After years of testing artificial intelligence across retail operations, companies are now focusing on bringing consumer insight directly into everyday decision-making. According to US-based analytics firm First Insight, the next stage of retail AI is less about dashboards and more about dialogue.
Following a three-month beta rollout, First Insight has launched a new conversational AI tool called Ellis. Designed for merchandising, pricing, and planning teams, Ellis allows users to ask plain-language questions about products, pricing strategies, and demand patterns within the platform. The company says this approach can reduce decision timelines from days to minutes.
From Reports to Real-Time Answers
Research shows that while many large retailers collect massive volumes of customer data, a significant number struggle to turn those insights into fast, actionable decisions. AI systems that shorten the gap between insight and execution tend to deliver stronger commercial results than traditional reporting tools.
Historically, First Insight has helped retailers such as Boden, Family Dollar, and Under Armour predict consumer demand, price sensitivity, and product performance through surveys and predictive modelling. These insights were typically delivered through dashboards or static reports.
Ellis changes that model by enabling conversational queries. Teams can ask whether a six-item assortment is likely to outperform a nine-item range in a specific region, or how removing certain materials may affect customer appeal. Responses are generated using existing predictive data models.
Faster access to insight increases its value, especially during early product development and line reviews.
Solving a Common Retail Bottleneck
Industry research suggests conversational AI can address a long-standing challenge in retail. Insight often loses value when it cannot be accessed quickly, particularly during early-stage product development or assortment planning.
Predictive analytics are already widely used across the sector. Retailers report that consumer data and predictive modelling help refine assortments, improve pricing strategies, reduce markdown risk, and increase full-price sell-through.
Pricing, Assortments, and Competition
First Insight says Ellis is powered by a predictive retail large language model trained on consumer response data. This enables the system to answer questions about optimal pricing, expected sales velocity, ideal assortment size, and customer segment preferences.
“Ellis brings predictive intelligence into line reviews, early concept development, and the boardroom, helping teams move faster without losing confidence.”
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