How Lipton Is Brewing a Smarter Supply Chain with Alation

When you think of Lipton, you probably picture a warm cup of tea. But behind every cup lies a global operation powered by data—and a team on a mission to move from simply searching for insights to solving business problems with agentic AI.

At a recent live session, “From Searching to Solving: The Data Management Shift,” Robin Rietveldt, Global Director of Data & AI at LIPTON Teas and Infusions, joined Luke McClaughlan from Alation to share how Lipton’s data strategy evolved, how they’re embedding AI into their operations, and why metadata management is key to their success.

A global brand with efficiency at its core

Lipton Teas and Infusions is the world’s largest warm-tea company, generating around $2 billion in annual revenue—three times larger than its next biggest competitor. Its brand portfolio spans household names like Lipton, Pukka, Tazo, and PG Tips.

Since being acquired by CVC Capital Partners in 2022, Lipton has operated as a standalone private-equity-backed business. That context shapes its data priorities: cost reduction and efficiency.

“Lipton’s data and AI program is designed to make us faster, leaner, and more efficient,” Rietveldt explained. “We focus mainly on finance and supply chain—areas with huge potential for streamlining business processes like source-to-pay, order-to-cash, and record-to-report.”

Turning data strategy into reality

When Rietveldt took over the data and AI function, he introduced a clear, four-pillar framework to align strategy with execution:

1. Value creation. Every AI or data initiative must be tied directly to measurable business outcomes. For Lipton, this means identifying where AI can deliver the most impact—typically in finance and supply chain. These areas are rich with repetitive, manual processes that are ideal for automation and efficiency gains.

2. Use cases. Once the value opportunities are clear, the next step is use case development. Rietveldt’s team translates business goals into specific use cases, ranging from simple reports to complex machine learning models. They cluster these into delivery roadmaps to guide execution.

3. Data foundation. Once you’ve chosen use cases, “you need to build them on top of the data foundation, and it needs to be very solid.” Alation has played an important role in strengthening that foundation through robust metadata management, data architecture, and quality practices.

4. Operating model. This “people and process pillar” keeps teams aligned. Rietveldt runs an annual, quarterly, and monthly “drumbeat” to identify priorities, align use cases, and ensure accountability. This structured cadence keeps data initiatives moving consistently from concept to delivery.

By solidifying the data foundation, fixing the operating model, and delivering quick-win use cases, Rietveldt was able to build credibility and momentum, giving the team time to plan for scale and secure executive buy-in.

“It’s not just about delivering the work,” he added. “Storytelling and communication are critical to building trust and sustaining momentum.”

From factory floors to forecasts: Where AI is already creating value

The supply chain has emerged as Lipton’s proving ground for AI innovation. Rietveldt described it as “the perfect mix” of clean, controllable data and a whip-smart workforce who are eager to adopt new technologies.

He shared three examples that illustrate how Lipton is turning data into intelligent action:

• Digital assistance for factory workers. Lipton is developing a frontline system that enables operators to troubleshoot issues in real-time. When a machine fails, a worker can query digital systems and instantly access relevant instructions and manuals.

• Prescriptive maintenance and digital twins. Inspired by Minority Report, Lipton aims to predict and prevent issues before they happen—“repairing the machine before it breaks.” Digital twins simulate production scenarios to identify potential bottlenecks.

• Computer vision for quality control. Cameras across production lines detect packaging or labeling defects and trigger instant responses on the edge. “As soon as the problem gets spotted, it can solve an issue and give commands to workers on the line,” Rietveldt explained. “That reduces costs later in the supply chain and protects our customer relationships.”

Each use case reflects a larger evolution—from reports and dashboards to intelligent, agentic systems that act on behalf of people.

Building the data foundation that powers AI innovation

Lipton’s success with AI is underpinned by its disciplined approach to data management. Rietveldt continues to ground his program in the DAMA-DMBoK framework, focusing on three areas he considers essential to scaling AI: data architecture, data quality management, and metadata management.

On the architecture front, Lipton maintains a strong data lake design that ensures seamless data flow from source systems to the consumption layer. On data quality, the team measures six key dimensions—completeness, uniqueness, timeliness, validity, accuracy, and consistency—at multiple stages of processing to maintain reliability.

Metadata management, however, has proven to be the real accelerator. When Rietveldt first joined Lipton, he recalls that “nobody knew it—‘meta-what?’” But once implemented, the impact was clear. Metadata, he explained, “allows people in your business to more quickly find, understand, use, and create value with data.”

Lipton’s partnership with Alation has been crucial in this transformation. With Alation serving as the company’s central metadata hub, teams can easily find and trust the information they need. As Rietveldt put it, “AI cannot function without having a good understanding of the context, and that’s where metadata really plays an important role.”

Turning trust into measurable impact

Rietveldt likens data management to a defensive line in football: often invisible, but vital to success. Data management sits at the beginning of the value chain, enabling every AI-driven action that follows. To make that impact visible, his team focuses on storytelling and clear measurement.

Lipton regularly tracks platform usage: who is using the data, where they are located, and what they are searching for. These metrics serve as a “health indicator” to demonstrate value to executives who want to see tangible business impact.

That visibility has paid off. The finance function, for example, has become one of Lipton’s most engaged data users, leveraging Alation to streamline cost management and even publish complex accounting manuals in one accessible place. The enthusiasm has been so strong, Rietveldt joked, that the finance team now wants to migrate their entire SharePoint library into Alation.

Lessons learned: Focus on what hurts most

Reflecting on his journey, Rietveldt emphasized two lessons that have guided Lipton’s progress.

The first lesson is to start with the real business pain points. It’s easy, he said, “to just get started and curate a data catalog,” but far better to listen to what people actually need. Addressing the issues that are truly hurting the business builds trust, excitement, and momentum.

The second lesson is to meet users where they are. Lipton integrates Alation directly into its Power BI reports, embedding contextual information such as data sources, refresh schedules, and key metrics. “Each report is equipped with a front page,” Rietveldt explained, “and this is now fed by Alation.” By surfacing metadata in the tools people already use, Lipton helps employees extract more value and insight from every dashboard.

The next frontier: Agents and self-service

Looking ahead, Rietveldt sees enormous potential in the convergence of metadata, self-service, and agentic AI. He believes AI agents will lower the barrier to information discovery, enabling users to interact with Alation more naturally and intuitively. “The application of agents will make a difference,” he said. “People will have an interface through Alation, powered by agents, to find what they need inside the platform.”

Lipton also plans to expand self-service analytics, empowering business teams across regions to find and use data independently. That effort depends on a strong metadata foundation—which Alation provides.

And the work is far from over. Curation, he noted, is “a never-ending story.” As new data sources appear, Lipton continues to refine and expand its catalog. “There’s always more data coming in, more gaps to fill, more depth to add,” he said. “It’s like cleaning your room or making your bed—the first thing you do to set the day right.”

Brewing the future of agentic AI

For Lipton, the journey from searching to solving begins with trust—trust in data, in metadata, and in the systems that bring them together.

By combining Alation’s metadata-driven platform with a clear, disciplined data strategy, Lipton is not just transforming how it manages information; it’s setting the stage for an intelligent, agent-powered future where data actively drives decisions across the enterprise.

Curious to see for yourself? Book a demo with us today.