What CPG and Retail Leaders are Focusing On to Drive Growth and Real Transformation

How can consumer products and retail companies drive profitable growth in an era defined by complexity, disruption, and AI? That was the central question that more than 90 senior leaders set out to address at the Growth Leaders Summit in London, hosted at the top of the iconic Gherkin. 

Co-hosted by o9 Solutions and Bain & Company, the gathering brought together executives from Kroger, Kingfisher, Danone, Unilever, Specsavers, EPIC Conjoint, and others to explore how Revenue Growth Management is evolving from a pricing discipline into the strategic engine for commercial planning, execution, and customer value creation.

Kicking off the day, Adam Ben-Yousef, event host and Senior Vice President of Revenue Growth Management at o9, reminded the audience that AI is “not just another tool but a part of a generational transformation.” But he also urged leaders to resist the hype and focus instead on building the knowledge models and processes that make AI truly valuable: “Without them, AI simply scales inconsistency,” he explained.

“But with them, it can radically improve how we plan, forecast, and make commercial decisions.”

Kingfisher: Turning AI Investment into Real-World Efficiency

Ben Sutherland, Head of Product at Kingfisher, shared how the ideas Adam outlined in his opening keynote are playing out in practice at a brick-and-mortar and e-commerce player in European home improvement retail, with banners like B&Q, Screwfix, and Castorama.

Ben explained how Kingfisher is investing in AI, data, and infrastructure to improve customer experience and efficiency. “We’ve seen a very real impact on both margins and efficiency from our AI investments,” he said, highlighting its new predictive capabilities for promotions and seasonal stock. “When you’re moving £3 billion worth of product in a year, even small improvements in stock management and markdown optimization have a significant impact on the P&L.”

But he also acknowledged the challenge of scale in regards to leveraging cutting-edge digital tools, noting that “our analysts want data in real time, and our infrastructure has to keep pace. Without that, the technology can’t deliver its full value.”

Ben closed with a few key takeaways from Kingfisher’s journey:

• “Focus on the foundations. The decisions you make early on about data, infrastructure, and process determine how far AI can really go.”
• “Avoid creating new legacy. If you move too fast without fixing the root causes, you just build new layers of complexity.”
• “Encourage experimentation. Some of the most powerful AI use cases start small in the hands of teams improving their own day-to-day work.”

EPIC Conjoint: Can AI Be Trusted to Set Prices?

Pricing is one of the biggest decisions in consumer goods and retail, so EPIC Conjoint’s Director of Survey Operations and Customer Success, Louise Verde, ran a simple test to see how publicly available large language models (LLMs) stack up against traditional methods.

The research firm compared generic LLMs (like ChatGPT and similar models) with a traditional conjoint study of 388 U.S. toothpaste buyers. Human shoppers behaved as expected: brand mattered most, and demand fell gradually as price went up.

The LLMs, however, did not follow that pattern. They placed too much emphasis on functional features like fluoride content, undervalued brand and taste, and assumed shoppers were more price-sensitive than they are. At higher prices, they sometimes produced inconsistent results. Grok came closest on brand strength (think Colgate and Crest), but none of the models matched the human decision pattern. Nor could they reveal real differences between shopper segments without being “taught” those patterns from human data.

When asked if she would let GenAI set prices autonomously, Louise said, “On its own, the answer is no.” At least, not yet. Despite “early results showing promise, they also expose a critical gap,” she explained, “without real human behavioral data as a foundation, AI-generated pricing preferences drift from market reality.”

Kroger: Connecting Data to Decisions That Move the Shelf

After pricing research, the conversation turned to how value is actually captured through execution at Kroger, the largest traditional supermarket operator by revenue in the U.S. With 2,700 stores across 35 states, their main commercial challenge is not a lack of data but rather converting it into consistent, local actions that protect revenue and margin on every trip.

“We have too much data,” said Caroline Keating, Category Manager at Kroger. Too much data without connected workflows leads to margin leakage through mis-set planograms, out-of-stocks, and wasted promotional spend. She explained that she manages $3B in the dairy category, where shelf space can range from 45 feet of yogurt to just eight feet depending on the store. Every disconnected decision risks lost sales per linear foot.

Because the impact shows up at the shelf, the solution must start there. She described AI pilots that tailor planograms and reduce basic execution errors, helping protect on-shelf availability. “We can spend as much as we want on marketing and price,” she said, “but if the customer comes to the shelf and we don’t have what they want, we’ll lose them.”

She closed by explaining that automation should ultimately eliminate low-value noise so leaders can focus on the decisions that truly move revenue, one shelf and one store at a time. As Caroline joked, “I’m the CEO of yogurt. I don’t need every dropped case emailed to me.”

Bain & Company: Competing in the Age of Algorithms

What happens when an algorithm writes your grocery list? It’s no longer a thought experiment. More shopping now begins with a search box or a voice prompt, so software increasingly decides what gets seen first and what falls away.

Against that backdrop, Bain & Company associate partner Pieter Janssens opened with a simple test. Ask a language model for a grocery list. Then ask again with a constraint such as lowest price or highest nutrition. “Your brand likely disappears on the second list,” he warned. Once a constraint is applied, the agent reshapes the choices and sentiment gives way to calculation.

That shift changes how brands compete. If agents sit between shoppers and shelves, selection tilts toward what can be measured. Availability, value, quality, and clean, machine-readable product data move to the front. Brand equity still matters, yet it cannot make up for weak fundamentals or missing information.

The implication extends beyond agents to how companies run growth. To compete now, firms need modern Revenue Growth Management capabilities that can sense, decide, and act across price, promotion, assortment, pack and price architecture, trade terms, and retail media, with store-level workflows that close the loop. But competing at a high level also requires real investment.

Top players already fund the capabilities that keep them visible and fast. As Pieter explained, if companies do not want to get left behind, they need to “invest about 3 percent of revenue in technology and data to operate at the level of top performers.”

Danone: Redefining Sell-In Through Data and Collaboration

Sell-in is the working interface between manufacturers and retailers. It’s where both sides agree on what to stock, how to price it, how to promote it, how to support it with media, and how to keep it supplied.

Paula Karhunen, Head of Sales at Danone, said that this interface is changing “from a static routine to a space for innovation,” because data, retail media, and shelf-level automation now shape decisions in real time. But as automation spreads, the stakes get higher. “When big decisions scale, big mistakes scale too,” she cautioned. If a plan is misaligned, the error does not stay local. It rolls across banners and weeks and shows up as wasted spend or empty shelves.

Her remedy is to change how partners work together. She called for courageous collaboration, which means joint planning that connects marketing, trade, supply, and sustainability into one flow of work. “We need to become much more cross-functional in collaboration to bring to life the shopper’s mission,” she said. 

In practice, that means retail media targeted to real missions, promotions that fit those missions, and supply coordination that reduces waste while keeping product available when and where shoppers need it.

Unilever: Creativity First, AI Second

What role should AI play in brand building? At Unilever, it amplifies rather than replaces human ideas. “Creativity first. Strength, scale, and speed second,” said Isabel Massey, Unilever’s Global Head of Media & Integrated Brand Experiences. The order is intentional because, as she put it, “As technology grows, nothing matters more than human connection and community. That need to belong is only getting stronger.”

To translate that philosophy into operations, Unilever starts by codifying each brand’s DNA. The team documents what the brand will do and what it will not do, so AI can plug in safely and generate consistent work across markets and formats. With those guardrails in place, the boundary becomes clear. “We never, ever, ever touch the product.” AI supports the brand; it doesn’t redefine it. Its role is to adapt content at speed while the core promise stays intact.

In execution, that means taking a familiar creative and fitting it to the moment without changing what the brand stands for. For example, “Get Ready With Me” was a popular social video format that followed people during their pre-party routines. Leveraging AI, Unilever was able to quickly create a post-party counterpart—“Get Unready With Me”—focused on winding down and skincare removal. AI accelerated the variations—scripts, edits, captions, and languages—so the story could meet people where they were, while the product and the brand’s essence remained the same.

As Isabel reflected, it’s not only about “how we build brands using machines, but also how we build brands totally away from machines, where there’s only human connection and nothing else.” That balance is the future of brand building: using AI to move at the speed of culture while never losing sight of what makes brands—and people—human.

Specsavers: Modernizing a Legacy Without Losing Its Soul

Specsavers operates at the crossroads of healthcare and retail, providing eye and hearing care across 11 countries. Each store is a joint venture co-owned by local optometrists or audiologists, combining clinical expertise with a trusted community presence. That model has fueled growth to nearly 50 million customers worldwide, but it has also created fragmented systems, inconsistent data, and layers of legacy technology.

Such complexity challenges set the stage for the company’s current transformation. As Group Commercial Director Ian Hockney put it, “Throw a problem at a CIO and say ‘see you in three years,’ and it’s almost certainly going to fail.” Specsavers is addressing that challenge by shifting ownership of transformation to the business. Strategy now sits with the Board and executive team, while a Business Design Authority helps coordinate decisions across markets so local stores can stay nimble while the organization remains aligned.

The work begins with a clear purpose. Every project starts by mapping the full customer journey, from a clinical exam to product pickup and aftercare, so the role of technology is defined at every step. This approach ensures that digital investments are guided by customer outcomes rather than technical ambition.

Strong data foundations are central to the effort. The near-term goal is a single customer view, or “golden record.” Reaching it requires cleaning duplicate records, fixing master data processes, and establishing firm governance. Better data supports better care and enables a more consistent retail experience for customers in every market.

Bain & Company: Building RGM That Works in the Real World

“There is no better moment to talk about revenue management within consumer products,” said Marco Caldarelli, Partner at Bain & Company, opening his remarks on the state of Revenue Growth Management. The urgency, he explained, comes from a fundamental shift in how growth and profitability now interact. “The steady growth algorithm has been fundamentally broken.”

In today’s environment, companies can no longer rely on the old playbook of steady price increases and predictable volume growth. “To deliver earnings—particularly for a Wall Street company—price has been a lever pulled hard in recent years to drive revenue. However, we can’t ignore the impact of the price-volume relationship,” Marco explained. “When volumes drop in consumer products—especially in categories like personal care—factories run below capacity, your benefit cost goes down, and to hit bottom-line targets, you end up cutting investments in brand building, R&D, and long-term growth.” The result, he warned, is a vicious cycle of shrinking volume, reduced competitiveness, and short-term fixes that erode value over time.

Marco argued, RGM has never been more critical. But it also must evolve. Too often, programs “start in the wrong place. Too many push for sophistication—shiny analytics—totally forgetting what actually makes the difference: integration in the real world.” True value, he said, appears only when algorithms turn into repeatable, frontline actions—when technology meets execution.

However, according to a recent study by Bain, only 5% of consumer packaged goods companies have successfully achieved that integration. The vast majority, Marco noted, “have sophisticated analytics but low integration into commercial workflows.” In other words, they’ve built the engine but haven’t yet connected it to the wheels.

To make that shift, Marco urged leaders to manage three trade-offs rather than chase a single silver bullet:

• Results now vs. capability later. Treat them as two distinct jobs—capture short-term gains while building the muscle for sustainable performance.
• Tools vs. workflows. Embed RGM in daily frontline routines so it becomes easy to use and hard to ignore. “Sales-friendly workflows,” he said, always outperform standalone dashboards.
• Sophistication vs. scale adoption. Start with adoption first; once people build the habit, sophistication can follow.

When teams get this sequence right, the impact can be “massive,” Marco noted—delivering more than 10 percent gross margin improvement while still driving category growth.

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