Agentic AI in Retail: From Experimentation to Execution
In the next five years, the heart of retail intelligence will shift from predictive insights to autonomous orchestration. By 2030, agentic AI won’t be a luxury — it will power the core of retail growth, connecting data, decisions and execution in real time.
Retail has long invested in analytics, machine learning and automation. By the end of 2025, global AI in retail is projected to grow from $11.6 billion (in 2024) to $40.7 billion by 2030 (a CAGR of 23%).
But numbers reflect mostly descriptive and predictive capabilities. The real frontier is agentic AI. These systems act instead of just advising. The broader agentic AI market is expected to balloon to nearly $196.6 billion by 2034 (CAGR 43.8%). These trajectories suggest a sweeping transition: agentic AI is not niche, but central to how businesses operate.
Already, 43% of retailers are piloting autonomous AI systems, including many of our customers, with another 53% evaluating potential uses. Salesforce data backs this momentum — 76% of retailers plan to increase investment in AI agents in the coming year.
Also: Kraft Heinz pilots ketchup-focused agent as part of AI ecosystem initiative
What does “agentic AI” mean in retail? It means systems that sense state (inventory, demand, external signals); reason between goals and constraints; plan multi-step actions; execute across systems (reordering, pricing, campaign changes); and learn and adapt based on feedback.
In 2025, retail is finally shifting from “AI as experiment” to “AI in action,” and agentic AI is at the heart of that transition. As generative AI evolves beyond reactive prompts, agentic systems bring autonomy, planning and orchestration into the core of retail operations. The challenge isn’t proving the concept; it’s integrating agents, governance and new workflows at scale.
Unlike traditional AI models or rule-based automations, agentic AI refers to systems that can reason, plan, act and adapt autonomously toward defined goals. These agents aren’t merely assistants; they become active participants in business processes, making decisions, executing multi-step tasks and continuously learning from context.
In retail, this means:
Autonomous merchandising and shelf monitoring: Agents detect stockouts and display anomalies or misplacements via computer vision, then trigger restocks or alerts. Walmart, for example, already uses robotic image scanning to patrol aisles and detect pricing or inventory issues.
Adaptive pricing and demand planning: Agents dynamically adjust prices or promos in real time based on demand signals, inventory and competitive moves. A touchy topic already in retail!
Buy for me: Amazon's "Buy for Me" is a new, AI-enabled feature within the Amazon Shopping app that allows customers to purchase products from external brand websites directly through the Amazon app. Instead of leaving the Amazon app to visit another brand's site, users can click a "Buy For Me" button on eligible listings, and an AI agent will handle the entire transaction by securely completing the purchase on the brand's website using the customer's details.
Imagine a world for agents search, find and complete a transaction without the customer visiting your website. Who owns the customer? How do you manage discoverability? Pricing across sites?
Retail media/ad orchestration: Agentic systems manage the full lifecycle of campaigns from budgeting, creative, trafficking, bidding and measurement, and automatically rebalance across channels (disruption for agencies).
Customer service agents: From proactive issue resolution to conversational assistants that take initiative (e.g., automatically offering returns or upsells).
Supply chain and replenishment: Agents monitor real-time demand and stock levels to trigger orders or reallocate stock across nodes (warehouses, stores).
These use cases move AI from a tool to an enabler. Essentially, agentic systems become embedded actors, shaping outcomes rather than waiting for human direction.
A 2025 NVIDIA survey reports that 9 out of 10 retailers are now piloting or adopting AI solutions. Salesforce finds 76% of retailers are planning to increase investment in AI agents over the next year, with customer service being their top use case.
Agentic commerce (AI bots that shop or recommend autonomously) is emerging as a new battleground. BCG warns that being absent from third-party AI shopping agents may cost retailers in discoverability, brand control and margins.
Also: PepsiCo embraces agentic AI to modernize field execution and CX
What’s the win? Some retailers report gains in staff productivity, faster issue resolution and fewer manual errors after deploying agentic features. In the NVIDIA survey, retailers cited improved decision-making and employee productivity as key benefits.
In e-commerce/supply chain, AI-enabled planning has delivered up to 4% revenue uplift, 20% inventory reduction, and 10% cost savings in some reported cases. The market for AI agents is projected to grow rapidly.
In 2025 alone, it’s expected to reach $7.6 billion, up from $5.4 billion in 2024 (45% year-over-year growth). However, the “Gen AI paradox” persists. Many companies deploying generative AI report limited bottom-line impact. Agents may be the path out of that trap, but only if embedded, not bolted on superficially.
Transitioning to Execution
To move from experimentation to execution, retailers need to navigate three key dimensions: architecture and systems, organizational readiness, and governance and trust.
1. Build the right architecture and integrations. Shift from patchwork AI tools to modular, vendor-agnostic systems where agents collaborate across domains (commerce, supply chain, CX). Ensure real-time, trusted data pipelines (DataOps) as agentic systems depend on fresh, accurate data to act with confidence. And implement memory, state and feedback loops so agents can remember past interactions, adjust strategies and refine behavior.
2. Start with tightly scoped pilots. Focus first on domains with clear metrics and controlled risk (e.g., shelf scanning agents, campaign orchestration for a retail media network, complaint-handling agents for contact center). Adopt a phased rollout moving from pilot to scale. Avoid overreaching before controls and evaluation are in place. Involve the line of business domain experts (category, operations, merchandising) early so AI agents align with business logic and exceptions.
3. Set AI as a priority at the executive level. This is a big gap and blocker for some larger organizations that think AI is “its job.” Build transparency, auditability and explainability so stakeholders understand agent decisions and can intervene when needed. Monitor and mitigate bias, drift and runaway behavior as autonomous agents can snowball if unchecked.
We’re entering the era where AI doesn’t just assist retail — it acts for retail. Agentic systems are no longer sci-fi; they are being applied in stores, campaigns and support. But success won’t come from flashy pilots. It demands architectural rigor, domain-aligned execution, measurable impact and trusted governance.
Retailers that treat agentic AI as a technology wave will lag behind those that absorb it as a new operating model — or simply be left behind in weeks or months, not years.
If you’re a retail leader today, your challenge is simple: evolve your experimentation mindset into agentic execution — or cede your edge to those who do.
Justin Honaman is the head of worldwide retail, restaurants and consumer goods business development for AWS.
