The New Growth Frontier

The rules of CPG growth are changing

For decades, CPG size and scale meant leverage. From expansive R&D pipelines to securing distribution, large brands could outspend and absorb failure in ways that smaller players couldn’t, often translating to their growth success. Speed and agility mattered but was usually secondary to size, funding, and operational capacity. 

Today, that dynamic is shifting. Margin, budgets, and speed-to-market timelines remain compressed, and organizations are being tasked with meeting evolving consumer needs, reducing risk, and delivering outsized impact.  

As a result, many brands are defaulting to the most familiar or accessible growth levers. Line extensions and mergers and acquisitions (M&As) can offer short-term wins, but neither has proven to be a reliable driver of long-term, sustainable growth—particularly in an environment where challenger brands fight for share and consumer dollars are stretched. 

The solution: innovate around consumer need states and develop a discoverability strategy, which helps to build brand awareness, trust, and lasting loyalty. 

As AI reshapes the what, where, and how of consumer behavior, purchasing decisions, and products that will win on shelf, the implications are clear: For smaller brands, there’s a window of opportunity to go on the offensive, strategically leveraging AI to elevate innovation quality and optimize relevance in LLMs. For established CPG brands, there’s an imperative to refocus innovation pipelines and take cues from their smaller counterparts, drawing on AI, where appropriate, to accelerate the pace at which they adapt to this continually evolving environment.

There’s no longer a question of whether change is coming, but how quickly brands can reset their playbooks to keep pace. 

A new era of advantage 

When it comes to speed and agility, emerging brands have always had an edge. With fewer bureaucratic barriers for risk taking, challengers often lead digitally, leaning into bold and culturally relevant messaging, viral social trends, and brand or influencer partnerships. Now, the democratization of capabilities brought by GenAI are empowering them to gain even more ground. 

To better understand the dynamics at play, we interviewed stakeholders from some of the fastest-growing emerging brands across the globe, digging into their innovation processes and how they’re leveraging AI in their day-to-day functions—including AI in product innovation. 

AI proponents argue that the technology is leveling the playing field for brands of all sizes. Phil DeConto, Vice President of Sales, Strategy, and Trade Marketing at Cibo Vita, agreed, noting that it’s helping smaller brands “punch above their weight class” across a number of functions while also reducing barriers for capabilities—think concept tests, iterative creative development, and shelving simulations—that historically favored larger brands and six-figure budgets. 

Our stakeholders also commonly cited benefits like sophisticated data capabilities, analytical infrastructure, and process transformation. In the past, smaller enterprises “spent a lot of time on strategy and little on process,” said Antonio Lanzone, a Trade Marketing Specialist at Andriani. “AI helps us reverse that. With an efficient process, we can create a better strategy.”  

But even as AI capabilities expand into seemingly infinite possibilities, our stakeholders noted that use case remains the most important criterion. “Our approach is very pragmatic,” said Florian Hartmann, Sales Director of ahead nutrition. “AI needs to directly improve speed, decision quality, and execution in store.”

According to Hartmann, one of their biggest impact drivers has been their partnership with a start-up to co-develop an AI-native, retail CRM. Not only is the platform customized for their business and market needs, its ability to contextualize information like buyer personas and interaction history also enables more relevant conversations for their sales teams.  

Changing the innovation growth equation

At its core, the path to innovation growth is deceptively simple: Start with validated, unmet consumer needs. Everything else—from format to benefit claims and even messaging—follows from a clear understanding of the problem consumers are actually trying to solve.  

This is where AI can help meaningfully shift the equation. When grounded in high-quality data, AI can accelerate every step of the product development innovation process without lowering the bar for decision-making—synthesizing insights, expanding ideation, and enabling rapid concept or formula refinement. The result isn’t just more efficient innovation; it’s smarter innovation, tying ideas directly to those unmet needs that can drive incremental growth. 

Once a concept is built on validated need and developed into a product that not only meets consumer expectations but does it better than anyone else, long-term success comes down to how it enters the market. the market. Early distribution and velocity are the strongest predictors of whether an innovation will sustain into year two. But the game has also changed for driving trial, further widening the doorway for emerging brands. Outside of in-store displays and banner ads, which can be cost-prohibitive for smaller players, social media campaigns and content creators are now firmly entrenched in the marketing mix, offering a more economical (and in some cases, more effective) lever to build consumer buzz and repeat—and get noticed by retailers.

“A single social post can reach tens of thousands in a day, at a fraction of traditional costs,” said Wayne Nah, General Manager at Paulaner China. “On TikTok, an influencer goes live, shows a product, and you buy on impulse right there. It’s become the most effective way to drive trial.” 

Nah also noted that these strategies can empower smaller brands to initially bypass retailers by launching exclusively direct to consumer (DTC) and scaling through creators, viral content, and special promotions—an approach employed by many successful challenger brands who have ultimately gone on to win exclusive partnerships or shelf space with large retailers.  

And these tactics—along with strong online reviews, widespread consumer appeal, and traction in market—will only serve to further accelerate their success when it comes to discoverability via LLMs. Strong reviews and consumer need states are at the core of how products are surfaced in results, generating a win–win for emerging brands looking to gain compounding success early on.   

Still, whether launching in-store or DTC, the ability to demonstrate velocity, scale, and stability is crucial when it comes to attracting retailer partnerships, said one leader of an accelerator program we spoke with. Their model—a full support system designed to speed impact—partners with select emerging brands that are disrupting within meaningful consumer trends.  

Once the brand is in the program, they must drive trial, awareness, and repeat quickly. “We start reviewing performance seven months in. If they’re not hitting hurdles, they’re at risk,” our retailer said, adding that they begin reviewing data and making course corrections quickly post-launch, to set brands up for success.

Relevance over reach

Imagine a marathon runner in training for a sub-five-hour finish. Instead of browsing through multiple websites, watching YouTube videos, or toggling between subreddits and reviews, she types a single question into an AI assistant: “How do I prepare to run a marathon in under five hours?” 

Within seconds, the assistant returns a personalized plan—inclusive of training schedules, nutrition guidelines, supplements, running shoes, and gear—along with links to purchase each recommended product. No endless scrolling, no deep dives down rabbit holes, just a zero-click journey that replaces dozens of micro-interactions. 

This example isn’t some futuristic consumer ideal; it’s agentic commerce in action. And it’s actively redefining how consumers discover and buy products, further upending the advantages large brands have largely grown accustomed to.  

In traditional search, SEO dominance and paid placement shape discovery. But in agentic commerce, relevance reigns supreme, as the agent evaluates structured product attributes, social content, reviews, and price against the shopper’s specific goal. In addition to opening the door for emerging brands, this new model of search and discovery is also better for consumers, elevating products that authentically meet their needs instead of “hacking” the algorithm through keyword optimizations or massive budgets, according to Ellie Thornton, a Senior Category Manager at Phizz. In other words: AI often rewards the best answer, not necessarily the biggest brand. 

Agentic shoppers could drive $190–$385 billion (USD) in US e‑commerce spending by 2030—the equivalent of 10% to 20% market share. This shift is already reshaping retail. LLM integrations with retailers or within retailer websites and retail media networks (RMNs) can recommend products, generate bundled baskets, and even propose recipes tailored to a user’s shopping history or needs. Instead of browsing a category page, a consumer might type: “I like citrus flavors, and I can’t have dairy. Which recovery drink should I buy?” The AI agent doesn’t return 100 search results to parse through; it instead returns two or three, along with an explanation why.  

The new media divide: retailer-specific vs. retailer-agnostic advertising 

Also disrupting the dynamics of consumer search and discovery: Major AI platforms are rolling out (or quietly testing) LLM advertising—a projected $25 billion opportunity that introduces contextually relevant placements directly inside AI-generated answers and reflects the growing role of AI in e-commerce. 

Early formats include:  

• Sponsored product mentions within conversational responses 

• Follow-up questions recommending branded products 

• Integrated shopping assistants that surface retailers, pricing, and add-to-cart options 

But unlike in traditional paid search, preserving consumer trust in agentic responses is key. As a result, many platforms are clearly labeling or separating ads from organic responses. This means that (for now), LLMs still prioritize the most contextually aligned product, ensuring the integrity of results for consumers and creating another case in which challenger brands can outperform category leaders. 

For retailers, these shifts pose new questions: If AI assistants recommend multiple retailers (or none at all), traffic patterns, RMN revenue, and conversion dynamics could change dramatically. Retailers will need to accelerate adoption of their own generative AI capabilities (personalization, conversational search, contextual suggestions) and may need to rethink partnerships or data sharing with leading LLM platforms—as some already are doing.

How will LLMs disrupt the brand marketing mix?  

• LLM advertising will become a standard layer of the marketing mix, but not a simple one. Each platform will have different formats, rules, and optimization paths.  

• Because algorithmic relevance outweighs spend (for now), brands with strong structured data, transparent value propositions, and cultural relevance can win—even against legacy giants.  
• These shifts in the marketing mix could compound existing challenges around data fragmentation and measurement, but they may eventually unlock new clarity around consumer motivations and cross-channel conversion. 

As agentic commerce and AI-driven discovery reshape the path from intent to purchase, a window of opportunity is opening. If a brand isn’t “legible” to LLMs or relevant to consumer need states, it will ultimately be invisible at the moment of choice.  

With this urgency in mind …