Retailers Bring Conversational AI and Analytics Closer to the User: Why “Dialogue, Not Dashboards” Is the New Retail Operating System

AI generated image for Retailers Bring Conversational AI and Analytics Closer to the User: Why “Dialogue, Not Dashboards” Is the New Retail Operating System

Retail has always loved a good dashboard. It’s comforting: neat charts, tidy KPIs, and enough color gradients to make a PowerPoint feel like a work of art. But dashboards also come with an inconvenient truth: they’re often where insights go to… wait. Wait for a specialist. Wait for the next refresh. Wait for someone to interpret the numbers. Wait until the decision window has closed and the only “actionable insight” left is that you missed it.

That’s why the RSS item Retailers bring conversational AI and analytics closer to the user (published January 16, 2026) caught my eye. The piece, published by AI News (TechForge Publications) and attributed to AI News, focuses on how retailers are pushing analytics and consumer insight directly into day-to-day decision-making via conversational interfaces—specifically through First Insight’s new tool, Ellis. citeturn1view0

This article expands the story beyond one product launch to a broader industry inflection point: the shift from analytics as a destination (dashboards) to analytics as an interaction model (conversation), increasingly embedded in the workflows of merchandising, pricing, and planning teams. Along the way, we’ll cover what’s actually changing under the hood, why retail is a uniquely fertile (and uniquely risky) environment for conversational analytics, and what leaders should demand before they let an AI “copilot” drive margin-critical decisions.

From dashboards to dialogue: what’s changing and why now

For the last decade, retail analytics has been on a familiar journey: collect data, warehouse data, build dashboards, then hold meetings in which the dashboard is presented like a weather report. The fundamental promise of modern AI tooling is not that it can generate another chart. It’s that it can compress the “time-to-decision” cycle so dramatically that insight arrives in the same moment as the question.

AI News frames this as a transition from “dashboards to dialogue,” highlighting a common operational bottleneck: retail teams have data, but can’t translate it into action quickly enough to influence product and commercial decisions. citeturn1view0turn3search4

That bottleneck is not trivial. In retail, timing is the business model. Whether you’re selecting a six-item capsule collection or a nine-item drop, the margin impact isn’t just about what sells—it’s also about how quickly you commit to the right inventory and avoid the wrong one.

Why conversational analytics feels inevitable

There are at least four forces making “dialogue with data” feel like the next default UI:

  • Decision velocity is increasing: planning cycles are under pressure from demand volatility, shifting consumer behavior, and supply chain constraints.
  • Data literacy is uneven: not every merchandiser or pricing lead wants to write SQL or navigate a BI semantic layer.
  • Generative AI normalized chat UIs: asking a system questions in natural language is now mainstream behavior, not a novelty.
  • Analytics vendors are embedding GenAI: Gartner expects a large share of new analytics content to be contextualized for intelligent applications via GenAI by 2027, and Forrester has argued AI is now a core capability of BI platforms, including conversational interaction with data (NLQ/NLG). citeturn4search0turn4search2

Retail is simply one of the clearest places where this shift can deliver value—because the cost of being slow is often measured in markdowns.

What the AI News piece says: First Insight’s Ellis as a case study

The original AI News story focuses on First Insight, a US-based analytics company specializing in predictive consumer feedback, and its new AI tool Ellis. In the article, Ellis is described as a conversational interface that lets merchandising, pricing, and planning teams ask questions about products, pricing, and demand within the First Insight platform—aiming to compress decision times into minutes. citeturn1view0turn2search3

First Insight positions Ellis as a way to bring consumer insight “into the moment when decisions are actually made,” rather than forcing teams to interpret dashboards or wait on analyst bandwidth. citeturn1view0

What makes Ellis interesting (and different from “just add ChatGPT”)

Retailers have no shortage of chatbots. Most are optimized for customer service. Ellis is pitched as something else: a conversational front-end for decision intelligence—essentially a merchant’s copilot rather than a shopper’s assistant.

According to First Insight’s own materials and related coverage, Ellis is powered by a “predictive retail large language model,” positioned as domain-specific rather than trained broadly on web text. The intent is to ground answers in the company’s predictive models and consumer response data, rather than provide generic language-model output. citeturn2search3turn2search2turn1view0

That distinction matters. Retail leaders don’t need eloquence. They need correctness, traceability, and a credible link between a recommendation and the data that supports it.

Why retail is obsessed with “insight to action” (and why it’s hard)

Retail isn’t just fast-moving—it’s multi-variable and unforgiving. A change in fabric composition, a competitor’s promo, a weather anomaly, or an influencer moment can alter demand in ways that conventional reporting might only catch after the fact.

McKinsey has described how companies can fail to embed big-data insights into daily decision-making due to capability gaps and poor infrastructure, noting that improving systems can reduce insight-generation time dramatically (from days to minutes in one example). citeturn3search4

But even if insight arrives quickly, organizations still struggle with two real-world friction points:

  • Access friction: “Where do I find this metric?” “Who owns this report?” “Is it updated?”
  • Interpretation friction: “What does this actually mean for the assortment?” “Is this signal strong enough to change price?”

Conversational analytics aims to reduce both by letting users ask questions as they naturally arise, with the system returning answers (ideally) connected to governed data and predictive models.

Conversational AI in retail has two front doors: consumers and employees

It’s easy to lump everything under “conversational AI,” but retail is pursuing two parallel tracks:

1) Consumer-facing conversational commerce

Shoppers increasingly use AI interfaces for discovery, recommendations, and even checkout. Coverage over the past year has highlighted retailers integrating shopping and checkout experiences into AI platforms and launching AI shopping assistants. citeturn0search0turn0news14turn0news12

This is the “AI as storefront” narrative: chat becomes the new search bar, and product data becomes the new SEO.

2) Employee-facing conversational analytics (the quieter revolution)

The Ellis story sits here: “AI as merchant workstation.” It’s not about selling a product directly; it’s about helping teams decide what to sell, where, at what price, and in what quantity.

In many ways, this internal use case can be even more valuable. A great consumer chatbot might lift conversion. A great pricing and assortment copilot can move gross margin, inventory turns, and markdown exposure—all at once.

How these systems work (in plain English)

Conversational analytics tools tend to combine three layers:

  • Data foundation: clean, governed datasets (POS, inventory, product catalog, loyalty, survey feedback).
  • Predictive layer: forecasting models, elasticity models, propensity models, scenario simulations.
  • Generative/conversational layer: natural language query (NLQ), explanation, summarization, and a chat UI that orchestrates retrieval and computation.

First Insight itself describes Ellis as operating across predictive algorithms, a generative AI context layer, and a conversational interface. citeturn2search6

In practice, the difference between “helpful” and “dangerous” often comes down to one question: does the conversational layer retrieve and compute from authoritative data and models, or does it improvise?

The role of domain-specific models vs general-purpose LLMs

First Insight and others argue domain-specific retail LLMs can outperform generic ones for retail decisioning because they’re designed around retail data types and business questions, not general web text. citeturn2search2turn2search0

Even if you don’t buy the “our LLM is special” marketing line at face value, the underlying point is valid: retail decisions are constrained by margin targets, seasonality, channel strategy, supply chain lead times, and compliance rules. Generic models don’t “know” your constraints unless you enforce them through architecture, governance, and integration.

What retail teams actually want to ask

The AI News article gives examples of questions like whether a six-item or nine-item assortment will perform better in a specific market, or how removing materials might affect consumer appeal. citeturn1view0

In real-world planning rooms, the question set gets even more specific:

  • Assortment: “If we add two trend items, which core items should we cut to protect sell-through?”
  • Pricing: “What’s the highest price we can hold in the Northeast without tanking conversion?”
  • Localization: “Which colors will likely over-index in suburban stores vs urban stores?”
  • Packaging/product changes: “If we remove an ingredient/material for cost reasons, which segments will churn?”
  • Scenario planning: “If a competitor runs 20% off for two weeks, what promo depth protects volume but minimizes margin damage?”

The conversational interface isn’t the magic. The magic is having a system that can answer those questions fast and defensibly.

Industry context: why “retail copilots” are multiplying

Ellis sits in a growing market of AI tools for merchandising and pricing. AI News mentions vendors such as EDITED, DynamicAction, and RetailNext as examples of players offering AI tools aimed at merchandising and pricing. citeturn1view0

At the same time, big platform vendors are pushing “shopping agents” and AI-driven retail solutions, including conversational product discovery experiences. Microsoft has described a “Personalized Shopping Agent” concept for retailers, using conversational design to help consumers describe what they want in natural language. citeturn0search5

Put together, the picture is clear: conversational interfaces are becoming a standard control surface—whether you’re a shopper browsing a catalog or a merchant deciding what to put in it.

Analytics democratization: a promise with sharp edges

One of the biggest claims in the AI News piece is that Ellis makes insight accessible beyond specialist analytics teams—so executives and non-technical users can engage with data without waiting. citeturn1view0

That promise lines up with broader market trends. Gartner has reported strong organizational usage of AI tools for automated insights and natural-language queries, while cautioning elsewhere (and consistently in analyst research) that governance matters. citeturn4search0turn4search1

Here’s the sharp edge: democratization can quickly become “everyone gets their own version of the truth” unless you implement guardrails.

Three governance rules that should be non-negotiable

  • Explainability: the system should show what data and assumptions drove an answer, not just produce a confident sentence.
  • Permissioning: pricing and margin-sensitive data should have role-based access controls.
  • Auditability: retailers need logs of queries, outputs, and downstream decisions—especially if decisions affect consumers (price discrimination concerns) or financial reporting.

The trust problem: conversational AI can be persuasive even when wrong

Retail is a business where “a little wrong” can be very expensive. A 2% error in demand forecasting in the wrong category can create stockouts (lost sales) or overstock (markdowns). And conversational systems add a unique risk: the output is often packaged as a fluent explanation, which humans may over-trust.

That’s why systems that combine predictive models with conversational layers must be designed for decision support, not decision replacement.

What good looks like: answer, confidence, and options

A well-designed retail copilot should return:

  • A direct answer (e.g., recommended price point, expected sell-through).
  • A confidence signal (e.g., strength of consumer feedback, model certainty, data freshness).
  • Alternatives (scenario A/B, trade-offs between margin and volume, sensitivity to promo changes).

When a system provides only the answer, without the context, it becomes a black box. And retail has been burned by black boxes before.

Case-study lens: why consumer feedback is a powerful training signal

One interesting thread in the AI News story is First Insight’s emphasis on predictive consumer feedback—survey-driven signals that can be used before a product is fully in-market. citeturn1view0

Why does that matter? Because retail data is often backward-looking. POS data tells you what sold, but not always what would have sold if you had stocked it, priced it differently, or presented it in another way. Consumer intent and preference data can help fill that gap—especially during early concept development.

First Insight says it has worked with brands including Boden, Family Dollar, and Under Armour. The AI News piece notes Under Armour has discussed using consumer data and predictive modelling to refine assortments and pricing and reduce markdown risk. citeturn1view0

On the Boden side, First Insight has publicly described an expanded partnership aimed at integrating consumer demand signals into product, merchandising, and pricing decisions, with Greg Petro (CEO of First Insight) emphasizing the limits of intuition and backward-looking sales data. citeturn2search1turn2search9

It’s worth noting: vendor press releases are not neutral research. But they do show where retailers believe the value is—speeding up decisions earlier in the product lifecycle.

Retail’s next competitive moat: not “AI”, but operational design around AI

Many retailers can buy similar technology. Fewer can operationalize it. The winners will be the ones who redesign workflows around fast insight—without losing rigor.

Deloitte’s 2026 retail outlook frames 2026 as a potentially pivotal year shaped by value-oriented consumers, AI-led commerce, resilient supply chains, and smarter margin management. citeturn3search5

That’s the real backdrop for Ellis and similar tools: the industry is under pressure, and “good enough” decision cycles are no longer good enough.

Five practical workflow changes retailers are making

  • Embedding analytics into planning meetings: chat-based queries during line review, not after.
  • Shortening test-and-learn loops: smaller launches, quicker readouts, faster iteration.
  • Upgrading product data quality: richer attributes to support both consumer-facing AI and internal planning.
  • Building cross-functional “decision pods”: merchants + analysts + supply chain + finance.
  • Establishing AI governance councils: to define policies on data usage, model updates, and accountability.

Security and privacy implications: conversational interfaces broaden the blast radius

Conversational analytics systems often sit at the intersection of sensitive data types:

  • Customer feedback and potentially PII (depending on collection methods)
  • Pricing strategy and promotional calendars
  • Supplier and inventory information
  • Regional performance and store-level profitability

When you put a friendly chat UI on top of that, you make the system more accessible—which is the point—but you also increase risk if access controls and logging are weak.

Retailers should evaluate:

  • Data retention: what conversations are stored, and for how long?
  • Model boundaries: does the system prevent data leakage across tenants/customers?
  • Prompt injection resilience: can a user trick the system into revealing restricted info?
  • Third-party risk: if an external LLM is involved, what leaves your environment?

These are not theoretical concerns. They are the difference between “AI as a productivity upgrade” and “AI as a new attack surface.”

Measuring ROI: what should retailers track?

Retail AI projects sometimes die by dashboard irony: everyone can see the metrics, but nobody can prove the business impact. If you’re deploying a conversational analytics copilot, you should define success in operational terms, not just “engagement.”

Suggested KPIs for conversational analytics in merchandising and pricing

  • Decision cycle time: time from question to approved action (e.g., price update, assortment choice).
  • Markdown rate: especially in categories where the tool is used heavily.
  • Forecast accuracy: before vs after (and by segment/category).
  • Sell-through at full price: a cleaner measure of assortment quality and pricing power.
  • Adoption breadth: users across teams and seniority levels (democratization only counts if people use it).

And a bonus KPI for the CFO: how often the tool’s recommendations are overridden, and why. Overrides aren’t failure—they’re feedback for calibration.

What to ask vendors before you bet margin on a copilot

Whether you’re evaluating Ellis or any other conversational decisioning layer, the due diligence questions are remarkably consistent:

  • What data does it use by default? (And how do you prevent it from using the wrong data?)
  • How does it handle uncertainty? (Confidence intervals, scenario ranges, sensitivity analysis.)
  • Can it cite its sources internally? (Not web citations—your datasets, tables, models, and time ranges.)
  • How is it governed? (Role-based access, audit logs, approvals.)
  • How often are models updated? (And what’s the change-control process?)

If answers are vague, assume the system is more “chatbot” than “decision engine.”

The bigger picture: conversational interfaces are becoming the retail control plane

In 2024, many retailers experimented. In 2025, many deployed pilots. In 2026, the winners will be the ones who integrate AI into the operational fabric of retail—where the questions happen, not where the reports live.

The AI News story about First Insight and Ellis is a useful signal of that shift: retailers want insight closer to users, closer to moments of decision, and closer to commercial outcomes. citeturn1view0turn2search4

And for those of us watching the industry, it’s also a reminder that the most transformative AI isn’t always the flashy “talking shopping assistant.” Sometimes it’s the quietly dangerous one: the system that tells a merchant, with a straight face, to price a product at $49.99 instead of $39.99—and then has to be right.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org