2026 Predictions: How AI Will Transform Retail and Consumer Goods, and What Leaders Need to Know

While AI is top of mind across every industry, and early adopters of it have seen significant growth across their organizations, it’s still the case that most companies haven’t yet scaled enterprise AI.

But 2026 may be the year where that changes.

In the coming year, we anticipate many retailers and consumer goods companies may move from experimenting with AI tools and applications to driving transformational innovations within their businesses by fully embracing enterprise AI. From autonomous supply chain decision-making to conversational and agentic commerce, there are a number of key areas in which retailers will evolve to ensure their internal operations and customer experiences can meet the demands of their rapidly changing industry.

However, while retailers and consumers are looking to the future, consumers are simultaneously seeking experiences rooted in traditional brick-and-mortar retail. What’s old is new again — a literal truth with the rapid rise of recommerce becoming mainstream — and though consumers are increasingly turning to LLMs over search engines to do their online shopping, they’re also craving in-store experiences to see, touch and feel what they’re buying before they buy it. Let’s dive into some of the most impactful shifts we see affecting retail and consumer goods in 2026.

Conversational and agentic commerce

Ecommerce as we know it is about to undergo radical change as agentic commerce is likely to play an increasingly central role in the online shopping experience. At this year’s National Retail Federation (NRF) conference, Google announced the launch of an agentic commerce standard they’re calling the Universal Commerce Protocol (UCP) to power a new checkout feature on eligible products in Gemini and AI Mode in Search. This comes only four months after they launched their Agent Payments Protocol. ChatGPT’s Instant Checkout, which is available to their 800 million active weekly users, is powered by their Agentic Commerce Protocol (ACP) they built with Stripe. These developments have laid the groundwork for retailers to embrace and implement agentic commerce in 2026.

There are two aspects to conversational and agentic commerce for retailers to consider this year: the conversational commerce piece, which makes it possible for consumers to speak to an LLM, and the agentic commerce piece, which involves non-human-in-the-loop transactions.

As it relates to the conversational commerce side, the way consumers are using the internet has fundamentally changed: The amount of time they’re spending with LLMs doing product search and discovery is much longer than the time they’re spending in traditional search engines. With search engines, customers are using fewer words in their queries and providing less of their personal information, but with LLMs, customers are much more forthcoming with information about themselves. 

In light of this shift to AI-centric product research and discovery, retailers should focus on semantic modeling and maximizing visibility of their products to ensure discoverability. That means making sure product catalogs are clean, accurate and fed with high-quality data for a rich AI-powered customer experience.

As for the evolution of agentic commerce, we’re anticipating a move toward human-in-the-loop shopping assistants closer to the end of 2026. While it’s not yet happening, we foresee it becoming a bread-and-butter part of the retail experience in which consumers will be passing on the act of finding and purchasing a product to an agent that will handle everything autonomously. It’s important for retailers to prepare for this by having a Model Context Protocol (MCP) server to tie different agents together (supply chain agents, inventory agents, customer 360 agents, etc.). Human-in-the-loop assistants are most likely to take hold in categories where products have to be purchased repeatedly, such as consumables or household items.

Autonomous supply chain decision-making

While many people talk about agentic commerce from a B2C perspective, the use of AI and agents in B2B for retailers will be in the form of autonomous supply chain decision-making. This will include tasks such as negotiating with suppliers and using AI to understand what you should be coming to the table with during those negotiations. It will also include tasks such as space planning in stores or in fulfillment and distribution centers, especially for retailers who have a very distributed network of these centers. Autonomous supply chain decision-making can help navigate issues like where to stock products based on seasonality and sales in particular regions. For example, a big-box retailer will be able to automate the decision to stock up on heavy winter clothing for longer periods of time in their East Coast stores over their West Coast locations based on sales data.

Predictive supply chain intelligence will evolve through AI-powered forecasting and inventory. While this is a discipline that’s always been driven by machine learning (ML), and retailers have been using ML for forecasting, AI will enable retailers to automate such tasks as writing SQL queries or creating and running data science models.

It will be crucial for retailers and consumer goods companies to prioritize focusing on both the speed and diversification of their supply chains in the coming year to improve their supply chain resiliency. The speed of access to insights and information will require harnessing data, and the data ecosystem that retailers develop will drive agentic decisions. In fact, agentic decision-making has a high likelihood of becoming a key differentiator for supply chains navigating rapid change.

The rise of recommerce and in-store experiences

While the industry is seeing this massive shift toward AI, agents and data-fueled operations, consumers are seeking both forward-looking and traditional retail experiences. Recommerce — another word for the secondhand market — has become mainstream due in large part to younger generations such as Gen Z, but also due to widespread concerns among consumers about increasing sustainability in their buying habits. More broadly, many consumers report increased interest in more conscious consumption and are rethinking which brands and products they feel are truly necessary in their lives. From buying local to prioritizing sustainable packaging and engaging in the circular economy, these trends are going to have an impact across all categories in retail. Consumer goods companies have a great opportunity to consider repositioning some of their existing products and embracing innovation to drive growth. Retailers looking for new revenue streams should look at their returns and reverse logistics strategy and consider whether they’re thinking about recommerce as a way to claw back profit margins.

Additionally, consumers are demanding in-store experiences, particularly touch and physical/sensory experiences, driving a rise in retail tourism. In vertically integrated retail, such as fashion and apparel, this has always been the case, but we’re seeing this happening in other categories such as toys, where consumers want to see the product and packaging and feel it in their hands. Retailers — and not just luxury ones — are responding by launching different in-store experiences, such as Costco bringing celebrities into stores to promote product launches. As consumers continue to seek physical experiences, retailers should pay attention to what their customers are asking for and conceptualize ways in which they can elevate in-store experiences to can’t-miss opportunities that drive genuine consumer value.

The coming year is going to bring major opportunities for growth and innovation for retailers and consumer goods companies, but it will be critical for them to first embrace enterprise AI. To drive enterprise AI at scale, these companies will need to have the following in place:

• Data readiness: Companies must make sure teams have access to the right data, both internally and externally, across all disparate systems. Then, they’ll need to clean and harmonize the data to ready it for data consumers. The data that companies use to train their models will be the biggest factor in AI implementation success, not the models themselves.
  

• Secure and easy-to-use data platform: The platform should be able to work with all data types (structured, unstructured, semi-structured) and run LLMs in a secure environment so that company data isn’t going out to public domains.
  

• Business-led use case governance: Don’t fall into the trap of deploying technology for technology’s sake, or companies risk deploying for use cases that aren’t going to make a meaningful difference. Apply AI implementations against true business priorities and have an objective framework in place that will make it possible to identify those critical business use cases.
  

• Collaborative and interoperable ecosystem: Companies should have a proactive strategy to take advantage of best-in-class solutions that are already being developed in the marketplace. Tap into partners that can provide you with the right tools for your needs. Have a relentless focus on value creation; if companies aren’t creating value with AI, they’ll find that their organizations will stop believing in the power of it, undermining all that they can achieve with it.

These are just a few of the insights we have on the changes we see coming on the horizon. For more information on industry predictions and how retailers and consumer goods companies can prepare, watch our Retail + AI Data Predictions 2026 webinar, and download Snowflake AI + Data Predictions 2026 now.