AI Assistant for Operational Decision Support Using Enterprise Data

• turned metrics and deviations into clear recommendations backed by specific numbers;

• suggested next steps (Next Best Action / Next Best Offer) based on comparisons to the plan and a comparable peer group, as well as internal rules/reference data;

• generated structured talking points for customer communication (short prompts to discuss assortment, sales, and equipment) without replacing the system of record.

Challenges

• Different data channels and varying data quality. Some information is available via REST API, some comes as scheduled extracts, and updates can be delayed. This required a robust synchronization approach and graceful handling of incomplete data.

• Factual accuracy of recommendations. We could not allow discrepancies between the assistant’s advice and the corporate system, so conclusions were strictly tied to verifiable input metrics.

• Clear explanations, not just text. Users needed not an abstract suggestion but an explanation grounded in concrete numbers and comparisons so decisions could be checked quickly.

• Operational manageability. We needed transparent integration error diagnostics, retries on failures, and observability so support would not turn into manual incident investigation.

Solutions

• Integration with fallback. We implemented the primary data exchange via REST API and a backup path via CSV/XLSX extracts and ETL, and added caching to reduce dependency on source availability.

• Standardized input for analysis. We introduced a unified “data packet” format assembled before recommendation generation: entity metrics, transaction/order history, comparison with a peer group, reference data, and plan targets (when available).

• Transparent output with user confirmation. We made conclusions verifiable: key source data is displayed alongside recommendations, and proposed next steps require user confirmation.

• Reliability and observability layer. We set up error handling and retries, centralized logs and metrics, and added alerts to monitor stability and data quality.

What We Delivered

• Production AI scenarios within the user flow. We implemented an end-to-end path from selecting an entity to receiving recommendations and prompts, including generation of explanations and numeric justification based on computed KPIs.

• RAG for enterprise context. We connected search over the internal knowledge base (visit procedures, rules/standards, reference materials) so responses rely on up-to-date internal documents and remain consistent in format and terminology.

• AI orchestration and recommendation generation pipeline. We implemented a generation service that accepts the standardized “data packet,” applies rules/context (including from the knowledge base), and returns a structured output (insights, recommendations, next steps) in an agreed format.

• Integration service and data synchronization. We deployed connectors to the corporate system, refresh schedules, and graceful degradation rules for partial data unavailability.

• Implemented calculations for comparative analytics. We introduced computation of key metrics (averages and latest values, peer group comparison, assortment mix analysis) and assembled them into a structure for analysis and recommendation generation.

• Quality checks and diagnostics. We prepared and applied integration tests and data correctness checks, as well as dashboards and logs for investigating recommendation quality and incidents.