7 AI trends in 2026: Preparing your brand for agentic commerce

Why 2026 is a breakout year for AI

For years, digital commerce has undergone numerous waves of evolution, from the rise of mobile to the emergence of social commerce. In 2026, the next big wave — arguably the biggest yet — is hitting in full force as agentic AI transforms how consumers discover, compare and buy products, and how they interact with brands. 

Digital commerce, as we know it today, is about to undergo significant changes. Instead of browsing pages, comparing prices, reading reviews and managing checkouts, shoppers will increasingly leverage GenAI channels to find and buy products, and later, delegate these steps to autonomous agents that interpret goals and take action.

This shift changes not only how consumers shop, but also how retailers structure their product data and customer journeys, design interfaces and rebuild loyalty as consumers engage less directly with their brand. 

But why now? There are three forces helping AI — and agentic commerce specifically — accelerate in 2026: 

1. Consumer readiness driven by habitual trust in recommendation algorithms and personal assistants.
2. Maturing LLM capabilities that understand preferences, contexts and constraints.
3. New industry standards that allow retailers, platforms and agents to interoperate.

And beneath it all, a new set of protocols, interoperability layers and governance frameworks emerges — the invisible plumbing required for agents to safely transact.

As a result, agentic commerce is expected to capture a significant portion of eCommerce, with Morgan Stanley predicting that nearly half of online shoppers will use AI shopping agents by 2030, accounting for approximately 25% of their spending. 

This article explores the seven most significant trends shaping the AI ecosystem in 2026.

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Trend 1 – The rise of GenAI platforms as a commerce channel

Perhaps the most visible trend so far, AI platforms like ChatGPT, Gemini and Perplexity are moving from being “smart assistants” to full-fledged retail channels. This trend is primarily driven by a rapid shift in consumer behavior. Shoppers quickly found out that they could rely on ChatGPT & Co. for:

• Product discovery (“Which headphones should I buy for gym workouts?”)
• Feature comparison (“Compare the battery life and comfort of these two models.”)
• Price optimization (“Find me the best deal today.”)
• Task execution (“Order more coffee pods when my supply drops below two boxes.”)

Indeed, a recent study revealed that 73% of consumers are already using AI in their shopping journey, embracing AI assistants for product ideas (45%), summarizing reviews (37%) and comparing prices (32%). While only 13% say they’ve completed a purchase after being referred by an AI assistant, 70% are at least somewhat comfortable with an AI agent making purchases on their behalf. 

For brands, this means: 

• Zero-click commerce is set to disrupt retail in 2026 as shoppers may never need to click, search or visit a website to make a purchase. Retailers that address zero-click commerce can gain loyalty and revenue, while those that resist risk losing visibility to AI platforms that control which products are seen.
• AEO (Answer Engine Optimization) becomes essential. Structured data, enriched metadata and clean catalogs determine whether an agent can understand and recommend a SKU.

Trend 2 – Discovery and conversion dominate now; post-purchase and loyalty follow

Right now, most agentic capabilities cluster at the start of the sales funnel, including browsing assistance, discovery, search, product matching and personalized suggestion lists. 

While 2026 ushers in a shift toward deeper-funnel agentic capabilities from cart creation to secure payments, the fact remains that the greatest friction in digital commerce exists in the messy middle: Checkout, shipping, taxes and payment authorization. Plus, many brands still struggle with how promotions, rewards, tier benefits and personalized offers flow into AI channels.

Both consumers and merchants want a future where the entire purchasing journey can be initiated and finalized in a GenAI channel — in a way, similar to marketplaces. However, the entire ecosystem is immature, so it may take some time for all pieces of the puzzle to be in place. 

However, AI platforms are moving in this direction fast: 

• Perplexity launched a free shopping experience with conversational product discovery, personalized product cards and instant checkout powered by PayPal.
• OpenAI introduced shopping research in ChatGPT using GPT-5 mini with reinforcement learning for comparative product guides, real-time feedback loops and deep internet research across retail sites. 
• Google launched agentic checkout across Google Search (AI Mode) and Gemini, enabling autonomous AI agents to execute purchases directly on merchant websites with “Buy for me” button functionality now live in selected retailers in the US. 

Once the ecosystem unlocks agent-assisted conversion, this will change the economics of eCommerce entirely. 

Trend 3 – Purpose-built agents lead the early wave instead of complex A2A solutions

While agentic AI is expected to autonomously handle the entire customer journey in the future, 

AI agents won’t come as a monolithic fix. Instead, they’ll come as an aggregation of many “purpose-built” agents that solve specific problems across the customer journey. 

Agent-to-agent (A2A) commerce isn’t a far-fetched reality, but at this stage, brands and retailers are focusing on what’s in front of them: Consumers directing their attention to GenAI channels instead of their eCommerce sites. To address this shift, businesses are focusing on purpose-built solutions that focus on specific solutions, such as a bundle builder, a reorder automation tool or a shopping assistant. These agents integrate seamlessly into existing workflows by filling gaps without requiring the overhaul of entire systems.

McKinsey’s 2025 State of AI survey reinforces this trend: 

• While 88% of organizations now report using AI in at least one business function, most are still in the experimentation or pilot phase, with only about one-third scaling AI programs across the enterprise. 
• Specifically, 62% of respondents are experimenting with AI agents, and just 23% have begun scaling agentic AI in any function. 

This demonstrates that agentic commerce will likely arrive in stages: Brands and retailers are currently piloting specialized agents, rather than deploying a single “master agent” that requires complex orchestration to run entire journeys. 

Trend 4 – Brand‑owned conversational commerce becomes agent‑powered

Many brands have already built chat‑based interfaces on their websites, in mobile apps, via SMS or on messaging platforms like WhatsApp and Messenger, mostly for FAQs, customer support or basic product recommendations. Guided selling isn’t new, but these processes only filter the results with prebuilt logic that assists customers in finding products, but don’t do much (or anything) autonomously. 

Agentic integration into brand-owned conversational commerce has the potential to add depth and facilitate meaningful conversations that a traditional chatbot isn’t able to provide. More than handling queries, brand-owned AI agents can guide product discovery, curate offers to manage carts and checkout, navigate promotions and loyalty programs. 

While AI agents can revive conversational commerce run by brands, it doesn’t mean chatbots will disappear. It’s likely that a hybrid of traditional browse/search interfaces and a chat-based shopping assistant will co-exist — at least for the foreseeable future. 

Trend 5 – AI-driven productivity tools reshape how we work

There’s more to AI than consumer-facing journeys — it’s also transforming the back office. 

• According to a McKinsey survey, 3x more employees are using GenAI for third or more of their work than their leaders imagine. 
• In addition, more than 70% of all employees believe that within 2 years, GenAI will change 30% or more of their work.
• DX Research also revealed that AI helped developers increase productivity by 5-15%, saving 3 hours and 45 minutes per week. In mature rollouts, for instance, 40-50% of developers use AI tools daily. 

Many tools today are also powered by AI, including dynamic dashboards, conversational analytics, automated anomaly detection, opportunity spotting, context‑aware suggestions during workflows and intelligent automation of repetitive tasks. This means tasks like report generation, data analysis, documentation, bug‑fixing, lead qualification, forecasting and administrative workflows — all become faster, simpler and more scalable.

The next frontier is orchestrating decisions across AI agents: One negotiating contracts, another shaping pricing, a third allocating inventory, and yet another customizing assortments for local markets. In this new model, humans collaborate with AI to make higher-value, faster and more informed decisions, turning productivity gains into strategic advantage.

Trend 6 – AI growth in B2B

While AI-driven agentic solutions have captured the spotlight in consumer markets, B2B commerce is poised to become the next arena of explosive growth: 

• 84% of B2B buyers using AI tools are speeding up their research and decision-making processes.
• 86% of Gen Z professionals use AI daily at work, primarily for product research in B2B buying.
• 90% of B2B buying will be AI agent-intermediated by 2028, driving over $15 trillion of B2B spend through AI agent exchanges.

B2B workflows — spanning multi-step approvals, negotiations, quote generation, recurring orders, compliance checks and inventory management — are inherently complex and often rely on manual work, making processes error-prone and slow. 

For instance, Forrester stated that AI makes a valuable solution to streamline complex B2B payments and adjacent processes, such as invoicing, accounts payable/receivable, trade credit, and order approvals, especially as “these processes don’t involve consumer trust or multilayered authentication across networks.” For 2026, the market analyst firm predicts that: 

• ⅓ of B2B payment workflows will use AI agents.  
• 20% of B2B sellers will be forced to engage in agent-led quote negotiations. 
• 1 in 5 sellers will be compelled to respond to AI-powered buyer agents with dynamically delivered counteroffers via seller-controlled agents. 

While AI readiness requires B2B organizations to double down on digital maturity to integrate data, align processes and more, it’s expected that, just like the B2C sector, manufacturers, distributors and wholesalers will start small, focusing first on specific use cases and then scale.

Trend 7 – Security, privacy and trust become differentiators

As AI agents gain more autonomy in commerce, trust is rapidly becoming the ultimate competitive advantage. Shoppers are increasingly willing to let agents act on their behalf — but only if they can be confident their data is protected and decisions are reliable. Key risks include unauthorized purchases, misuse of personal information, opaque decision paths, hallucinations, fraudulent interactions and errors in cross-agent communications.

By 2026, leading brands will standardize on transparent consent flows, granular user permissions, agent action logs, secure payment authorizations, override mechanisms and policy-driven guardrails. These measures signal to customers that their privacy, security and control are respected.

Brands that embed trust at the core of their agentic systems will scale faster and capture greater loyalty, while those that cut corners risk losing credibility, visibility, and ultimately, market share.

Designing for AI and agentic commerce in 2026

As consumers increasingly delegate shopping to AI agents, brands, retailers and B2B organizations must rethink how they engage and deliver value. Key areas of focus include:

• Data and discoverability: Structure product information for AI and Answer Engine Optimization (AEO) to ensure agents can find and recommend your products.
• Visibility and insights: Adapt to reduced direct access to some consumer behaviors while optimizing for agent-driven discovery.
• Seamless integration: Ensure interoperability with agent-ready platforms and workflows to support automated ordering, reordering and omnichannel experiences.
• Brand-owned agentic experiences: Design experiences that reflect your brand, build trust and manage autonomous actions responsibly.
• Security and compliance: Implement robust privacy, authorization and oversight mechanisms for AI-driven transactions to maintain credibility and avoid risk.
• Optimization for utility: Focus on the practical drivers of agent adoption — price, availability, speed and frictionless checkout — rather than just marketing visibility.

Monolithic legacy platforms are fundamentally ill-equipped for this agentic commerce era because their black box architectures were designed for human browsing and interaction, preventing agents from efficiently automating workflows or completing the buying process. 

By contrast, commercetools’ composable architecture, as well as its dedicated AI Hub and Agent Gateway offering, allows brands to go beyond simple product discovery to fully automate complex workflows- with a level of speed and security that all-in-one vendors simply cannot match.

Businesses that embrace these shifts and optimize for both autonomous AI and AI-enhanced experiences will unlock — not just human shoppers — will unlock faster growth, stronger loyalty and a decisive edge in the next era of commerce.

White Paper

Navigating the Agentic Commerce Era On Your Terms

The practical guide to help you approach agentic commerce strategically — not out of FOMO.

Download Now