In the “Using AI for Content Generation” session at eTail Boston, Jenna Flateman Posner explored what happens when AI moves beyond simply “creating content” and starts working as an active participant in retail operations.

Her framing of “agentic AI”, AI agents that take on multi-step, autonomous tasks, shifted the discussion from efficiency in writing to acceleration across the product lifecycle. For retail leaders navigating short product windows and competitive markets, the opportunity is about speed and enablement at scale.

AI in the product information pipeline

One of the most compelling applications Jenna outlined involved integrating AI directly into product information management (PIM) systems.

In categories like footwear, brands often have an extended gap between finalising a product and launching it to market. Traditionally, product page readiness depends on a sequence of human-led steps:

  • Waiting for samples to arrive.

  • Photographing products.

  • Writing descriptions.

  • Assigning attributes and tags.

  • Uploading to e-commerce platforms.

This creates a bottleneck, especially for brands managing thousands of SKUs across seasons.

Jenna explained how AI agents could compress this timeline:

  • Scanning the web for early product images from manufacturer sources, showrooms, or supplier feeds.

  • Extracting and structuring data - pulling technical details, sizing information, and features into the PIM.

  • Assigning product attributes like category tags, style codes, and materials.

  • Writing SEO-optimised descriptions based on the brand’s tone and guidelines.

  • Populating product pages in the e-commerce CMS.

The result? Brands could have product pages 80% ready to go before physical samples even arrive, needing only final validation and image swaps. For businesses competing on speed to market, this shifts the launch process from reactive to proactive.

Front-end personalisation: The AI stylist

Jenna extended the conversation beyond operations into the customer experience. AI, she argued, is already reshaping front-end engagement through tools like virtual stylists.

These AI-driven interfaces don’t just recommend products based on static filters - they learn from shopper behaviour, intent signals, and style preferences over time. In practice, this could mean:

  • Analysing a customer’s browsing patterns to predict what they might be shopping for before they explicitly search for it.

  • Dynamically adjusting recommendations in real time based on how a customer navigates a site.

  • Providing “complete the look” suggestions that feel tailored, not templated.

For retailers, the win is twofold: higher conversion rates from more relevant suggestions, and richer customer data to inform future merchandising and marketing.

Back-end intelligence: natural language access to data

Operationally, Jenna highlighted how AI can make complex systems more usable through natural language interfaces.

For example, an order management system (OMS) often requires specialist knowledge to retrieve specific information. By layering AI agents into these systems, a merchandising manager could simply type or speak a request, “Show me orders for SKUs under $50 shipped in the past week”, and receive an instant, formatted report.

This reduces the dependency on technical support teams, allowing more people across the organisation to access and act on operational data.

Customer service autonomy

Customer service is another area where AI agents are moving from scripted bots to dynamic problem solvers. Jenna pointed to emerging models capable of:

  • Reading and interpreting customer order history.

  • Identifying potential resolutions without human escalation.

  • Handling multi-step workflows, such as processing a return, reissuing a discount code, and updating inventory records.

While she emphasised the importance of maintaining human oversight, the potential efficiency gains are significant - especially for high-volume e-commerce brands dealing with repetitive, transactional queries.

The real payoff: time to shelf

Across all examples, the unifying theme was speed - specifically, shortening the “time to shelf” for products and campaigns.

By embedding AI agents into both front-end and back-end systems, retailers can:

  • Reduce lead times in product launches.

  • Increase the number of SKUs they can support without expanding headcount.

  • React to market trends faster than competitors.

This isn’t just about cost savings. In competitive retail categories, being first to market or first to adapt can directly determine revenue share.

The implementation mindset

Jenna’s approach to AI adoption was grounded in practical execution:

  • Identify bottlenecks in existing workflows where AI could take on repetitive, rule-based tasks.

  • Prioritise integrations that connect AI tools directly into core systems like PIM, OMS, and CRM.

  • Set guardrails for AI autonomy, ensuring human validation where needed.

  • Measure impact not only on efficiency but also on sales velocity and customer experience.

She cautioned against chasing novelty or overcomplicating the AI stack. Instead, the focus should be on finding high-frequency, high-value use cases that deliver tangible ROI.

The takeaway

The retail AI conversation often focuses on marketing copy, social captions, and campaign assets. This session showed that the real frontier lies in integrating AI as a silent, always-on operator across the product and customer journey.

When AI agents can prepare a product for launch before it’s even in the building, guide a shopper to the right purchase in seconds, and handle post-purchase operations without friction - the definition of “content generation” expands far beyond text.

For brands under pressure to do more, faster, agentic AI offers not just efficiency, but a structural advantage.

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