After years of experimenting with artificial intelligence, retailers are now entering a more practical and results-driven phase of adoption. The focus is shifting from complex dashboards and delayed reports to something far more intuitive — conversational AI that delivers insight at the moment decisions are made.
According to retail analytics firm First Insight, the next evolution of AI in retail will not be defined by more data, but by better dialogue.
From Data Overload to Decision Intelligence
Most large retailers today collect massive volumes of customer data. However, research from McKinsey highlights a persistent challenge:
many organisations struggle to translate insights into action quickly enough to influence product and pricing decisions.
Traditional dashboards often slow down decision-making. Teams must wait for analysts to prepare reports, interpret metrics, and translate findings — by which time opportunities may already be lost.
AI tools that shorten the distance between insight and execution are therefore emerging as the most valuable investments.
Introducing Conversational Retail AI
Following a three-month beta programme, First Insight has launched Ellis, a conversational AI interface designed specifically for retail decision-makers.
Instead of navigating dashboards, merchandising, pricing, and planning teams can now ask direct questions such as:
- Will a six-item or nine-item assortment perform better in this market?
- How will removing a specific material impact customer appeal?
- What price point maximises full-price sell-through?
Ellis responds instantly using predictive models trained on real consumer feedback — turning insight into conversation rather than charts.
From Dashboards to Dialogue
Historically, consumer insight has been delivered through static reports or visual dashboards. While powerful, these formats often require interpretation by specialists.
Ellis represents a shift toward natural-language analytics, enabling teams to interact with data the same way they would speak to a colleague.
This approach addresses a key bottleneck identified by Harvard Business Review, which found that insight rapidly loses value when it cannot be accessed during critical moments such as:
- Line review meetings
- Early product concept development
- Pricing workshops
- Executive planning sessions
Conversational AI allows insights to surface exactly when decisions are being debated.
Predictive Insight Already Proven in Retail
Predictive analytics is not new to the retail sector. Several major brands already rely on it to improve commercial outcomes.
- Under Armour has reported using consumer feedback models to refine assortments and pricing, helping reduce markdown risk and improve full-price sales.
- Boden has leveraged customer insight to balance trend-led items with core products more effectively.
Retail giants such as Walmart and Target have also invested heavily in machine learning to optimise regional demand forecasting, pricing strategies, and product testing.
A Deloitte study on AI in retail found that organisations using predictive consumer insight experience:
- Improved forecast accuracy
- Reduced inventory risk
- Better pricing confidence
- Stronger early-stage product validation
High-Value Use Cases: Pricing and Assortment Planning
Ellis is powered by what First Insight calls a predictive retail large language model, trained on consumer response data rather than generic internet content.
This allows the system to deliver insights across critical retail functions, including:
- Optimal pricing recommendations
- Expected sales velocity
- Ideal assortment size
- Consumer segment preferences
- Scenario testing for design or material changes
Academic research supports this approach. A study in the Journal of Retailing found that data-driven pricing models consistently outperform traditional cost-plus pricing — especially when consumer willingness-to-pay is measured directly.
Competitive Benchmarking and Market Positioning
Modern retailers also face intense competitive pressure. Understanding how products compare against competitors is now essential.
Research from Bain & Company indicates that retailers able to benchmark effectively can differentiate not only on price, but also on perceived value.
AI platforms that unify consumer insight, competitive comparisons, and predictive analytics into a single conversational layer allow faster, more confident decision-making — particularly in volatile markets.
Democratizing Retail Analytics
One of the strongest claims behind conversational AI is accessibility.
Ellis is designed to place insight directly into the hands of:
- Merchandisers
- Category managers
- Pricing teams
- Senior executives
Without the need for analytics expertise or long reporting cycles, leaders can engage with data instantly.
According to Gartner, organisations that democratise analytics are significantly more likely to achieve higher adoption and return on investment — provided strong data governance remains in place.
First Insight emphasises that Ellis retains the same methodological rigour as its existing platform, simply removing friction at the point of use.
As CEO Greg Petro explains:
“Ellis brings predictive intelligence directly into line review, early concept development, and the boardroom — helping teams move faster without sacrificing confidence.”
A Growing but Competitive Market
First Insight is not alone in this space. Vendors such as EDITED, DynamicAction, and RetailNext are also deploying AI-driven merchandising and pricing solutions.
What differentiates newer platforms is not deeper algorithms, but ease of use and speed of insight.
A recent Forrester report on retail AI notes that conversational interfaces are increasingly being layered on top of existing analytics systems — reflecting growing demand for intuitive, human-like interaction with data.
The Road Ahead
As retailers continue to navigate inflation, volatile demand, and rapidly changing consumer behaviour, the ability to test scenarios and act quickly has become essential.
Conversational AI does not replace analytics — it brings analytics closer to the decision-maker.
The transition from dashboards to dialogue may mark one of the most important shifts in retail technology:
insight that speaks the language of business, at the moment it matters most.