Agent Mode: Deep Research for Analytics

AI-Powered Variance Analysis for FP&A: Root Cause in Seconds, Not Days

AI-powered variance analysis automates the painful “why did we miss budget?” work that FP&A teams currently do in Excel over 3–5 days every close. Instead of testing price, volume, mix, region, and channel hypotheses one by one, an AI platform connects to ERP, planning tools, and warehouses, decomposes budget-vs-actual gaps across all drivers in parallel, and ranks the true root causes in seconds—often at dimensional intersections humans don’t check. A governed semantic layer keeps definitions for revenue, EBITDA, and variance consistent, while agentic analytics continuously monitors KPIs, triggers investigations when thresholds are breached, and generates executive-ready narratives and P/V/M breakdowns automatically. The blog explains what AI does not replace (finance judgment, strategic decisions, stakeholder storytelling), addresses common objections around messy data and trust, outlines an 8–12 week implementation path, and offers a checklist for evaluating variance analysis platforms—highlighting when this kind of AI is a strong fit for finance teams.

Why 70% of Trade Promotions Lose Money—And What AI Can Actually Do About It

Most CPG brands lose money on the majority of their trade promotions because they don’t actually measure true incrementality or ROI—just raw lift and spend. The blog explains how AI-powered, “measurement-first” trade promotion analytics fixes this by automatically building baselines, separating real incremental volume from pantry loading and cannibalization, and running root-cause analysis on every event, not just a few. With AI agents continuously monitoring promotions, teams can catch underperforming events mid-flight, reallocate spend toward tactics that work, and move from reactive post-mortems to autonomous, in-quarter optimization.

Market Access Analytics for Pharma: Detect Payer Changes Before TRx Falls

This blog explains how AI-powered market access analytics helps pharma teams spot payer policy changes—formulary shifts, new PA requirements, and tier moves—within days instead of waiting 60–90 days for claims reports. By combining a pharma-native semantic layer, data federation across formulary, claims, hub, and specialty pharmacy sources, and agentic analytics, the platform automatically investigates access issues, quantifies TRx impact, and delivers finished decks and models. The result is earlier detection, faster performance diagnosis, and field activation while there’s still time to protect scripts and pull through new access wins.