Over the past year, generative AI (GenAI) has transformed how commerce teams work, streamlining content creation, enabling personalized customer experiences, and improving marketing efficiency.
Boston Consulting Group (BCG) reports that 75% of executives list AI as one of their top three strategic priorities. Yet only 25% say they’re seeing meaningful business value.
Why the gap? It’s largely due to the limited capabilities of existing AI—until now. We’re on the brink of something bigger: Agentic AI, a new generation of AI that doesn’t just assist, but acts.
From Tactical Assistant to Strategic Teammate
Historically, AI in e-commerce and retail has been used primarily for tactical improvements, like writing product descriptions, generating ad copy, and speeding up workflows. But when it comes to making real business decisions—like optimizing media spend, driving sales strategy, or generating executive-level insights—AI has fallen short. Large Language Models (LLMs), while powerful, often hallucinate or lack the specific context needed for accuracy in specialized domains like retail commerce.
Agentic AI represents the next stage of AI evolution, which will far surpass the limitations of previous generations.
AI agents (or agentic AI) are advanced systems that go beyond assisting with isolated tasks. They’re trained with deep, domain-specific knowledge and act autonomously to surface insights, recommend actions, and help business leaders make smarter decisions faster—essentially taking on some of the strategy work to make operational improvements. According to the same BCG report, two-thirds of companies are now exploring the use of AI agents that can act on their behalf. In retail commerce, this is already becoming a reality.
Solving Fragmented Workflows in Retail Commerce
One of the most persistent challenges in retail commerce is fragmentation. Teams often rely on disconnected tools to manage pricing, media, sales performance, and digital shelf visibility. This leads to redundant manual reporting, duplicated effort, and critical gaps in decision-making.
AI agents provide a solution by acting as orchestrators across these silos. When built with access to unified and contextual data, AI agents can find budget efficiencies, better utilize resources, distribute key takeaways in seconds, and align cross-functional teams around shared objectives.
For example, a media agent can continuously monitor keyword performance, correlate it with real-time sales data, and reallocate budget autonomously, reducing both the time required for analysis and the delays in execution. A sales agent might evaluate sales performance, identify gaps in the plan, and offer recommendations on how to make up the shortfall, all while staying within the boundaries of a brand’s strategic objectives. These kinds of real-time decisions can create huge strategic advantages and have a major impact on the bottom line.
However, these kinds of outcomes are only possible when AI agents are goal-oriented, built with an understanding of both the metrics that matter and the constraints that shape decisions.
Goal-Oriented and Cross-Functional by Design
The most effective AI agents aren’t narrowly focused on one task—they’re designed to achieve broader business objectives. This goal orientation is what distinguishes agentic AI from rule-based automation or single-task assistants. To be successful, agents must understand the interplay between their actions and the resulting outcomes.
For instance, increasing digital shelf visibility might improve product discoverability, but if inventory is constrained or pricing is misaligned, the lift in visibility won’t translate to sales. A truly functional AI agent must see the bigger picture, integrate signals across departments, and prioritize actions based on their impact on overarching business goals.
In practical terms, this means AI agents must be:
- Data-Integrated: Pulling from multiple internal and external data sources to understand the full business context.
- Role-Tuned: Designed with the workflows, KPIs, and constraints of specific functional roles in mind.
- Interoperable: Capable of coordinating with other systems and agents to align on goals.
A New Standard of Efficiency and Performance
Early adopters of agentic AI in retail commerce are already reporting measurable benefits. In our initial deployment of a new AI agent, 20 brands benefited from a reduction in reporting time of over 50%, hundreds of hours saved per quarter, and improvements in key metrics like incremental return on ad spend (iROAS) and digital shelf performance.
These efficiency gains aren’t just about speed—they’re about accuracy, consistency, and focus. By automating routine tasks and surfacing the most impactful actions, AI agents free up human talent for what they do best: strategic thinking, customer empathy, and creative problem-solving.
Preparing for an Agentic Future
Agentic AI is redefining what it means to lead in commerce. Just as algorithmic retail reshaped the industry in the early 2000s—with pioneers like Amazon using predictive analytics for inventory, basket analysis, and supply chain optimization—today’s winners will be defined by how effectively they deploy AI agents to run their business.
These agents don’t just summarize—they reason, optimize, and act, connecting the dots between digital shelf metrics, customer behavior, and sales outcomes. They run 24/7, surfacing the right decisions at the right time so your team can focus on what truly matters: strategy, creativity, and growth.
The next wave of commerce is here. And it will be the retailers and brands that adopt AI agents as part of their operating model that not only operate faster—they’ll operate smarter.
