Agentic AI in retail: eight strategic mistakes to avoid for a positive ROI

Agentic AI Reshapes Retail: From Automation to Autonomy. The Challenges and the Centrality of the Human-in-the-Loop Approach. Insights from Antonio Burinato, GM of Innovaway.

The retail sector is undergoing an unprecedented transformation, driven by margin pressure, increasingly structured omnichannel models, and a continuous explosion in data volumes. In this complex scenario, purely manual decision-making processes are no longer sustainable. While the adoption of Process Automation previously offered concrete answers to the market by reducing the costs of repetitive operations, its structural limits have now clearly emerged. Traditional automation, operating deterministically on predefined workflows, is incapable of adapting to ambiguous conditions or learning from exceptions.

For major players in the sector, bridging these gaps is no longer an option, but a fundamental operational requirement. This marks the beginning of the massive adoption ofAgentic AI, an advanced technological paradigm that shifts corporate focus from mechanical execution to true decision-making autonomy.

Beyond Traditional Automation: The Value of Agentic AI

Unlike classic systems, an Agentic AI-based ecosystem "reasons": it analyzes context in real time, formulates complex action plans, executes tasks across heterogeneous information systems, and dynamically adapts its strategies based on the results achieved.

Recent market analyses indicate that Agentic AI is not a future promise, but a current priority. As confirmed during the NRF Big Show 2026, retailers that have already integrated autonomous agents into their decision-making processes report up to a 30% reduction in costs related to supply chain inefficiencies and a 25% increase in customer care resolution speed. The true competitive differentiator therefore shifts to reaction speed: response times to unexpected events shrink from entire days to just a few minutes.

Main Areas of Application

Agentic AI serves as a cross-functional efficiency lever. The most relevant operational implementations for the retail ecosystem include:

• Customer Experience End-to-End: Conversational agents that guide complex purchasing journeys, manage returns by interpreting the user's true intent, and ensure absolute consistency across all corporate touchpoints.
• Supply Chain Orchestration: Proactive systems that anticipate stockouts or over-stock situations by analyzing exogenous variables, reallocating inventory among stores based on localized forecasts.
• Adaptive Merchandising and Pricing: Dynamic optimization of store layouts and retail prices based on competitor moves, demand elasticity, and margin targets.
• Workforce and Supplier Management: Intelligent scheduling of staff shifts in response to traffic peaks and continuous predictive monitoring of supplier performance.

Strategic Challenges for Project Success

For C-Level executives, CIOs, and IT Managers, moving from the Proof of Concept (PoC) phase to production is the ultimate testing ground. To protect ROI and ensure a real business impact, eight critical project areas must be rigorously managed:

• Inadequate Data: A successful AI project requires integrated and consistent data across physical and digital channels. Limited or fragmented datasets risk amplifying inefficiencies.
• Unclear Objectives: Technological adoption must be driven from the outset by clear, measurable business KPIs.
• PoC Scalability: The prototype must be designed as a genuine "first operational step," based on realistic system integrations, to avoid developing solutions that cannot be industrialized.
• Lack of Orchestration: AI agents cannot operate in isolated silos; they require a robust central coordination and orchestration layer.
• IT and Business Misalignment: Continuous dialogue between those who define operational priorities and those who develop the technical solution is an absolute prerequisite for success.
• Wrong Use Case Selection: Priority should be given to limited but relevant scopes to achieve quick validation in the field and measure impact.
• Confused Ownership: The governance of the agentic ecosystem must be defined with extreme precision, establishing clear operational responsibilities and roles.
• Middle Management Resistance: Adopting a change management plan is crucial to prevent teams from perceiving AI as a threat to their roles.

The "Human in the Loop" Paradigm

Agentic AI represents a formidable enabler of efficiency and responsiveness for retail, but at Innovaway, we are deeply convinced that digital transformation generates value only through a solid Human in the Loop.

The goal of the agentic ecosystem is not to replace human capital, but to act in close synergy with it—liberating professionals from repetitive tasks and providing highly qualified data-driven recommendations. It is always up to the experience, intuition, and strategic vision of the human operator to validate and govern AI decisions in the most critical business contexts. Only by conceiving, orchestrating, and scaling projects around this vital human-machine collaboration can retailers transform innovation into a solid, profitable, and truly long-lasting competitive advantage.

Click here to read the full interview with Antonio Burinato