Mucinex and Lysol Turn Gen AI Into a Daily Decision Tool

Reckitt is one of the few global consumer goods companies that has moved generative artificial intelligence (AI) out of experimentation and into daily operations at scale.

Instead of running isolated pilots, the company, whose brands include Lysol and Mucinex, rebuilt its data infrastructure first, then deployed AI agents and analytics tools that now support marketing, sustainability and enterprise decision-making.

Data First: Building an AI-Ready Decision Layer

Reckitt’s AI strategy rests on a centralized data platform built on Microsoft Azure, which unified consumer, sales, media and operational data that had previously been fragmented across brands and regions. Leadership concluded early that deploying generative AI on top of inconsistent data would amplify errors rather than improve decisions.

Reckitt embedded Power BI and Copilot capabilities into its analytics stack, allowing marketers and insights teams to query enterprise data using natural language rather than relying on static dashboards or centralized reporting teams.

According to Microsoft’s customer case study, Reckitt saw a 60% improvement in marketing efficiency after deploying these tools, while cutting time spent on routine analytical tasks by up to 90%. Internal testing showed that AI-generated insights performed as well as or better than traditional research outputs when evaluated against historical benchmarks.

Elaine Rodrigo, Reckitt’s chief insights and analytics officer at Reckitt, told Microsoft that the goal was to let teams move from data to decisions faster, while keeping humans accountable for final judgment. The company deliberately avoided using AI to make autonomous decisions, instead using it to surface correlations, anomalies and performance signals that teams could act on.

“The insights generator development journey has been nothing short of remarkable,” she said. “The purpose is to empower our marketers; to ensure that every idea they develop starts from insight.”

Reckitt extended this data-first approach into marketing operations through its partnership with TTEC Digital, integrating AI-driven insight generation into campaign planning, execution and measurement workflows across global teams.

Scaling AI Across Global Marketing

With its data foundation in place, Reckitt moved to agentic AI. By 2025, the company had rolled out AI agents to more than 500 marketers globally, embedding them into daily workflows rather than isolating them in innovation teams.

According to Digiday, Reckitt measured 20% to 40% time savings in brand tracking and performance analysis after deploying these tools.

What differentiates Reckitt’s rollout is how narrowly it targeted use cases. Teams conducted time-and-motion studies to identify where marketers spent the most time on low-value tasks, then deployed AI specifically to eliminate those steps. Executives told Digiday that repeated pilots often become a dead end, creating organizational fatigue without delivering scale. Reckitt instead designed systems with governance, training and deployment in mind from the start.

Internally, the company framed AI as a capacity multiplier rather than a headcount reducer. By automating analysis and reporting, the company pushed marketers to spend more time on interpretation, creative strategy and consumer understanding.

Applying AI to Emissions and Product-Level Data

Reckitt’s most technically demanding AI deployment sits outside marketing. Scope 3 emissions account for the majority of the company’s carbon footprint, but traditional measurement relied on representative averages that limited actionable insight.

Working with CO2 AI and Quantis, Reckitt applied AI-driven automation to calculate product-level emissions for approximately 25,000 individual products. The system matches activity data with emissions factors at scale, increasing accuracy by an estimated 75x compared with prior approaches.

What once required months of manual modeling now takes minutes. More granular data allows Reckitt to identify which ingredients, suppliers and packaging choices drive emissions, enabling targeted interventions aligned with its 2030 and 2040 net-zero targets.

Together, these deployments mean Reckitt now runs AI across more than 500 marketers, draws from roughly 35 integrated data sources and analyzes 25,000 products at the SKU level.