Redefining the Possible: Learnings from Analytics Unite 2026

Analytics Unite 2026, held April 7-9 at The Drake Hotel in Chicago, brought together more than 250 senior leaders, innovators and data experts from across the consumer goods and retail industries. Building on the momentum of previous years, this year’s summit focused on the theme of "Redefining the Possible" — moving beyond experimental pilots to reimagining how data, analytics, and advanced technologies can have long-lasting, enterprise-wide impact.

Over three content-packed days, attendees from iconic global brands and retailers shared how they are leveraging agentic AI, semantic data layers and human-in-the-loop workflows to scale innovation and navigate a volatile global market. With visionary keynotes, hands-on technical workshops and collaborative roundtable discussions, Analytics Unite 2026 served as the premier destination for sharing strategies that deliver measurable, real-world results.

Here’s a look at the key takeaways from this year’s sessions.

DAY ONE

Keynote

How Reckitt Keeps Retail Execution Timely and Cost-Efficient With AI

Reckitt CIO of North America Varun Kakaria is well aware that many organizations are overusing artificial intelligence, and his approach to the technology has been to first focus on the basics and then use AI as a tool to make processes smarter. 

One business area in particular that he said has untapped opportunity for this type of automated efficiency is retail execution — a naturally fragmented area due to the unique needs of varied retail partners and the markets in which they operate. 

During the opening keynote, Kakaria said retail execution requires an archetype-based approach, but not so much that strategies are differentiated for each market that they become siloed again.

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Workshop 

The Agentic Commerce Blueprint — Accelerating Growth for Consumer Brands and Retailers 

Sangeetha Chandru, SVP of AI and analytics for EXL Service, led a workshop regarding the shift toward agentic commerce and AI-driven consumer behavior.

The transition from keyword-based searches to natural language conversations is fundamentally changing how products are discovered and purchased. 

Charu Pujari, SVP, engineering and AI, for Loblaw Cos., joined the conversation, sharing details about one of Canada’s first agentic commerce experiences via a ChatGPT app, which can synthesize 52 weeks of purchase history, dietary preferences, real-time inventory and delivery slots to create a personalized, ready-to-purchase basket.

Attendees then engaged in conversation about their own progress with agentic capabilities and how they hope to grow in the space. 

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DAY TWO

Keynote 

The W[AI]s of Working: Rethinking Growth, Profitability, Consumer Reach

Founder and CEO of Confluencer Commerce Bryan Gildenberg kicked things off with a keynote that delved into the major challenges companies face as AI reshapes the commerce landscape. 

Here are a few key takeaways:

With the deep macro uncertainty in the world right now, there’s nothing more important from an analytics point of view than getting faster and more current with price elasticity and promotion analytics. 

Regionally, specificity and speciality are three profiles that analytics can help companies align against in a more engaging way.

There’s going to be an increasing shift in in-store media as retailers try to figure out how to monetize in-store assets in interesting ways.

As the TikTok-ification of content changes the way shoppers find and discover brands, it will become increasingly important to figure out how to make social media content more commerce-enabled.

One of the real traps in a “data-driven” organization is that the data reflects the world of yesterday but the behavior reflects the world of tomorrow.

It’s important to understand the connection between social content, social commerce and commerce and how important it is to build the wiring, measures and mindset from a business point of view to attack this problem in a more holistic way.

Bring context to contextual conversation. It’s about caring less about the demographics of who you’re selling to and more about what they are trying to do and where they are in the purchase journey and cycle.

Session

Why Enterprise AI Fails: The Data Readiness Gap No One Talks About 

Enterprise AI models keep getting better, but companies won’t achieve meaningful scale until the underlying data is ready. 

“Every conversation with my team and stakeholders is towards scale versus speed versus perfection,” said Meenakshi Ghoge, senior director of data and analytics at Mondelez International. “At the same time, we need to keep learning because AI is coming up and doing things every single day.”

Indy Cho, AVP of data products and data science for Costco, had to set a strategy to simplify the amount of reports from different sources he initially encountered at Costco (“You’ve all seen the show, 'Hoarders,' right?” he jokingly asked the audience). 

“It’s trying to figure out how to set the strategy to simplify that complexity,” he said. “When you think about how much is out there and the promise of AI, it's not a choice of, 'Should we get moving to get our infrastructure ready?' but instead, 'We have to be moving together.'” 

Ghoge said getting from data to decisions is “no longer an IT conversation or a business conversation; it’s an enterprise conversation.” 

Decision-makers need to consider inconsistencies in existing metric definitions, knowledge extraction and data ownership.

"The challenge is that cost is invisible until something breaks and I can't scale a monthly forecast of daily sales,” she said. 

For those in the middle of the data readiness journey, Cho said, “go slower because you have to bring others along."

Ghoge agreed with starting small and going slow.

“Build accountability to pick up speed at scale, encourage conversations and get official data contracts started because that takes the organization along,” she said. “Very seldom is IT the owner of data; it mostly lies in the business.” 

Session

Where AI Actually Pays: Transforming Order Processing into Measurable Margin

When it comes to manual operational processing, AI can help with everything from speed to accuracy and margin. 

“Order processing is not the most glamorous part of the business, but it's certainly the most impactful,” said Patricia Nugent, industry executive director of retail for Oracle, in her session with Sampath Vuluvala, director of business solutions of Central National Gottesman. 

Vuluvala has used AI before but finds the most value in its ability to understand unstructured orders coming in different formats and from different vendors. Inputs can show up via email, PDFs and EDI files to name a few, “and those constantly change,” said Nugent. 

While small amounts of data can be handled by humans, “if that person is on vacation, or a new person comes in, that's where the challenges start cropping up,” said Vuluvala. 

Nugent added that “an operational issue then becomes a financial issue, and then it's a relationship problem.” 

Vuluvala said AI helped speed up partner onboarding, and has allowed 90% of his orders to be touchless, with a human barely being used, “but they still have control of knowing exactly what happened along the way.” 

Along with having a great foundation in place, here are few other things Vuluvala has learned:

• Don’t hesitate to experiment. Not all AI models work the same way.
• Don’t try to jump into creating an AI agent or a chatbot on a particular business for the particular problem. Look at the whole business process flow, and identify areas where AI could help. There could be multiple places where different types of AI could help.
• Do think about it from end to end. What is it that you want? What is the input coming? Think about all the whole nine yards before you start building anything.

Session

Field Intelligence: The Case for Closing the Execution Gap 

In this session sponsored by GoSpotCheck by Form, Emily Nash, senior manager of retail productivity and strategy for Bodyarmor and The Coca-Cola Co., discussed closing the gap between strategy and the retail shelf. 

To do so, she's built a specialized sales team that focuses solely on sports drinks in high-growth markets, primarily targeting large-format stores. Additionally, she's moved from a checklist mentality to strategic field execution, so reps know exactly what to do regarding promotions and customer orders.

Overall, she said a modern approach requires visibility into REX impact, clarity on what reps need to do and constant feedback loops.

"When you're in thousands of stores, small decisions can compound. As a brand, you must differentiate yourself and meet the consumer where they are," said Nash.

Session

Scaling Autonomous Supply Chains With Human-AI Workflows 

Abhideep Dasgupta, manager, value engineering, Celonis, said the key to winning in human-AI workflows is to move away from making decisions in a vacuum and toward a unified, agentic architecture. Companies can't simply "bolt on" AI to legacy systems and expect miraculous results. 

"We have all this data feeding in, and reports and analytics, but we need to start allowing AI to come up with the insights from that and also take action against it to create an autonomous supply chain where humans and agents are working in the same space," he said. 

What's holding companies back? Most supply chains suffer from a disconnect between the decision layers and execution layers. And AI is often lacking the business context necessary to provide intelligent answers, according to Dasgupta.

He proposed adding a "semantic graph" between the data layer and the action layer. This is a living map that understands how the business actually operates, rather than just where data is stored.

Instead of viewing this process as a decade-long overhaul, Dasgupta recommended a composable approach in which organizations slowly remove old systems and plug in new AI-enabled capabilities to gradually build an end-to-end autonomous supply chain.

Expert Spotlight

Session

The New Intelligence Layer for CPG: How Unified Consumer Feedback is Reshaping Decision-Making 

The voice of the consumer is one of the most underutilized sources across organizations, but how do you manage it when the signals are scattered, buried and unstructured? 

In this session, Yogi CMO Mariya Babaskina pointed out the major data organization challenges like fragmentation, blind spots and bias, missing insights from the “core middle” of customers who have valuable opinions (but may not leave a public review), and speed to insight. 

“There’s always this unique balance of having a quality insight that you feel incredibly confident in delivering and handing over to someone else, and how quickly you can get to it,” said Babaskina. “And that's where the song and dance is interesting because by the time you reach a place where you feel confident with the information you're delivering, the moment to act has already moved on.” 

Companies can go the more difficult DIY route to unify consumer feedback, or they can work with a third-party vendor to help move from reactive reporting to proactive strategy, as director of DTC, martech and digital compliance Jennifer Peters did at OLLY. 

From dealing with product reformulations of their vitamin gummies to packaging issues, Peters needed help to measure the impact of launching new formulas, which allowed for unified transparency across the company and gave teams a much-needed common language. 

“Our QA team does not speak a marketing language, but when you're all getting your information from the same place, suddenly you have a point of contact and a point of context that you didn't have before, and you could have much more intelligent conversations around what different teams are seeing.”

Panel

How Mars, Church & Dwight, PDC Brands, Lowe's Build Intelligent Supply Chains

When it comes to supply chains, volatility comes with the territory. From unpredictable consumer demands to global disruptions, a linear supply chain is no longer a realistic or efficient model. 

AI can help retail and consumer goods companies make decisions in real time, pinpoint growth opportunities and pain points, and speed up systems performance, but to what extent?

Janice Burk, VP of technology supply chain for Lowe's, Alexander Cunningham, director of advanced analytics for Church and Dwight Co., Kristen Daihes, global VP of supply chain for Mars, and Sulabh Jain, head of supply chain for PDC Brands, spoke about how they implemented AI within their own supply chains, and what they learned along the way.

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Panel

Creating Today’s New Performance Cultures

In the race for companies to implement new tools, performance culture also needs to evolve. Here are a few takeaways as CPG and retail decision-makers discussed strategies to nudge organizations toward a higher-performing, data-driven culture.

"It matters what companies say — and if they're just talking the talk of data-driven versus actually doing it. Announcements are great, but they don't really necessarily imply that there's adoption in alignment.” 

“We do a learning Friday email every week, so just kind of building that community has helped people have one spot that they can go to, try and learn, and see how the tools and data can apply.”

— Todd Hassenfelt, Global Digital Commerce Senior Director, Strategy & Execution at Colgate-Palmolive

“We have rewards for failing responsibly. When you learn something, that is one of the most important things because you can iterate quickly on it.”

“Have curiosity with all of the tools available. It's all about making sure you have the right measures in place, and people know what matters most. And when they do, you can start going through the layers of 'whys.' And when you can teach someone those 'whys,' they start to get really good at asking the right questions that are going to drive the outcomes that you want.” 

— Elaina Wheeldon, VP, Technology Pricing, Promotions, Competitive Intelligence, Merchandising & Marketplace Technology, Lowe’s

“Data teams now need to feel like they are a part of the solution, because with AI as part of the toolkit, how do we solve problems?” 

“Allow your teams to experiment. Give them space to learn fast, and you'll be surprised at what innovation can come out when they have the time and the space to do it.”

— Sam Wong, Senior Director of Data, Analytics & AI, Mark Anthony Group 

Expert Spotlight

DAY THREE

Panel

Retail & Consumer Goods Analytics Study

CGT's latest Retail & Consumer Goods Analytics Study moves beyond general trends and captures practical applications of how organizations are defining data and analytics on the front lines in 2026. The key research pillars were organization evolution, investment and scaling, AI/emerging tech, collaboration gaps and maturity benchmarking. 

Here are just a few takeaways from a panel of experts who weighed in on how they’re navigating shifts in real time.

Where are We on the Analytics Maturity Curve?

“The earlier parts of the curve were about insights and dashboards and humans pulling the insight out of information. The latter part of the curve is about decision-making, action-taking, less human involvement and more autonomy. Companies are mostly in the middle. We're not early, we're not mature. It’s mostly because predictive and prescriptive analytics haven't yet been embedded into the day-to-day workflows. Why? Certain functional analytics aren't at the same level of maturity. Data is an abundance, but not yet translated into decision clarity, and AI is pushing conversations, but it's not pushing practice.” 

— Linda Corn, Partner of Growth Consulting, Circana

What’s Slowing Companies Down the Most? 

“It’s operational structures. Our clients are not saying that it’s an analytics problem; it’s a decision-making problem. It's about the organizational structures and the talent.” 

— Chris Daniel, GM of Global Retail and Consumer Goods, Toptal

Where Should Organizations Prioritize Investments when Resources are Limited?

“Speed is going to be the most important aspect. You cannot achieve this without analytics. Whether you use technology, talent or platforms is your call. But if you're delaying anything and waiting for it, there’s going to be trouble.”  

— Atul Arya, Founder, CEO, Blackstraw 

“I think there's a cost to developing AI, and the idea is to choose a big use case. When you choose a use case that only has so much value and has significant cost in developing, it’s tough to justify ROI. But a big use case justifies the AI and implementation. Try to have at least three really big ideas across your brands.” 

— Shiva Jayaraman, SVP, Consumer Business & LATAM CEO, Wipro

Panel

Mars, Amazon, Chicago Cubs Leaders Prepare for the Future of Agentic Commerce

As agentic AI reshapes the consumer shopping experience, it’s important that consumer goods and retail companies consider data-driven strategies that will not only help deliver a seamless experience but also a personalized one that consumers can trust. 

But how does this happen when AI bots change the way content ranks and brands are suggested?

Three consumer goods experts — Udit Mehrotra, head of product for Amazon's languages experiences business in North America, Vijay Veeraghattam, director of data science and AI innovation at Mars Snacking, and Sraavya Pathsamatla, assistant director, database marketing for the Chicago Cubs — discussed how they are considering the impact of agentic AI in the commerce landscape.

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Fireside Chat

How Mars' Ramesh Kollepara Rethinks Infrastructure for Modern Intelligence Practices

A few years ago, big infrastructure was about moving to the cloud, giving enterprises the velocity needed to drive growth in their organizations, according to Ramesh Kollepara, SVP, global CTO, Mars Snacking. 

But something has fundamentally changed in the last 18 months, where intelligence has become its own infrastructure layer, and AI is no longer just a capability but an integral part of operations.

During the closing keynote, Ramesh shared that modern intelligence practices have drastically changed how fast organizations can make decisions, how they can collaborate internally, and how well they can gather and analyze insights. They have transformed operational processes from reactive to proactive. 

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