Innovation hasn’t slowed—but the rules for winning have changed.
Today’s CPG brands are operating in a three-shelf reality: the physical shelf, the digital shelf, and an emerging third shelf where AI-driven agents influence discovery and purchase. At the same time, innovation budgets are under pressure, launches are more selective, and success depends on sharper alignment with consumer values, data, and technology.
Join SPINS and industry leaders for a data-driven look at how analytics, activation strategies, and AI agents are reshaping innovation in 2026. Drawing on the latest SPINS insights and real-world brand examples, we’ll explore which innovation archetypes win, what’s driving disruption, and how brands can improve discoverability and growth in algorithm-driven commerce.
Walk away with practical guidance to futureproof your innovation strategy—and thrive in the 3-shelf reality.
IN THIS WEBINAR, YOU’LL LEARN:
Generated by AI
The webinar focused on how CPG innovation—especially across food and beverage—is shifting from a “new product launch” mindset to a broader growth system built on analytics, omnichannel activation, and readiness for AI-driven shopping. Speakers from SPINS and MikMak shared frameworks, early market signals, and practical steps brands can take to stay competitive as discovery and commerce rapidly evolve.
Innovation Isn’t Slowing—It’s Getting More Strategic
One of the most important takeaways was the apparent contradiction in today’s innovation landscape: new item launches may be declining, but R&D investment remains strong. The webinar’s explanation was that brands are becoming more deliberate about where they place bets. Instead of maximizing the number of launches, they’re using data to improve confidence, reduce wasted spend, and increase the odds that innovations scale.
SPINS framed innovation as a spectrum of risk. At the low-risk end are core expansions—new flavors, formats, or pack sizes that build on proven demand. Moving up in ambition are sideways stretches into adjacent segments aligned with brand values, then attribute adjacency plays that bring a differentiating claim (like “better-for-you”) into categories where it’s underrepresented. The highest-risk, highest-reward moves are category capture strategies—attempts to reshape or enter categories where a brand has a credible “right to win.”
A Repeatable Way to Find White Space Opportunities
Rather than relying on intuition alone, the session emphasized an analytical approach to identifying where disruption is most likely. The “white space” formula combined signals like high household penetration (lots of shoppers) with low category growth and low innovation activity. The logic is straightforward: when a category is widely purchased but relatively stagnant, it may be ripe for a meaningful change in product, positioning, or experience.
This was illustrated using practical, everyday categories—like lunchbox staples—where consumer demand is consistent, but innovation can be limited. The broader point was that growth opportunities often hide in plain sight when you use the right data lens.
Three Catalysts Are Reshaping the Market
A major portion of the webinar centered on the forces that are accelerating change. The speakers highlighted three disruption catalysts that brands should treat as immediate, not theoretical.
First, preference-based consumption has gone mainstream. What began as a natural/organic phenomenon is now present across categories: shoppers increasingly buy based on values, ingredient preferences, and functional outcomes. That shift is pushing executives to prioritize health- and wellness-led innovation far beyond the “better-for-you” aisle.
Second, “nonhuman participation” is entering the shopping journey through AI agents. The panel noted early evidence that consumer behavior is already shifting, including MikMak’s reported growth in traffic from ChatGPT referrals to brand websites. Even if fully automated agent purchasing isn’t universal yet, AI is clearly becoming a discovery and decision layer.
Third, “anywhere commerce” is now the operating reality. Shopping no longer starts and ends at a shelf or a single retailer site. It happens across retailer apps, social platforms, creator content, and increasingly within AI interfaces—requiring brands to think omnichannel by default.
The “Third Shelf” Is Here—and It’s Built on Product Data
One of the most distinctive concepts introduced was the idea of a “third shelf”: agentic commerce. Historically, brands competed for visibility on the physical shelf (placement, facings, in-store execution) and the digital shelf (search, PDP quality, retail media). Now there’s a new competitive surface: being discoverable and recommendable by AI agents.
The key implication is that machine-readable data matters as much as—if not more than—marketing polish in this context. Beautiful creative won’t help if an AI agent can’t reliably parse a product’s attributes, benefits, dietary claims, sizing, or differentiation across channels. Agent readiness starts with data readiness: consistency, structure, and completeness across brand sites and retailer product pages.
Media Has Changed—And the Old Playbooks Don’t Transfer Cleanly
The webinar also traced how brand building has evolved through distinct media eras. In the pre-social era, discovery was largely top-down through mass channels like TV. The social era introduced scalable interest targeting and helped fuel DTC growth. But privacy changes and platform shifts reduced the reliability of that acquisition model, while costs rose.
At the same time, retail media networks expanded, turning retailers’ first-party data into powerful—and often expensive—ad ecosystems that can favor incumbents. Meanwhile, discovery continues to migrate to TikTok, Instagram, and now AI tools. The consistent message was that brands that win over time are not those with the most stable plans, but those that adapt fastest to where attention and discovery actually live.
Measurement and Speed Are Becoming Competitive Advantages
Across polls and discussion, a clear tension emerged: brands are investing more in nontraditional media and AI, but many still lack confidence in measurement and optimization. The presenters argued that in a volatile environment—where platform algorithms shift and external factors can change demand quickly—responsiveness is a core capability.
The goal is to shorten the loop between planning, performance visibility, and budget reallocation. AI can help reduce manual work and support more real-time measurement, but only if organizations commit to disciplined data practices and cross-channel clarity.
What to Do Next: Practical Moves Brands Can Make Now
The webinar’s most actionable guidance clustered around readiness and measurement. Brands were encouraged to audit and improve product data so it’s consistent, attribute-rich, and structured for AI interpretation. They also recommended actively using AI tools to understand how products are being recommended, what questions consumers are asking, and where information gaps exist across brand and retail surfaces.
Finally, innovation decisions should be informed by multiple signals—AI insights, consumer feedback, and velocity/performance data—rather than treating AI as a standalone solution. The best outcomes come from combining machine speed with market reality.
Final Takeaway
The webinar’s core message was that innovation is no longer only about what you launch—it’s also about how you show up, how you get discovered, how you measure impact, and how you compete in a world where “the shopper” may increasingly include AI agents. Brands that build strong data foundations and agile activation systems will be best positioned to win across the physical shelf, the digital shelf, and the emerging third shelf of agentic commerce.
