AI has become essential in retail, but many retailers are still navigating early challenges. Two key pitfalls include:

• Contextual Intelligence Over Blind Automation – While basic AI can optimize prices and promotions, it often lacks contextual intelligence. Models based solely on historical data may overlook real-time factors like market shifts and changing consumer sentiment. Decisions made without this context risk straying from current realities.
  
  
• Human-in-the-Loop Design– Many retailers view AI as a "set-it-and-forget-it" tool. However, the most effective implementations combine AI insights with human expertise, capturing nuances that AI alone might miss.

A significant issue is the quality of training data and the absence of feedback loops within the software. Poorly curated data can lead to biased results, while the lack of feedback mechanisms prevents ongoing model refinement. Without these loops, AI models become less responsive to evolving market dynamics.

The over-automation risks include decision detachment—which can prioritize efficiency over customer experience and brand perception. Three critical areas of concern in pricing at Engage3 are:

Over-automated pricing can create volatility, damaging trust and price perception. Essential guardrails include balancing AI-driven changes with long-term brand value.

AI may over-optimize promotions, resulting in repetitive offers that cannibalize sales. Aligning AI with strategic goals is vital to ensuring promotions enhance category performance.

In volatile market dynamics, rigid AI models can hinder responsiveness. Maintaining flexible override capabilities and engaging continuous feedback loops keeps models adaptive. Recalibration involves balancing automation with control, enabling retailers to thrive as AI becomes the industry standard.

Real progress isn’t just using AI—it’s knowing when to challenge it, when to trust it, and when to rely on judgment.