Data topics

This buyer's guide compares the leading AI procurement software platforms in 2026, evaluating how they help organizations transform procurement from a transactional function into a strategic driver of business value. The guide examines capabilities across spend intelligence, supplier performance analytics, contract and purchasing insights, sourcing optimization, risk monitoring, invoice and payment analysis, and value recovery. It also compares AI-native procurement platforms, source-to-pay suites, and analytics-first solutions, highlighting where each approach excels depending on an organization's procurement maturity and technology landscape.

AI agents are quickly moving from pilot projects to production deployments across consumer packaged goods organizations, but understanding where they create real business value versus where hype still dominates remains a challenge. This field guide explores the AI agent use cases that are actually being deployed across category management, revenue growth management, trade promotion optimization, demand forecasting, shopper insights, supply chain analytics, retail execution, and omnichannel commerce.

Category management is evolving from periodic reporting and spreadsheet-driven analysis to continuous, AI-powered decision-making. This guide compares six of the leading category management platforms that CPG teams are actively evaluating in 2026. The comparison examines capabilities across category analytics, assortment optimization, shelf and space planning, retailer collaboration, demand insights, promotional performance, and AI-driven recommendations.

SKU rationalization in CPG isn’t just “cut the bottom 20%.” This post explains why spreadsheet-led, structured-data-only frameworks (POS, margin, velocity) miss the real why behind underperformance—and how full-context rationalization combines structured metrics with unstructured intelligence (reviews, competitive signals, research) to separate “kill it” SKUs from “fix it” opportunities. It also shows how agentic analytics enables continuous monitoring and automated root-cause decomposition so portfolio decisions stay current instead of stale.

This blog argues that most CPG assortment decisions are running on “half a brain” because they rely almost entirely on structured data (POS, margin, distribution) while ignoring the unstructured side (reviews, retailer call notes, JBP decks, social listening, competitive docs). The result: repeated SKU rationalization cycles that cut tails but don’t fix the underlying portfolio problem, leaving shelf space vulnerable to private label and faster insurgent brands.

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.

Learn how PepsiCo is using Tellius to streamline data integration and report generation through interactive dashboards and reports, unlimited drilling and sharing, and natural language ease and self-service analytics.

There’s a growing need for CPG companies to pull together all relevant information faster decision-making. Here’s how AI-powered analytics is helping.

Data-driven decision-making is vital to CPG companies as consumer preferences and buying patterns change. Learn how self-service analytics can help.

Learn how one company increased their ROI much faster with AI-powered segmentation and targeted campaigns compared to traditional methods.