Closing the Agentic Confidence Gap in Retail

In modern retail, artificial intelligence is everywhere. From forecasting engines to dynamic pricing tools and demand planning dashboards, AI is now firmly embedded within the industry. In fact, 70 percent of retail companies are either piloting or have already partially implemented agentic AI systems, according to FluentCommerce. However, despite this widespread adoption, full deployment is low — only 8 percent of retailers have fully deployed AI across their operations.

Lurking behind that optimism and experimentation are lingering trust issues for retail organizations, from ethical and regulatory concerns (43 percent in the FluentCommerce study) to data quality and integration issues (39 percent). That distrust often trickles down to an employee’s daily workflows: an AI tool recommends an action based on data, but the employee doesn’t feel fully comfortable relying on it, opting to stick with their gut, dismissing that recommendation, and further delaying widespread AI adoption.

The hesitation is understandable. Many teams still feel scarred from past transformation efforts — previous waves of technology adoption that failed to live up to their promise, vendors that overpromised results, and data inputs that never quite aligned with reality. And now, agentic systems are entering the picture, requiring more autonomy than previous technology solutions, greater access to data, and higher levels of automation. The stakes suddenly feel even higher.

When technology starts making commercial decisions for us, any potential agentic mistake undermines trust in that system, process or workflow, and widespread adoption is kept at bay. It’s a vicious cycle.

Confidence in agentic AI doesn’t arrive through blind faith; it’s built deliberately through structure, transparency, and a progression of adoption based equally on trust and results, reinforcing the former through the delivery of the latter.

Step One: AI as an Advisor

This first step of building confidence is understanding how to use AI alongside human judgment. The technology makes recommendations, but workers still approve every action. Over time, teams begin to see AI’s outputs aligning with their own judgment, building trust.

In retail, markdown planning offers a useful example. Traditionally, teams will comb through spreadsheets to identify slow-moving products, model a handful of different discount scenarios, and push prices live in a matter of days or weeks. With AI, the flow changes subtly but meaningfully. Early steps requiring hours of human review — SKU by SKU, row by row — are now automated, with intelligent agents able to flag at-risk SKUs, simulate multiple markdown depths, and predict outcomes across margin, revenue and sell-through. A recommendation from that agent is served up, with merchandisers still able to choose the final action from evidence-based options rather than relying on intuition alone.

The goal at this stage is alignment, not automation. People see the logic, challenge it, and slowly realize the AI often gets closer to the truth than manual methods.

Step Two: Assisted Execution

Once accuracy is consistent, retailers can move to more active collaboration with AI. Here, AI acts, but only within clearly defined guardrails.

In pricing, those guardrails might include minimum margin thresholds or markdown caps. Within these boundaries, prices can adjust automatically, while teams receive clear summaries of what changed and why.

This is the collaborative phase where people handle exceptions and strategy, and the agent handles execution. Confidence grows because the agent’s actions are within pre-defined guardrails. They’re both visible and reversible.

Step Three: Autonomous Optimization

Over time, specific decisions and processes can run end-to-end without any human intervention. In pricing, this might mean continuous micro-adjustments to balance margin and stock clearance. In inventory, it could include autonomous replenishment or stock reallocation between stores.

Humans don’t disappear but rather shift from manual work to more strategic tasks — setting objectives, establishing constraints, reviewing outputs, and planning for the future.

Embedding Confidence Into Culture

The fastest way to build trust is to make it measurable. Retailers that track clear metrics on AI’s impact, such as decision accuracy vs. human outcomes, margin impact, or cycle time from insight to action, create an objective view of performance.

However, numbers alone aren’t enough. Confidence comes from familiarity, which is why the most successful retailers embed AI directly into their operating rhythm, reviewing an agent’s actions in weekly meetings or letting it simulate scenarios for promotion planning.

Over time, teams will build confidence and feel comfortable using agents to unlock real value for the business, with the peace of mind that comes from a trusted and proven solution.

Richard Potter is the co-founder of Peak.ai, a UiPath company. Peak.ai is a provider of agentic AI solutions that deliver real business outcomes for manufacturing, consumer goods and retail companies worldwide.