation, reporting, and deck creation, as well as pricing analysis, promotional planning, and scenario modeling. Leaders identified many of these activities as prime opportunities for AI to cut manual effort and improve decision-making (Exhibit 1).
Data gathering, pricing and margin management, and promo planning offer the strongest combination of time savings and quality uplift.
1Impact reflects estimated benefit only during the period in which the use case was active; it should not be interpreted as sustained weekly impact.
2Customer relationship management.
Overall, commercial teams spend too much time assembling information and too little time collaborating with retailers on category strategy, innovation planning, negotiation, and execution, all of which directly impact CPG companies’ growth.
So far, AI has made limited inroads into this work. Only 12 of the 41 respondents have embedded AI into core workflows or scaled it across their organizations; the rest are stuck at the ad hoc or pilot stages. Leaders see AI’s potential: Respondents rank customer planning, sell-in strategy development, pricing analysis, and scenario modeling among AI’s highest-value opportunities. But most commercial organizations are trying to layer AI onto data that is fragmented, inconsistent, or simply not connected across functions.
Where the foundation exists, the payoff is real. In performance management, for example, AI can track KPIs, detect variances, and recommend actions based on what is driving performance—cutting the human effort required by roughly half and freeing up commercial teams to interpret insights, shape retailer conversations, and act on growth opportunities.
CPG companies have used tech tools for years. What’s new with agentic AI is that these tools no longer have to operate in isolation; agents can orchestrate decisions across the full commercial system. Getting there means moving past quarterly forecasts and static plans toward dynamic, store-level decisions made with real speed and precision, and letting commercial teams spend more of their time on retailer partnerships, category strategy, white space opportunities, and execution in the moments that matter most.
What does a day in the life of the agentic-empowered commercial team look like?
Bringing the power of agentic AI to a CPG organization’s commercial function changes how decisions are made across the business. Consider, for example, a team responsible for beverages at a large grocery retailer (Exhibit 2).
In the agentic-AI-enabled workday, AI handles the busywork, while people make the judgment calls.
The day might begin with a commercial review. Instead of each function arriving with its own deck, spreadsheet, and recommendation, the team starts from a shared AI-generated view of the business. Overnight, agents have synthesized retailer performance, promotional results, inventory positions, digital-shelf metrics, competitive activity, and field execution data into a small set of prioritized opportunities and risks. One alert shows category sell-through at a key retailer running below plan. The system traces the decline to a combination of out-of-stocks in several distribution centers and a competitor promotion running in the same aisle, then recommends a fix: shift inventory from a distribution center running a surplus to the three stores hit hardest by the out-of-stocks, and counter the competitor’s promotion with a buy-one-get-one offer on the flagship SKU, projected to recover most of the lost volume within two weeks.
The discussion that follows focuses on how commercial experts might challenge the assumptions behind the promotion recommendation; shopper-marketing specialists might explore whether a retail media investment could amplify it; and supply teams might test whether inventory can support the plan. Rather than scheduling follow-up meetings to reconcile competing analyses, the team interrogates the agent directly, asking it to simulate alternatives and quantify trade-offs across volume, margin, and execution risk. The team might, for instance, compare whether shifting funds from a discount promotion to a value-pack offer would drive stronger growth or whether that money would be better spent on a retail media buy.
By midmorning, the focus shifts outward. The key account manager (KAM) walks into the retailer meeting with a deeply informed, cross-functional point of view. If the retailer pushes for a deeper discount on a flagship product, the KAM can evaluate the potential volume uplift or margin impact on the spot. If the retailer proposes expanding the distribution of a new item, the system can immediately assess demand and inventory requirements. Decisions made midconversation flow into forecasts, promotional plans, and store-level actions automatically—no additional round of meetings required.
The rest of the day is no longer dominated by status meetings and email chains. Instead, the team turns to the handful of decisions that actually require human judgment: how to respond to a competitor’s move or how to balance a retailer’s request against broader portfolio goals. When a competitor launches a new product, for example, the team gets a rapid read on likely share impact by shopper segment, say, a 4 percent share loss concentrated among value shoppers, along with a set of response options to weigh, such as a price adjustment on the closest-matching SKU or a bundled offer aimed at that segment.
In this model, commercial teams spend more time exercising judgment and less time assembling the case for it. The KAM’s role shifts from process manager to retailer strategist and negotiator, backed by more real-time information than ever (see sidebar, “Agentic AI in joint business planning”). RGM specialists become category and portfolio stewards rather than spreadsheet and presentation builders. Commercial leaders spend their time setting guardrails, resolving exceptions, and shaping long-term growth rather than reconciling competing analyses.
Over time, the boundaries between these traditional commercial roles may blur further. Expertise will still matter, but teams will increasingly organize around solving retailer problems rather than executing functional handoffs.
To recapture growth, CPG commercial leaders should prioritize five actions
So what should a commercial leader actually do? Five priorities matter most:
- Manage the parts of the retail sales funnel as one. Consolidate retail media, digital-shelf, and in-stock management under one owner and budget for each key retailer, instead of running them as three separate jobs. That requires treating product content and search ranking at each retailer as seriously as shelf placement in a physical store: audit how products surface for key search terms, fix content gaps that hurt rankings, and coordinate with retail media teams. Geo-targeting and the ability to shift media spend in real time matter, too. And make sure that product content and pricing work for both shoppers and the algorithms influencing what they see and buy.
- Make RGM and customer planning more dynamic and granular. Most promotion, assortment, and pricing decisions still run on a fixed calendar, even as retailer and shopper conditions change. Start with the one or two decisions that create the most value: promotion spend allocation and store cluster assortment are usually the best candidates. Use real-time retailer point-of-sale and inventory data to inform those decisions, set guardrails so the system can act without a full reapproval cycle, and move the review cadence from quarterly to weekly or biweekly.
-
Replace periodic store audits with continuous monitoring. Most commercial organizations still catch out-of-stocks, distribution gaps, and display compliance failures on the next store visit or the next after-the-fact review—by which point the sale is already lost. Use AI to flag these exceptions continuously, and route them straight to whoever can fix them: field teams, brokers, distributors, or the retailer itself.
If you run field sales or direct-store delivery, start with AI-enabled outlet ordering, route optimization, and compliance monitoring. If you don’t, start by improving in-store visibility and narrowing the gap between what a store sees and what the commercial team does.
-
Prepare your people for AI on both sides of the negotiating table. Retailers are already using AI in negotiations with suppliers, and KAMs should expect an AI counterpart across the table as often as a human one in the years ahead. Getting your own teams ready starts with change management on day one, not after the tools are live.
One big part of change management is harnessing bottom-up momentum. Commercial employees may already be experimenting with AI on their own. Identify the employees using it well and give them time to mentor peers (in our experience, AI transformations that directly involve a meaningful share of the workforce see better results than ones run top down). Build people’s skills before rolling out a new workflow, and bake the outcomes that AI use can enable (such as faster customer plans or fewer missed executions) into performance reviews and promotion criteria so the change actually sticks.
- Invest in data and technology. The previous four actions each depend on having data that most CPG companies don’t yet have connected, such as sales attributed to retail media networks, retailer point-of-sale feeds, inventory positions, and digital-shelf metrics. That’s not a problem for a chief technology officer to solve alone. Since commercial leaders control which retailer data feeds and use cases get funded, they should put money and head count behind cleaning, connecting, and analyzing the two or three sources tied to their highest-value decisions before chasing more use cases. Pair a commercial owner with the technical team behind each source, so the work stays scoped to a business decision.
One question will follow commercial leaders through all of this: What’s the ROI? Some use cases, such as field execution and outlet ordering, can show results in weeks. Others, such as customer planning and retailer negotiations, take longer to prove out. Time savings are one measure of the returns, but for CPG companies, the real payoff from agentic AI will be measured in growth that comes from serving shoppers better: the products they want, in stock and fairly priced, wherever they shop.
.jpg?cq=50&mw=767&car=16:9&cpy=Center)