AI in Retail: Execution Speed as the Real Differentiator

The session featuring Artie Sharpe, Sarah Sathaye, Mark Bietz, and Joan Abrams, moderated by Jane Cho , explored how AI is reshaping retail, not as an abstract technology trend, but as a practical tool for improving speed, precision, and decision-making in high-pressure retail environments. For these leaders, the real value of AI lies in how quickly it can help brands move from insight to execution.

One of the clearest messages from the panel was that AI-driven shopping experiences are no longer a distant concept. Platforms such as ChatGPT and Perplexity are already building capabilities that allow retailers to integrate their product data directly into AI-powered environments.

This is more than an SEO or paid search shift. In these AI-native commerce models, discovery, evaluation, and purchase may happen in a single interface. Consumers could ask an AI assistant for product recommendations, receive options filtered by their preferences, and complete a transaction without ever visiting a traditional e-commerce site.

For retailers, the risk is clear: waiting until these platforms are mature means missing the opportunity to shape how products are presented and discovered in this new channel. Joan Abrams highlighted that integration readiness now is critical. Even if these platforms have limited consumer adoption today, the speed at which they can scale means retailers need to establish a presence early.

Mark Bietz stressed that AI can only be as effective as the data it consumes. Many retailers still have inconsistent product descriptions, incomplete metadata, and poorly tagged SKUs. These issues don’t just cause inefficiencies internally, they directly reduce the accuracy of AI-driven recommendations, search results, and merchandising.

While AI can assist in automating some aspects of data clean-up and tagging, the panel agreed that a disciplined approach to data governance is still essential. This includes ensuring all SKUs have correct attributes, descriptions are clear and standardised, and product imagery is properly formatted and tagged.

Data hygiene isn’t just an operational task, it’s a competitive differentiator. In AI-driven merchandising systems, well-structured data can determine whether a product surfaces in the top results or is buried.

One of the most tangible impacts of AI today is in accelerating creative production and merchandising workflows. The panel shared practical examples of how AI is compressing timelines that once took weeks into hours:

These capabilities allow retailers to respond instantly to market signals, emerging trends, or sudden demand spikes. For seasonal retailers, this agility can be the difference between capitalising on a trend and missing the window entirely.

Sarah Sathaye articulated a vision shared by many retail leaders: a unified AI “marketing command center” capable of ingesting creative assets, performance data from every channel, and brand guidelines, then automatically generating and testing thousands of creative variations, optimising budget allocation, and surfacing the best-performing combinations.

While the tools to power parts of this vision exist, the current reality is a patchwork. Creative generation, merchandising, analytics, and campaign execution often sit in separate platforms. This fragmentation slows decision-making, introduces inconsistencies, and makes it harder to optimise holistically.

The panel agreed that integration is a major industry challenge. Without a centralised, AI-enabled hub that can connect these elements, the potential efficiency gains of AI remain limited.

Highly seasonal retailers from Halloween costume specialists to knitwear brands face unique pressures. Demand patterns can shift rapidly, product assortments need to adapt, and merchandising windows are short.

AI offers specific advantages in these environments:

The panel also discussed the potential for AI to support seasonal demand forecasting, though this is an area where adoption is still early. Predictive tools could help retailers decide how to allocate stock, set promotional calendars, and manage marketing investment in the lead-up to peak periods.

While much of the session focused on speed and capability, there was also recognition of the governance side of AI adoption. Retailers need clear policies on acceptable use of AI-generated content, particularly when using likenesses, brand imagery, or third-party creative elements.

Sarah noted that some tools enforce strict guardrails, refusing to generate certain types of content to avoid IP infringement. Others are more permissive, leaving the responsibility to the user. Brands must decide where to set their boundaries and ensure that internal teams understand the guidelines.

On the operational side, AI tools must be integrated into workflows without adding friction. This means training teams not only on how to use AI features but also on how to interpret outputs and feed back the right inputs for continuous improvement.

The session reinforced that AI’s most immediate and measurable value in retail lies in its ability to shorten the path from insight to execution. But the enablers of this speed are not just tools - they are data discipline, integrated workflows, and early adoption of AI-native commerce opportunities.

For now, the brands most likely to benefit will be those that:

AI is not replacing the fundamentals of retail execution, it’s amplifying the speed and scale at which they can be applied. In an environment where consumer behaviour shifts quickly and competition is intense, that speed may be the ultimate differentiator.

Independently Created. Not affiliated with eTail Boston.

Unofficially eTail is a ClickZ Media publication in the Events division