How can retailers turn AI agents into real profit drivers?
These tools let retailers adjust prices and refine promotions in near real time.
Retailers should rethink how their merchandising teams work as so-called agentic artificial intelligence (AI) systems move from experimentation into day-to-day operations.
Unlike traditional analytic tools, agentic AI is built to plan, act and learn on its own. In merchandising, that could let retailers adjust prices, fine-tune promotions and rebalance product assortments in near real time, replacing the static weekly or monthly review cycles that still dominate many organisations, according to consulting firm McKinsey & Co.
“Historically, the merchant role requires spending critical time and energy on manual, repetitive tasks,” it said in a January report.
“By offloading it to AI agents, merchants could reclaim up to 40% of their time to do what they do best: focus on strategy, find great products, understand customers, and optimise vendor negotiations,” it added.
Yet most retailers are not ready to deploy the technology at scale.
McKinsey found that 71% of merchants said AI merchandising tools have had limited or no impact on their business so far, whilst 61% said their organisations were only slightly prepared, or not prepared at all, to scale AI across merchandising functions.
One barrier is data quality. Generative AI tools often struggle with fragmented commercial data, such as inconsistent stock-keeping unit files or incomplete pricing histories.
That results in insights that are “directional rather than decision-ready,” according to the report.
Agentic AI systems are designed to address those gaps. They can clean and reconcile data autonomously, run thousands of scenarios at once and coordinate actions across pricing, promotions, inventory, and vendor management.
Crucially, they are also built to explain the reasoning behind their recommendations, a key requirement for merchants who remain accountable for results.
“Early agentic AI adopters are already seeing the benefits of this new frontier, with significant revenue and margin lifts resulting from stronger assortment decisions and data-backed bargaining capabilities,” McKinsey said.
The market opportunity is drawing attention. The agentic AI segment in retail and e-commerce is projected to reach about $60b this year, up from $47b a year earlier, and could exceed $218b by 2031, according to data from Mordor Intelligence Private Ltd.
Nvidia Corp.’s third annual State of AI in Retail and Consumer Packaged Goods survey found that retailers are using AI agents to power digital shopping assistants, dynamic product catalogues and faster warehouse and supply-chain operations.
In the survey, 47% of retail and consumer goods companies said they are using or evaluating agentic AI, whilst 21% plan deployments within a year.
McKinsey said the technology could reshape daily work for category managers. Instead of reacting to problems after the fact, merchants could start each day with AI-generated briefs flagging underperforming promotions, local pricing gaps or emerging markdown risks.
With limited human approval, agents could then adjust store-level prices, redirect marketing budgets or shift inventory.
Vendor discussions could also change, moving away from backward-looking reviews toward forward-looking growth planning, supported by live benchmarks and scenario analysis.
Still, McKinsey warned that tech alone would not deliver results. Retailers need to redesign operating models, clarify decision rights and invest in skills.
Only 24% of merchants surveyed said they receive substantial AI upskilling.
The firm also flagged risks, including unclear pricing guardrails, overreliance on short-term metrics, and resistance to moving away from legacy systems.
To manage those risks, McKinsey urged retailers to roll out agentic AI in phases, pairing technology with workflow changes and scaling only after consistent performance is proven.
Questions to ponder:
- How should retailers redesign merchandising roles as AI agents take on more analytical work?
- What safeguards ensure autonomous pricing and promotions protect brand trust and profits?
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EXPERT OPINION
How should retailers redesign merchandising roles as AI agents take on more analytical work?
Merchandisers should shift from doing the analysis to deciding what to do with it. Use agentic AI for what it does best - crunch data, spot patterns, test sources, save time, repeat - so people can focus on judgment, storytelling, supplier collaboration and in‑store/customer impact. But this only works if data is consistently reliable, and this is a recurring challenge. Start small and scalable (even store‑level data that’s easy to validate), prove accuracy, then expand.
What safeguards ensure autonomous pricing and promotions protect brand trust and profits?
Guardrails first: clean data, clear rules and human override. Prices should move only when there’s a clear consumer benefit (e.g., smarter promo rotation or genuine cost‑down passed on), not constant micro‑fluctuations that confuse or alienate shoppers. For example, IKEA’s “new lower price” is the right mindset: downward when justified and there to stay, stable otherwise.
How should retailers redesign merchandising roles as AI agents take on more analytical work?
Merchandising roles will soon be transitioning from data processing to "curatorial strategy." As AI agents handle the heavy lifting of inventory forecasting and SKU rationalization, human merchandisers should be repositioned to focus on trend-spotting and high-level brand storytelling. These elements will not be replaced by AI as it will ALWAYS require human intuition and cultural context. The role becomes less about managing spreadsheets and more about managing the AI’s parameters to ensure the product mix aligns with the brand’s long-term vision rather than just short-term volume.
What safeguards ensure autonomous pricing and promotions protect brand trust and profits?
The primary safeguard is the implementation of "strategic guardrails" or price floors that the AI cannot override, preventing a "race to the bottom" that erodes brand equity. Furthermore, autonomous systems must be audited for "logic drift" to ensure they aren't rewarding low-value, price-sensitive consumers at the expense of loyal, high-margin segments. Transparency in how dynamic pricing is applied is essential; if a consumer feels exploited by an algorithm, brand trust is lost instantly. Profit protection must be hard-coded into the AI’s objective function, prioritizing margin health over raw conversion rates.
The rise of AI agents fundamentally reshapes merchandising roles within the retail industry. Retailers should shift merchandisers from data crunchers into strategic decision-makers who set the guardrails within which AI operates. This means defining the brand values that no algorithm should override on its own. On the safeguards side, autonomous pricing and promotion engines should operate within human-defined parameters such as floor and ceiling price thresholds, margin protection rules, and brand-sensitivity filters that prevent poorly judged promotions that can damage brand reputation and profit. Regular human review cycles and real-time alerts for unusual patterns ensure that AI recommendations are audited rather than blindly executed. Retailers who thrive will treat AI as a powerful but bounded tool, with empowered human merchandisers serving as the ethical and strategic layer that keeps pricing decisions aligned with long-term brand equity and customer loyalty.