From Fragmented Reports to Loyalty Growth: AI Reporting for Chain Retailers

The Problem

Large chain retailers have a goldmine of customer data, and it almost always lives in silos. Purchase behavior sits in the POS, loyalty program activity in Paytronix, financials in the ERP, and operational detail in the back-office applications that run the business day to day. Supply chain systems hold their own slice of the same customer.

The result is a treasure trove of insight scattered across disparate systems, where each team can see only the portion of buying behavior its own tool exposes in real time. A retailer can have a robust loyalty program, understand how customers behave around key promotions, and still be unable to assemble that picture in one place. The scale of the omnichannel signal being fragmented is public: US retail e-commerce alone ran to $326.7 billion in the first quarter of 2026, 16.9 percent of total retail sales [1], on top of the monthly retail, wholesale and manufacturing activity the Census Bureau tracks as the only such combined series [2].

Many organizations are finding out that they don't have enough analysts to service the reporting needs of the organization. With a small team of analysts responsible for data preparation and reporting as well as answering ad hoc questions from senior executives to support strategic planning across multiple lines of business, pressure mounts as the volume of requests continues to rise, creating longer and longer cycles to deliver the information they need.

We find that there is a large cost associated with the current approach: leaders require timely and accurate data to optimise the loyalty rate, but the reporting is manual, fragmented and too slow.

The Solution

Instead of relying on IT teams to manually parse out numbers for executives to interpret, AI-powered reporting uses three core features to empower business operators with unparalleled insight: data unification, agent-based orchestration, and natural language generation.

It is worth being clear-eyed about how hard the middle step is, because the honest version of this pitch is what makes it credible. On BIRD, a large-scale text-to-SQL benchmark deliberately grounded in real, messy database content, the leading model of its day reached 40.08% execution accuracy against 92.96% for human annotators [3]; on the earlier cross-domain Spider benchmark — 10,181 questions and 5,693 SQL queries over 200 multi-table databases across 138 domains — the best model scored 12.4% exact-match accuracy when tested on databases it had never seen [4]. That is precisely why a production system grounds answers in a curated, reconciled layer rather than pointing a model at raw schemas [5], and why complex questions get decomposed into sub-queries before retrieval rather than answered in one shot [6].

No more lengthy, custom reports for senior executives. Instead, the system integrates customer and operational data on an ongoing basis from a retailer's core retail systems and Jarvis Chat enables users to receive on-the-spot answers in words and pictures by asking natural language questions.

The result is rapid insights, significant cross functional alignment, and better strategic decision making to grow your loyalty program.

ROI & Business Value

Analyze this: The biggest change is that Organizations must make decisions at business speeds, not report-cycle speeds.

How We Solved It with Jarvis AI

We built an agent from the ground up on top of the Jarvis Registry that submits telemetry from various data sources.

The architecture is built on three coordinating layers: an agent gateway that secures and routes every data request, an agent registry that catalogs all approved agents and connectors, and an agent orchestrator that sequences those agents to produce a complete, executive-ready answer. The same decomposition now ships from the hyperscalers — AgentCore reached general availability on 13 October 2025 with Runtime, Gateway, Identity, Memory and Observability as distinct services [7] — and ASCENDING's registry documents tool-level access control with OAuth/SAML plus complete audit trails for every AI interaction, with no security certification claimed [8]. For a deeper look at how these components work together, see our guide on agent gateway, agent registry, and agent orchestrator architecture.

In the backend, our agentic approach to flow connects to a POS system, Paytronix, an ERP system, back-office software, supply chain management and more. The Agents continuously collect and normalize information from various systems of record to create a solid reporting foundation upon which executives and operations leaders can build to gain better insights into their businesses.

Jarvis Chat on the frontend provides intuitive analytics for non-technical business users to ask any question they wish, phrased naturally, such as:

• "Which customer segments are dropping in repeat purchase rate this month?"
• "How did loyalty redemption change after last weekend's promotion?"
• "Show top stores by loyalty growth and basket size impact."

Once a conversation with Jarvis Chat has taken place, Jarvis Chat then provides a quick and interactive answer supplemented with charts and graphs, allowing for fast analysis and decision making without having to go through the additional step of building a BI report. Trust in that answer has to be measured on more than one axis — the standard academic benchmark for language models reports seven metrics per scenario, including calibration and robustness alongside accuracy, on the explicit argument that accuracy alone hides the failure modes [10] — and the query-level record has to be turned on deliberately, since model invocation logging on a mainstream inference platform is disabled by default [9].

By orchestrating back-end agents using artificial intelligence, and analyzing conversations, chain retailers can obtain improved loyalty, with reduced wait times and less administrative burden.

Why This Matters for Future Retail Customers

Retailers don't have a loyalty data problem. They have a loyalty decision speed problem.

By unifying and making customer signals easily accessible through conversational AI, executive teams can act more quickly on churn risk, campaign performance, and product and store-level behavior signals that drive loyalty. This tempo in action reduces the organization's dependence on expensive and over-allocated analytics resources and improves the likelihood of achieving loyalty objectives.

What's next for this seasonal retail cycle? Converting existing data into meaningful customer retention metrics through the implementation of AI reporting. To understand the underlying architecture that makes this possible, explore our deep dive on agent orchestrator workflows for retail analytics.

FAQ

What is AI-powered retail reporting?

It is a reporting layer that unifies customer and operational data from the core retail systems on an ongoing basis, then answers questions asked in plain language. Three capabilities carry it: data unification across POS, loyalty, ERP, and supply chain systems; agent-based orchestration that sequences the work behind each request; and natural language generation that returns the answer in words and charts rather than a raw result set.

Why does loyalty data end up siloed?

Because the systems that generate it were bought separately and were never designed to share one customer view. Purchase behavior lands in the POS, program activity in Paytronix, financials in the ERP, and fulfillment signals in supply chain systems, with back-office applications holding the remainder. Each team therefore sees only the slice its own tool exposes, which is why no single system can answer a cross-functional loyalty question.

Isn't the real problem that we need more analysts?

Usually not. The bottleneck is that a small analyst team owns data preparation, recurring reporting, and ad hoc executive questions at the same time, so rising request volume lengthens the delivery cycle no matter how efficiently the queue is worked. Removing the repetitive report-generation load is what frees those analysts to move from producing numbers to doing high-value strategic analysis.

What questions can a business user actually ask?

Operational ones, phrased naturally: "Which customer segments are dropping in repeat purchase rate this month?", "How did loyalty redemption change after last weekend's promotion?", or "Show top stores by loyalty growth and basket size impact." The answer comes back interactively with supporting charts and graphs, so there is no separate BI report to commission and wait for before a decision can be made.

How do the agent gateway, registry, and orchestrator divide the work?

Into three layers with one job each. The agent gateway secures and routes every data request. The agent registry catalogs the approved agents and connectors that request is allowed to reach. The agent orchestrator sequences those agents so their individual results assemble into one complete, executive-ready answer rather than a set of partial ones a human still has to reconcile.

What actually changes for the executive team?

Decision tempo. Churn risk, campaign performance, and store-level behavior signals become available when the question is asked rather than when the reporting cycle allows, which reduces dependence on an over-allocated analytics team. The framing this deployment is built on: chain retailers do not have a loyalty data problem, they have a loyalty decision-speed problem, and speed is what the reporting layer returns.

References

1. US retail e-commerce sales for the first quarter of 2026, adjusted for seasonal variation, were $326.7 billion and "accounted for 16.9 percent of total sales," released 18 May 2026 — US Census Bureau (2026): https://www.census.gov/retail/ecommerce.html
2. The Manufacturing and Trade Inventories and Sales report is "the only source of monthly data on total business activities of retail trade, wholesale trade, and manufacturers," drawing on three surveys — US Census Bureau (2026): https://www.census.gov/mtis/index.html
3. On BIRD, a text-to-SQL benchmark grounded in large real-world database content, the strongest LLM tested reached 40.08% execution accuracy against 92.96% for human annotators — a 52.88-point gap that sets realistic expectations for natural-language querying over enterprise schemas — Li et al. (2023): https://arxiv.org/abs/2305.03111
4. Spider comprises 10,181 questions and 5,693 unique complex SQL queries over 200 multi-table databases across 138 domains, with train and test deliberately using different databases; "the best model achieves only 12.4% exact matching accuracy on a database split setting" — Yu, Zhang, Yang et al. (2018): https://arxiv.org/abs/1809.08887
5. The paper introducing retrieval-augmented generation frames the problem this reporting layer solves — parametric models' "ability to access and precisely manipulate knowledge is still limited," with provenance an open problem — and proposes grounding generation in an explicit retrievable index — Lewis, Perez, Piktus et al., NeurIPS (2020): https://arxiv.org/abs/2005.11401
6. A managed retrieval layer documents query decomposition, "a technique used to break down a complex queries into smaller, more manageable sub-queries," alongside configurable chunk counts, hybrid versus semantic search, metadata filtering and reranking — the mechanics behind answering a multi-part executive question — Amazon Web Services (2026): https://docs.aws.amazon.com/bedrock/latest/userguide/kb-test-config.html
7. Amazon Bedrock AgentCore reached general availability on 13 October 2025 across Runtime, Memory, Gateway, Identity and Observability, with Observability delivering end-to-end visibility through CloudWatch dashboards and OpenTelemetry compatibility — Amazon Web Services (2025): https://aws.amazon.com/about-aws/whats-new/2025/10/amazon-bedrock-agentcore-available/
8. ASCENDING's Jarvis Registry page documents "Fine-grained access controls at the tool level with OAuth/SAML integration," "complete audit trails for every AI interaction," "Real-time monitoring, analytics, and audit trails," AWS Marketplace availability and Kubernetes deployment across EKS, AKS and GKE, with no security certification claimed — ASCENDING (2026): https://ascendingdc.com/jarvis-ai/jarvis-registry/
9. Amazon Bedrock model invocation logging "is disabled by default"; once enabled it records each invocation with the caller's IAM/STS ARN, model ID, operation, timestamp and input and output token counts, queryable per principal — Amazon Web Services (2026): https://docs.aws.amazon.com/bedrock/latest/userguide/model-invocation-logging.html
10. HELM evaluates language models across 42 scenarios and reports seven metrics for each core scenario — accuracy, calibration, robustness, fairness, bias, toxicity and efficiency — on the argument that single-metric evaluation obscures how a model will behave in deployment — Liang, Bommasani, Lee et al., Stanford CRFM (2022): https://arxiv.org/abs/2211.09110