About the Author
Josh Linkner is a five-time tech entrepreneur, New York Times bestselling author, keynote speaker, and globally recognized innovation expert. He has founded or co-founded five tech companies that sold for a combined value of over $200 million, and is co-founder and Managing Partner of Mudita Venture Partners, an early-stage venture capital firm. As Chairman of Platypus Labs, Josh helps organizations across industries build cultures of innovation and creative problem-solving.
In This Article
- Five macro shifts reshaping the industry, from AI-powered demand forecasting to the rise of agentic commerce and autonomous store operations
- Real examples of AI at work: Walmart’s self-healing inventory system, Unilever’s AI demand-sensing that saved $300 million annually, Amazon’s Rufus shopping assistant generating $12 billion in incremental sales, and more
- Three developments to watch over the next 12-36 months
- Why human creativity and judgment still matter more than any algorithm
- A 90-day action plan for CPG and retail leaders ready to move
AI has moved well beyond pilot programs and innovation labs and into the core operations of CPG manufacturers, grocery chains, mass retailers, and direct-to-consumer brands. According to NVIDIA’s 2026 State of AI in Retail and CPG survey, 91% of retail and CPG companies are now actively using or assessing AI, and 90% plan to increase their AI budgets this year. McKinsey’s research found that CPG and retail companies leading in digital and AI are delivering three times greater total shareholder returns compared to sector peers. Meanwhile, McKinsey estimates that generative AI alone could unlock $240 billion to $390 billion in economic value across retail.
AI is rewriting the rules of how products are developed, how supply chains are managed, how prices are set, and how consumers discover and purchase goods. Here’s what you need to know.
Five Ways AI Is Changing CPG & Retail
1. Demand Forecasting Has Moved from Educated Guessing to Predictive Precision
For decades, CPG companies and retailers have relied on a combination of historical sales data, seasonal patterns, and human intuition to forecast demand. The result has been chronic overproduction, persistent stockouts, and billions of dollars in waste. AI is dismantling that model.
AI-powered demand-sensing systems can now process millions of variables in real time, including weather data, social media trends, local events, competitor pricing, and macroeconomic signals, to predict what consumers will buy before they buy it. According to NVIDIA’s 2026 survey, 51% of retail and CPG companies are already deploying AI for supply chain efficiency, while 64% report increasing supply chain pressures year-over-year. The organizations that are deploying AI to address those pressures are pulling ahead. NVIDIA’s survey also found that 89% of retail and CPG respondents say AI is helping increase annual revenue, with 30% citing revenue increases exceeding 10%.
The scale of what’s at stake is enormous. McKinsey estimates that by 2030, 30 to 35% of all current activities across consumer functions could be automated. For an industry operating on thin margins where the difference between profit and loss can come down to a few percentage points of forecast accuracy, this is not incremental improvement. It is a structural transformation.
2. Personalization Is Becoming the Primary Competitive Battlefield
The era of mass marketing and one-size-fits-all product assortments is ending. AI is enabling a level of consumer personalization that was previously impossible at scale, and the companies that can deliver it are winning.
Amazon’s Rufus AI shopping assistant reached 300 million users and generated $12 billion in incremental sales in 2025, handling over 274 million daily queries. That represents roughly 13.7% of all Amazon searches flowing through an AI-powered conversational interface rather than traditional keyword search.
On the retailer side, Walmart developed Wallaby, a retail-specific large language model trained on decades of its own transaction data, to power a Content Decision Platform that predicts the type of content each shopper wants to see and generates personalized homepages tailored to individual customers.
The numbers behind personalization are compelling. McKinsey’s research shows that AI-powered personalization drives 5 to 15% revenue lift, with some companies seeing up to 40% more revenue from personalization activities. AI-driven personalization can boost conversion rates by up to 23% through real-time user behavior analysis. For CPG brands and retailers competing for the same consumer dollar, the ability to deliver the right product, at the right price, through the right channel, at the right moment is becoming the defining competitive advantage.
3. Product Innovation Is Being Radically Accelerated
New product development in CPG has traditionally been a slow, expensive, and high-failure-rate process. AI is compressing timelines and improving success rates in ways that change the economics of innovation entirely.
PepsiCo used generative AI to shorten its innovation cycle from over six months to as little as six weeks. Nestle’s R&D team used generative algorithms to accelerate formulation ideation from months to weeks, producing over 1,300 AI-backed product ideas. The Clorox Company uses generative AI to simultaneously develop hundreds of digital prototypes and test them with millions of consumers, compressing what used to be months of concept testing into days.
Yet the opportunity remains largely untapped. According to Deloitte, only 6% of manufacturers currently use AI systems for product development, but nearly 25% expect adoption within two years. The early movers are already seeing 6 to 10% increases in sales from new products, with profit margin advantages of 3 to 7% versus followers. That gap is expected to widen to 8 to 15% by 2027. The companies that integrate AI into their innovation pipelines now will have a compounding advantage that becomes increasingly difficult to close.
4. The Physical Store Is Being Reimagined by Computer Vision and Automation
The retail store itself is undergoing an AI-driven transformation. Computer vision, autonomous checkout, intelligent shelf monitoring, and robotic fulfillment are changing what a store looks like and how it operates.
The computer vision AI in retail market was estimated at $1.66 billion in 2024 and is projected to reach $12.56 billion by 2033. According to Deloitte’s 2024 Retail Tech Survey, 68% of U.S. retailers are either piloting or actively implementing computer vision. Amazon’s Just Walk Out technology is now deployed in over 170 third-party locations across airports, stadiums, universities, and hospitals in four countries.
The stakes are high. Retail shrink reached a projected $132 billion in global losses in 2024, up from $112 billion just two years earlier. AI-powered loss prevention systems typically reduce shrinkage by 15 to 30%. Meanwhile, Walmart is targeting 65% of stores to be automated by 2026, with over half of its fulfillment center operations already running on automated systems. For retailers, the question is no longer whether to invest in physical AI, but how fast to scale it.
5. The Adoption Gap Is Widening, and the Window Is Closing
Here is the part of the story that should be a wakeup call to every CPG and retail leader: nearly everyone is talking about AI, but very few have a real plan.
NVIDIA’s survey found that 91% of companies are using or assessing AI. But Bain’s 2025 Consumer Products Report found that only 37% of CPG executives rank generative AI among their top five priorities, and just 6% report having an actual plan to leverage AI for business value. 54% of retail and CPG respondents say their workforce lacks the skills to deploy generative AI effectively. Nearly 50% of retailers believe they need significant retraining, but fewer than 20% know what the new skills actually are.
This is not simply a technology problem. It is a leadership problem. The companies that treat AI as a series of disconnected experiments will fall behind those that are building integrated strategies across their value chain. McKinsey’s data shows that the CPG and retail companies that lead in digital and AI deliver three times greater total shareholder returns. The gap between leaders and laggards will only increase.
How AI Is Actually Being Used Today
Walmart’s AI-Powered Supply Chain and Retail Operations
Walmart’s AI transformation represents one of the most comprehensive deployments in the retail industry. The company’s Self-Healing Inventory System, which detects stock imbalances and automatically redirects product, saved over $55 million in waste during its 2025 rollout, with particularly strong results in perishables. The system uses AI to identify when products are at risk of expiring or sitting in the wrong location and reroutes them before value is lost.
On the product data side, Walmart’s generative AI systems have improved over 850 million product catalog data points, a task that CEO Doug McMillon said would have required 100 times the headcount to accomplish manually. The company also developed Wallaby, a retail-specific large language model trained on decades of Walmart transaction data, powering everything from item comparison to personalized shopping recommendations. AI has also reduced out-of-stock rates by 20 to 25% and helped drive U.S. e-commerce to profitability, with e-commerce sales rising 22%.
Unilever’s AI-Driven Demand Sensing
Unilever’s AI deployment offers a masterclass in how CPG companies can use AI to fundamentally improve supply chain performance. The company’s AI-powered demand-sensing system reduced forecast error by 30%, cut safety stock by 15%, and saved approximately $300 million in annual holding costs.
Unilever’s Collaborative Planning, Forecasting, and Replenishment (CPFR) model runs over 13 billion computations per day. A pilot with Walmart in Mexico achieved 98% on-shelf product availability, and the company is now rolling the system out across 30 key customers globally. In temperature-sensitive categories like ice cream, Unilever’s weather-integrated AI forecasting delivered up to 30% sales increases by aligning supply with real-time weather conditions. The company has also trained 23,000 employees in AI by end of 2024, demonstrating that scaling AI is as much a people investment as a technology one.
Amazon’s Rufus and Just Walk Out
Amazon continues to set the pace for AI in retail across both digital and physical channels. Rufus, Amazon’s AI shopping assistant, reached 300 million users in 2025 and generated $12 billion in incremental sales, handling 274.3 million daily queries. In 2025, Rufus evolved from a simple Q&A tool into an autonomous shopping agent with memory, price tracking, and auto-purchasing capabilities, representing the early shape of what agentic commerce will look like at scale.
On the physical side, Amazon’s Just Walk Out technology has expanded to over 170 third-party locations across airports, stadiums, universities, and hospitals in the United States, United Kingdom, Australia, and Canada. The system uses computer vision, sensor fusion, and deep learning to enable shoppers to pick up items and leave without scanning or waiting in line.
PepsiCo’s End-to-End AI Strategy
PepsiCo is building one of the most integrated AI strategies in the CPG industry. The company shortened its innovation cycle from over six months to as little as six weeks using generative AI, enabling faster testing and iteration on new product concepts.
At CES 2026, PepsiCo announced an industry-first partnership with Siemens and NVIDIA to deploy AI-powered digital twins across its plant and supply chain operations, the first collaboration of its kind for a global CPG company. The company also partnered with Salesforce to deploy Agentforce, an AI agent platform, across its field sales operations, making PepsiCo one of the first CPG companies to bring agentic AI into its commercial workflow.
What’s Coming Next: Three Moves to Watch (12-36 Months)
1. Agentic Commerce Will Reshape How Consumers Buy
The concept of agentic AI, systems that do not just answer questions but independently execute multi-step tasks, is moving rapidly from research into retail operations. According to NVIDIA’s 2026 survey, 47% of retail and CPG respondents are already using or assessing agentic AI, with 20% having active AI agents in operations and another 21% expecting them within the next year.
The implications are significant. The agentic AI market in retail and e-commerce is expected to grow to $175.1 billion by 2030, and transaction volume through AI agents is projected to reach $3 to $5 trillion globally. Traffic from AI sources has surged 1,200% while traditional search traffic declined 10% year-over-year. For CPG brands, this means the AI agent becomes the new shelf. The brands whose product data, content, and pricing are optimized for AI consumption will win a disproportionate share of this emerging channel.
2. AI-Powered Digital Twins Will Become Standard Infrastructure
Digital twins, virtual replicas of physical operations that can be tested, optimized, and stress-tested in simulation before changes are made in the real world, are moving from experimental technology to core infrastructure. PepsiCo’s partnership with Siemens and NVIDIA to deploy AI-powered digital twins across its manufacturing and supply chain operations is a leading indicator of where the industry is headed. Unilever is building a digital twin of its global supply chain using satellite imagery and AI to simulate disruptions, assess risks, and inform logistics decisions before they become crises.
Within 12 to 36 months, expect digital twins to become standard practice for major CPG companies and large retailers, enabling them to simulate everything from new product launches to distribution network changes to extreme weather scenarios before committing real resources.
3. Generative AI Will Rewrite Product Development and Creative Production
Generative AI is moving from a content creation novelty to a core business capability across the CPG value chain. Coca-Cola’s Project Fizzion, built with Adobe, produces branded content up to 10 times faster and reduced time-to-market for creative assets by up to 90%. Nestle is working with IBM on generative AI to discover new packaging materials that reduce virgin plastic usage. Walmart’s Trend-to-Product system uses generative AI to go from trending social data to products on shelves in as little as six weeks.
The companies that learn to integrate generative AI across the full product lifecycle, from concept ideation and formulation to packaging design, marketing content, and shelf optimization, will have a structural speed advantage that compounds over time. The ones that treat it as a marketing gimmick will find themselves perpetually playing catch-up.
The Human Factor: Why Creativity Still Wins
With all of this technological momentum, it is tempting to conclude that the future of CPG and retail belongs to whoever has the best algorithm, but it does not. It belongs to whoever combines the best tools with the most creative, adaptable human thinking.
In my work with leaders across industries, I’ve seen a consistent pattern: the organizations that thrive in periods of disruption are not necessarily the ones with the biggest technology budgets. They’re the ones that cultivate what I call a Find A Way™ mindset, an organization-wide commitment to creative problem-solving that prioritizes agility over brute force and improvisation over perfect planning.
Consider the cautionary tale playing out across the industry right now. 66% of CPG companies have implemented or are actively scaling generative AI. Yet according to Bain, only 6% have an actual plan to leverage it for business value. The technology is outrunning the strategy. The companies that will win are not the ones deploying the most AI tools. They are the ones whose leaders can connect AI capabilities to genuine consumer insight, brand intuition, and competitive vision.
Start small and start now. Maybe it is your brand team running a 90-day pilot on AI-accelerated concept testing for one product line. Maybe it is your supply chain group deploying demand-sensing AI in one region before expanding globally. Maybe it is a cross-functional workshop where marketers, supply chain leaders, and data scientists sit together to identify the three AI use cases most likely to drive measurable value in the next 12 months. The breakthroughs accumulate, but only if you start accumulating them.
A 90-Day AI Action Plan for CPG & Retail Leaders
1. Pick One High-Friction Problem and Solve It Well
Do not attempt to “do AI” across your entire operation simultaneously. The organizations converting AI investment into operational results are the ones going deep on specific use cases rather than spreading themselves thin. NVIDIA’s survey found that 95% of retail and CPG companies say AI is helping decrease annual costs, with 37% reporting cost reductions greater than 10%, but those results come from focused deployment, not from scattering resources across dozens of experiments. Whether it is demand forecasting for one product category, AI-assisted planogram optimization for one store format, or generative AI for one stage of product development, choose one workflow, define your success metrics before you start, prove value, then scale.
2. Audit Your Data Foundation Before Buying More Tools
AI is only as good as the data it runs on. In CPG and retail, this is particularly acute: product data is often inconsistent across channels, supply chain data lives in legacy systems that do not talk to each other, and consumer data is frequently siloed between marketing, sales, and e-commerce teams. Walmart’s investment in using generative AI to clean and improve over 850 million product data points was not glamorous, but it created the foundation that made every subsequent AI application more effective. Before purchasing another AI platform, invest in getting your data foundation right. The companies that build clean, integrated, machine-readable data infrastructure now will deploy new AI applications faster later. The ones that wait will spend their time in remediation.
3. Close the Skills Gap Before It Closes Your Options
The talent challenge in CPG and retail AI is real and growing. 54% of retail and CPG respondents say their workforce lacks the skills to deploy generative AI effectively, and nearly 50% believe they need significant retraining, but fewer than 20% know what the new skills actually are. Meanwhile, 96% of CPG companies plan to hire AI-related roles in 2025. Unilever trained 23,000 employees in AI by the end of 2024, building fluency across the organization rather than concentrating expertise in a single team. Your team needs training not just on which systems to use, but on when to trust AI outputs, when to apply human judgment, and how to translate AI-generated insights into decisions that drive real business results. Build a change management framework alongside your technology rollout. It will pay dividends long after the current generation of tools is obsolete.
Metrics to Watch
As you execute, track operational efficiency improvements in the specific workflow you have targeted, and track AI adoption confidence among your team. If your people are using the tools but not trusting them, you have a governance and training problem. If they are trusting them without questioning outputs, you have a different but equally serious problem. The goal is informed, confident, human-led AI integration.
The Bottom Line
The CPG and retail industries are not being replaced by AI. They are being reorganized around it. The leaders, firms, and professionals who strategically and creatively engage with this shift will define the next era of the industry. CPG and retail sit at a unique inflection point: it is the sector where consumer expectations are highest, where margins are thinnest, and where the gap between AI leaders and laggards is widening fastest. That is not a contradiction to be resolved. It is a reality to be led through.
The best time to start was yesterday. The second-best time is today. Find a way.
Frequently Asked Questions
How is AI currently being used in CPG and retail?
AI is being deployed across the full CPG and retail value chain: demand forecasting and supply chain optimization, personalized marketing and dynamic pricing, AI-powered product development, computer vision for store operations and loss prevention, and autonomous checkout systems. According to NVIDIA’s 2026 survey, 91% of retail and CPG companies are actively using or assessing AI, and 89% report that AI is helping increase annual revenue.
How is AI changing product development in CPG?
AI is compressing innovation timelines dramatically. PepsiCo shortened its innovation cycle from over six months to six weeks using generative AI, and Nestle used generative algorithms to produce over 1,300 AI-backed product ideas. According to Deloitte, only 6% of manufacturers currently use AI for product development, but nearly 25% expect adoption within two years, making this one of the highest-growth areas for AI application in the industry.
What is agentic commerce, and why does it matter for CPG brands?
Agentic commerce refers to AI systems that independently execute multi-step purchasing tasks on behalf of consumers, from product research and comparison to actual transactions. According to NVIDIA, 47% of retail and CPG companies are already using or assessing agentic AI. The agentic AI market in retail is projected to reach $175.1 billion by 2030. For CPG brands, this means the AI agent becomes the new shelf, and brands whose product data and content are optimized for AI consumption will capture a disproportionate share of this emerging channel.
How is AI improving retail supply chains?
AI-powered demand sensing is delivering transformative results. Unilever reduced forecast error by 30% and saved approximately $300 million in annual holding costs. Walmart’s Self-Healing Inventory System saved over $55 million in waste during its 2025 rollout. NVIDIA reports that 51% of retail and CPG companies are deploying AI for supply chain efficiency, and 95% say AI is helping decrease annual costs.
Where should CPG and retail leaders start with AI?
Pick one high-friction, measurable workflow and deploy an AI solution with clear success metrics. Simultaneously, audit your data infrastructure to ensure you have clean, integrated product and supply chain data across systems. Then invest in training your team not just on tools, but on the judgment required to use AI responsibly and effectively. The organizations seeing the strongest results are the ones combining focused use cases, strong data foundations, and deliberate change management.
Learn more about Josh Linkner’s keynote speaking in CPG & Retail.
Josh Linkner speaks to consumer packaged goods and retail organizations around the world about innovation, navigating disruption, and building cultures that thrive in an era of rapid change. To explore how Josh can energize your next event, schedule a call today.