Retailers Bring Conversational AI and Analytics Closer to the User: Why “Chat With Your Data” Is Suddenly Retail’s Most Profitable Interface

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Retail has a long-standing love affair with dashboards. Beautiful, colorful dashboards. Dashboards with just enough filters to make you feel powerful and just enough dropdown menus to make you question your career choices.

But as anyone who has tried to make a pricing or assortment call at 5:47 p.m. on a Friday can tell you: dashboards are great at showing you what happened, and occasionally decent at hinting at what might happen—but they’re not always great at helping you decide what to do right now. That’s why a new class of retail tools is gaining traction: conversational AI layered on top of analytics, turning insight into something you can query like a colleague instead of excavate like an archaeologist.

This shift is the core theme of a recent article from AI News (TechForge Publications), Retailers bring conversational AI and analytics closer to the user, authored by AI News and published on January 16, 2026. The piece focuses on US-based predictive consumer feedback firm First Insight and its conversational tool Ellis, which aims to move retail analytics from dashboards to dialogue. citeturn1view0

In this article, I’ll use that RSS item as the foundation, then zoom out: what’s really happening in retail “chat with your data,” why the timing makes sense, what the benefits and risks look like, and how retailers should evaluate whether these copilots will be a transformation—or just another tab nobody opens after the demo.

Dashboards are not the problem. Latency is.

Most retail organizations today aren’t “data poor.” They’re “action delayed.” They have customer data, sales data, inventory data, pricing data, promotion data, and a small museum’s worth of Excel files built by someone named Chris in 2017. The issue is that retail decisions happen in fast, messy moments: line reviews, assortment meetings, vendor negotiations, promotional planning, and the endless series of “we need an answer in 10 minutes” questions that appear whenever a seasonal plan collides with reality.

And reality has been particularly energetic lately: inflation aftershocks, supply chain volatility, increased value-seeking behavior, and consumers who can change preferences faster than your product lifecycle can say “spring capsule.” Deloitte’s outlook work has repeatedly emphasized the pressure on margins and the need for retailers to modernize operations and decision-making, noting that generative AI is moving from hype toward practical use across merchandising, supply chain, and marketing. citeturn3search2turn3search3

So the emerging pitch is straightforward: reduce the time between insight and action. McKinsey has framed “insight-driven” approaches as a way to embed consumer understanding into category decisions and execution, with measurable uplifts reported in some transformations. citeturn3search4

Conversational analytics is essentially an attempt to make “asking the data” feel like asking a teammate—less navigation, more intent.

The RSS story: First Insight’s Ellis and the move from dashboards to dialogue

The AI News RSS story centers on First Insight, a company known for using consumer feedback and predictive modeling to inform product decisions in retail. In the article, First Insight argues that the “next phase” of retail AI should be defined by dialogue rather than dashboards, and it highlights Ellis as a conversational interface for merchandising, pricing, and planning teams. citeturn1view0

According to AI News, Ellis became available after a beta period and is designed so business users can ask questions directly—like whether a six-item or nine-item assortment performs better in a specific market, or how changing product materials might affect appeal—then receive answers grounded in First Insight’s models. citeturn1view0

To add detail beyond the RSS summary, First Insight’s own materials describe Ellis as an “AI growth tool” that allows brands to ask strategic and tactical questions about concepts, products, pricing, and demand by segment, with answers returned quickly. It positions Ellis as powered by a “predictive retail large language model” trained on consumer response data collected through First Insight’s platform. citeturn2search0turn2search5

That focus on predictive consumer feedback is important. Many retailers already have descriptive analytics (“what sold last week”) and some predictive analytics (“what might sell next month”). First Insight’s model, as described, leans into consumer intent and willingness-to-pay signals gathered directly from consumers (rather than solely inferred from historical transactions). citeturn2search0turn2search4

What Ellis is trying to fix

  • Analytics bottlenecks: when only specialized analysts can pull the right view, everyone else waits.
  • Decision compression: retail teams need to decide in minutes, not days.
  • Scenario exploration: “What if we remove this fabric?” “What if we raise price $5?” “What if we cut two SKUs?”

This is the classic “democratization of analytics” story, but with a 2026 twist: the interface isn’t a dashboard, it’s conversation.

Why conversational AI is showing up now (and why retail is a prime target)

Retail is a particularly fertile environment for conversational analytics for four reasons:

1) Retail has high-frequency decisions with real money attached

Assortment size, pricing, promo timing, and inventory commitments are not theoretical. A small improvement in conversion, markdown rate, or forecast accuracy can be worth millions at scale—especially in categories with thin margins.

Deloitte’s retail outlook has highlighted that retailers are chasing efficiency and better personalization, and it points to measurable performance improvements tied to gen AI experiments (for example, improvements in conversion when gen AI tools are deployed during peak shopping periods). citeturn3search2

2) Retail is drowning in “semi-structured” knowledge

Retailers have structured data (sales, inventory, prices), but also oceans of semi-structured and unstructured data: product descriptions, reviews, call center transcripts, survey feedback, store associate notes, vendor docs, and creative briefs. Conversational systems—especially those using retrieval-augmented generation (RAG) or domain-tuned models—are good at bridging that messy middle.

3) The business user is finally being treated as the end user

For years, retail analytics tools were built for analysts. The user interface assumed you loved pivot tables the way some people love vintage mechanical keyboards. Now, vendors are designing for merchants, planners, marketers, and store leaders—people who need answers quickly and don’t want to learn a new query language.

4) “Agentic commerce” is pushing conversational UX everywhere

Conversational AI isn’t just being applied behind the scenes. It’s becoming a new front door to shopping itself. In January 2026, major announcements at the retail industry’s conference circuit signaled that conversational interfaces are moving closer to checkout, including AI-assisted shopping flows and integrations with assistants. citeturn0news14turn0news17turn0search5

When customers start shopping through chat, internal retail teams naturally ask: “Why are our internal tools still stuck in 2014?”

Conversational analytics: what it is (and what it is not)

Let’s put some guardrails around the term, because “conversational AI” now means everything from a basic FAQ bot to a tool-using autonomous agent that can reorder inventory and argue with suppliers (politely, one hopes).

Conversational analytics (practical definition)

Conversational analytics is an interface that allows a user to ask natural language questions about business performance, drivers, predictions, and scenarios, and receive responses grounded in enterprise data and analytics models.

Done well, it includes:

  • Grounding: responses cite or reflect actual data sources, not internet vibes.
  • Context: the system remembers what “this assortment” refers to across turns.
  • Governance: roles, access control, logging, and guardrails against “creative accounting.”
  • Actionability: the answer is structured enough to drive a decision (not a poem about demand elasticity).

What it is not

  • Not a replacement for BI or data engineering.
  • Not a substitute for experimentation, measurement, and retail judgment.
  • Not a guarantee that the underlying data is correct.

In fact, conversational analytics can sometimes make data quality issues more dangerous, because it packages outputs into confident-sounding narratives. The better the natural language, the more persuasive the mistake.

Under the hood: how tools like Ellis likely work

First Insight says Ellis is powered by a predictive retail LLM trained on consumer response data, and positioned as a conversational interface embedded into its platform. citeturn2search0turn2search4

Based on how modern enterprise conversational analytics are typically built, there are a few common architectural patterns (even if each vendor has proprietary details):

1) Predictive models + a narrative layer

Retail planning relies heavily on predictive components: demand curves, price elasticity, forecast models, uplift models, and optimization. A conversational layer can translate those outputs into explanations and recommendations.

First Insight’s marketing describes multiple layers: predictive algorithms producing demand curves and value scores, then a generative context layer to summarize and explain, and finally the conversational interface for interaction. citeturn2search4

2) Retrieval for qualitative feedback

Consumer surveys and open-ended feedback are gold, but messy. LLMs are good at summarizing patterns across thousands of comments—if you keep them grounded and auditable.

3) Tool use for “what-if” questions

A serious retail copilot shouldn’t just “talk.” It should be able to run calculations, slice a dataset, apply constraints, and show assumptions. Some platforms will wrap this as “agentic” behavior: the system uses tools (queries, models) behind the scenes and then explains results.

Why “dialogue, not dashboards” resonates with merchants

Retail is one of the few industries where the people making decisions often have to combine art (taste, brand positioning) with science (unit economics, conversion data, competitive price ladders). Dashboards are good at science. Dialogue is good at translating science into decisions in the middle of human meetings.

Examples of the kinds of questions conversational interfaces support (and that First Insight itself lists in its communications) include:

  • “What’s the optimal price by market, with margin constraints?” citeturn2search0
  • “Should we launch 6 items or 9 items for this capsule?” citeturn1view0turn2search0
  • “Summarize qualitative feedback by segment (Gen Z vs Boomers) in five sentences.” citeturn2search0
  • “What product attributes tested most positively in the US vs Canada?” citeturn2search0

These are not questions a merchant wants to answer by clicking through a dozen charts. They want a concise answer and the ability to challenge it: “Okay, but what if we drop the price $3 and remove the second colorway?”

The bigger trend: conversational AI is expanding across the retail stack

Ellis is one example focused on internal decision intelligence, but it sits within a broader movement: conversational interfaces are being added across customer experience, store operations, and marketing.

Customer-facing assistants: product discovery as a conversation

Microsoft, for example, has been building retail-focused templates and managed solutions that enable conversational product discovery, designed to be embedded in websites or apps and used by shoppers or store associates. citeturn0search0turn0search1turn0search2

These assistants aim to replace keyword search with a back-and-forth dialogue: “I’m going camping in March, what do I need?” rather than “camping gear.” The commercial goal is higher conversion and bigger baskets; the UX goal is less friction and more confidence.

Copilots for retail operations: the agentic turn

At the beginning of January 2026, Microsoft discussed “agentic AI capabilities” for retail functions and described “Copilot Checkout” as a way to turn conversations into conversions without redirecting users away from the assistant environment (with the merchant remaining merchant-of-record). citeturn0search5

Whether every retailer wants that future is another question, but the direction is clear: conversational UX is moving closer to the transaction layer, not just the discovery layer.

Case study logic: where conversational analytics can create real value

It’s tempting to treat conversational analytics as a UI upgrade. But in retail, a UI upgrade can have outsized effects if it changes behavior. The real value often comes from three mechanisms:

1) Faster iteration cycles

If a merchant can test three pricing scenarios in one meeting instead of three meetings, that’s a productivity win. If that speed means you catch a pricing mistake before production, that’s a margin win.

First Insight’s own positioning is that Ellis compresses decision time; its beta communications also framed the ambition as compressing planning cycles. citeturn2search5

2) Better use of consumer feedback at scale

Retailers have used consumer feedback for decades (focus groups, surveys, panels), but the bottleneck has been synthesis. LLM-based summarization can make qualitative insight more usable—again, assuming it’s properly grounded and not hallucinating themes.

3) More consistent decision quality

One underappreciated benefit of embedded AI tooling is reducing variability between teams and categories. McKinsey’s “category accelerator” framing describes how consistent processes and embedded insight can reduce wide variability in category execution. citeturn3search4

Conversational copilots can help standardize the questions asked, the scenarios considered, and the metrics referenced, making “how we decide” more consistent across the business.

But does it work? Evidence, experiments, and the “LLM placebo effect”

The question every retail exec should ask isn’t “Is this cool?” It’s “Does this change outcomes?” The good news is that we’re starting to see more causal evidence in the research literature on GenAI’s effect in retail workflows.

One large-scale field experiment paper, for example, reports that GenAI-based enhancements integrated into consumer-facing workflows on a large cross-border retail platform produced sales increases with treatment effects ranging from 0% to 16.3% depending on the application, with conversion rate improvements as a key mechanism. citeturn0academia21

This doesn’t prove that every conversational copilot will deliver uplift, but it supports the broader claim that reducing friction in discovery and decision-making can move the needle.

The caution: there is also a very real “LLM placebo effect.” New chat interfaces create a sense of progress even if the underlying data pipelines, model quality, and governance are unchanged. The novelty wears off. What remains is whether the tool reliably answers the questions people actually ask, in the way the business can trust.

Practical risks: hallucinations, misinterpretation, and accidental policy violations

Conversational analytics introduces risks that dashboards rarely do. Dashboards can mislead, sure, but they typically don’t invent numbers. LLM-based systems sometimes can—unless carefully constrained.

Risk 1: Hallucinated facts

If an answer includes a made-up conversion rate or a fabricated “top driver,” someone will quote it in a meeting and a bad decision will become a confident decision. The fix is rigorous grounding, citations to internal data sources, and “show your work” UX.

Risk 2: Misinterpretation of outputs

Even correct outputs can be misunderstood. For example, a model might say “demand is likely to be higher for option A,” but the confidence interval is wide. The conversational response may compress uncertainty into a narrative. Governance matters here.

Risk 3: Data privacy and leakage

Retail data is sensitive: customer segments, pricing strategies, future assortments, supplier terms. Vendors and retailers need clear policies on what data can be used for training, what stays in the tenant, and how logs are handled.

Risk 4: Over-automation

Agentic systems can be tempting: “Let the AI reorder stock” or “Let the AI set prices.” But automation without controls can amplify errors. The more “hands-off” the action, the more “hands-on” the governance needs to be.

How retailers should evaluate a conversational analytics vendor (a non-magical checklist)

If you’re a retailer considering a tool like Ellis—or any conversational analytics layer—evaluate it like you would any system that can influence pricing, inventory, and product decisions.

1) Grounding and traceability

  • Can the system show the underlying data and model outputs?
  • Does it cite sources (datasets, surveys, time windows)?
  • Can you reproduce the answer later?

2) Role-based access control (RBAC)

  • Can the assistant see future assortment plans for everyone?
  • Can store managers see supplier margin terms? (They shouldn’t.)

3) Quality of the underlying data

Conversational UX won’t fix messy product catalogs, inconsistent attribute schemas, or incomplete consumer feedback sampling. It will just explain the mess fluently.

4) Fit to retail workflows

  • Does it work in line review, assortment planning, and pricing meetings?
  • Does it support market-level and segment-level decisions?
  • Can it handle the “what-if” questions your team actually asks?

5) Measurement plan

Define success metrics before rollout: markdown reduction, forecast accuracy, conversion uplift, time-to-decision, or planning cycle compression. If you don’t measure it, you’ll end up with the world’s most expensive chat window.

What this means for retail teams: new skills, new roles, and fewer “where did that number come from?” moments

Conversational analytics changes not just tools, but how teams work.

Merchants and planners

Expect faster scenario discussions and less dependency on an analytics specialist in every meeting. The best teams will treat the copilot like a junior analyst: fast, tireless, occasionally wrong, and in need of supervision.

Analytics teams

Analytics doesn’t disappear; it shifts. Analysts spend less time building one-off views and more time improving data quality, defining metrics, validating models, and building governance and adoption frameworks.

IT and security

Security teams will need to get comfortable with AI systems that generate text, store prompts, and potentially integrate with external models. Clear data handling and vendor due diligence become non-negotiable.

The retailer’s strategic dilemma: build, buy, or bolt-on?

There are three paths to conversational analytics:

  • Build: You create your own assistant over your data stack. You get control, but you also get maintenance and model risk.
  • Buy: You adopt a specialized solution (like First Insight for consumer feedback-driven decision intelligence). You get domain focus, but you need integration discipline.
  • Bolt-on: You add a generic “chat with BI” layer to existing dashboards. Quick, but it may lack retail nuance (assortment, price elasticity, localization).

First Insight’s bet is clearly on specialization: a retail-focused conversational interface tied to its consumer feedback and predictive modeling platform, rather than a generic chat wrapper. citeturn2search4turn1view0

Where this goes next: from conversational insight to conversational execution

Today’s conversational analytics helps you understand and decide. The next step is execution: generating the change request, updating the plan, triggering the workflow, and tracking results—without leaving the conversation.

That’s the “agentic” direction many large vendors are pointing toward, with copilots that can do more than answer questions. In retail, the execution layer is where the biggest gains—and the biggest risks—live.

If 2024 was the year retail experimented with gen AI, 2025 was when pilots spread, and 2026 looks like the year conversational interfaces become a default expectation in both customer-facing and internal systems. Deloitte’s 2026 outlook explicitly frames an “AI-led marketplace” dynamic that forces retailers to develop agility, intelligence, and discipline. citeturn3search3

Conclusion: the interface war is moving inside the retailer, not just to the shopper

The AI News RSS item is about a specific product (Ellis) and a specific claim (retail AI should be dialogue, not dashboards). But the broader story is that the user experience of analytics is becoming as important as the analytics themselves.

Retailers who win won’t necessarily be those with the fanciest LLM. They’ll be the ones who:

  • Have clean, governed data foundations
  • Can embed insight into decision moments (not just reporting moments)
  • Measure outcomes relentlessly
  • Design AI systems that are helpful, constrained, and auditable

In other words: less “AI will change everything,” more “AI will change the Tuesday afternoon assortment meeting.” Which, in retail, is basically the same thing.

Sources

Bas Dorland, Technology Journalist & Founder of dorland.org