AI Trends in 2026: What Businesses Need to Know

The sudden rise of artificial intelligence (AI) has led to what some analysts call an “AI arms race,” as major software companies compete to capitalize on AI trends for a range of use cases. 

In a 2025 Shopify survey* 75% of store owners reported using AI tools for tasks like content generation, data analysis, and store operations. Respondents earning more than $100,000 annually are significantly more likely to recognize AI’s ability to scale operations with a small team and reduce operating costs.

Yet, with AI advancing so rapidly since the launch of ChatGPT in 2022, it can be difficult to keep up with trends that could impact your online store. Ahead, you’ll learn about eight AI predictions that will define the industry in 2026.

8 AI trends shaping ecommerce in 2026

1. Generative AI for enhanced creativity and efficiency
2. AI-powered personalization across the customer journey
3. The continued rise of conversational AI and AI agents
4. Predictive AI for smarter decision-making and forecasting
5. Digital twins for optimizing operations and customer experience
6. AI in cybersecurity: navigating evolving threats
7. Multimodal AI applications
8. AI governance and evolving regulations

Here are eight of the top ecommerce AI trends in 2026:

1. Generative AI for enhanced creativity and efficiency

Generative AI models use machine learning to spot patterns in data. It uses those patterns to create new AI-generated content, such as text, image, video, audio, or code.

McKinsey reports that 88% of organizations regularly use AI in at least one business function, up from 78% the year prior. And in a 2025 Shopify survey, store owners reported content generation as the most common AI use case, at 69%.*

Generative AI tools can create branded copy for your business in seconds with simple prompting and real-time data from your own store.

As of 2025, Google can detect AI-generated content—some 86.5% of top-ranking pages use AI—but it’s not penalized as long as it meets Google’s quality standards. As a result, GenAI is becoming a creative partner for brands, not just another shiny add-on. 

Shopify Sidekick, for example, can:

• Write product descriptions and marketing copy
• Automate answers to customer support inquiries
• Edit images and remove backgrounds
• Create product videos 
• Design and update your website

“The cost of any software effort is essentially trending toward zero,” says Alex Pilon, a senior developer at Shopify. “If you’re a Shopify merchant, you can redesign your site for Valentine’s Day today and revert tomorrow for a few dollars in tokens instead of a four-figure agency fee.”

2. AI-powered personalization across the customer journey

AI-powered personalization uses data you’ve collected on customers to adjust what they see. These tools let you meet customer’s expectations: Among early-adopter consumers in a 2025 study, 42% were comfortable using personalized recommendations.

Practical use cases of AI for personalization marketing include:

• Personalized product and content recommendations. Analyze browsing history, past purchases, or even items in a customer’s cart to surface relevant or complementary products. Some 73% of shoppers say they’re more likely to buy when these personalized recommendations are on offer.
• AI-powered shopping assistants. Conversational tools can operate as personalized shopping guides, answering product questions and helping customers navigate large or complex catalogs. Salsify’s 2026 Consumer Research report found more shoppers use these AI search tools than product review sites or online forums.
• Smart search and discovery. Decked uses Shopify Search & Discovery to handle complex on-site searches more effectively. They reported a 4% increase in revenue as a result.
• Enhanced behavioral and demographic targeting. AI helps personalize email campaigns, marketing messages, and delivery channels based on segmented or individual behavior. Retailers who offer these personalized product recommendations via email report 37% higher conversion rates and 30% higher click-through rates.

Personalization quality depends on customer signals, product data, and implementation. Otherwise you risk sending irrelevant messages 80% of customers tune out.

Shopify automatically gathers customer data from all your channels—what they’ve clicked, what they’ve bought, what they’ve left in their cart—and brings it into one unified customer profile. 

All that information lives in one place as a single source of truth. Use it to personalize emails, texts, or on-site recommendations. You can market to each customer as if you remember them personally without manually managing that data. 

Tip:Clienteling software houses customer data like a CRM but goes further and provides retail associates with opportunities for personalization and building rapport.

Adam Wolfe, founder and CEO at Boost Auto, combines product knowledge with customer personalization.

“Our deep understanding of car data means we can suggest the perfect fit for each customer, driving both satisfaction and repeat business," he says. 

3. The continued rise of conversational AI and AI agents

Agentic AI systems use machine learning to operate without human intervention. They can gather data and complete everyday tasks on your behalf. 

Alex explains that there is a difference between a purpose-built agent and generic prompting. “An agent is a purpose-specific configuration of AI. You give it tools (APIs, databases), resources (your product docs, brand voice), and a system prompt, and it becomes a super-intelligence at that one thing—for example, a marketing expert that’s connected to your Shopify store.”

Capgemini’s report shows AI agents can be a competitive advantage. As of April 2025, it found most organizations had not scaled AI agents. Some 31% were considering deployment in 6 to 12 months, 30% had started exploring, and 23% were piloting, but just 2% had implemented at scale.

That report aligns with evolving consumer behavior: Nosto’s 2026 survey found 72% of consumers expect AI shopping assistants to help them shop online. Price drop alerts, personalization recommendations, and gift inspiration were the top use cases.

Practical use cases of ecommerce AI agents include:

• Triaging support tickets
• Bulk updating product catalog data
• Using Sidekick Pulse to receive personalized recommendations with a list of specific tasks to improve your website

Most executives predicted AI agents will be actively performing at least one customer service process per day within the next 12 months. This is powered by conversational AI agents capable of handling complex customer inquiries, managing initial support tickets, and even assisting with internal workflows. 

For Shopify store owners, that can mean 24/7 AI customer service for online stores, faster resolution of common issues, and reduced service costs with Shopify Inbox.

4. Predictive AI for smarter decision-making and forecasting

Predictive AI analyzes data to forecast trends and outcomes. These AI-powered tools can predict sales patterns, optimize inventory levels, and identify customers at risk of churn. 

NVIDIA’s 2025 State of AI in Retail and CPG found that 44% of retailers use AI for predictive analytics, and 41% use it for customer analysis and segmentation. Both of these features help with:

• Demand forecasting. Have AI find patterns in past data that impacted demand, so you can spot when those same patterns emerge again. “We know exactly what we need based on real sales data, so we’re not tying up cash in excess inventory or missing sales due to stockouts,” says Tyler Angelos, CEO at Angelus Direct.
• Dynamic pricing. AI tools like Shopify Smart Pricing suggest markdowns and manage A/B testing of suggested price changes before a full rollout. 
• Operational planning. Shopify merchant Doe Beauty leverages Shopify’s AI-driven tools to manage inventory across its global supply chain efficiently. It saves $30,000 and about four hours of human labor each week thanks to Shopify Flow automations. 

5. Digital twins for optimizing business operations and customer experience

A digital twin is a virtual replica of a real-world entity or process.

Ecommerce brands can use product digital twins to:

• Prototype. Make a prototypeinstead of designing a real-world prototype for every product design tweak and conducting extensive physical testing. For example, you can quickly alter a digital representation of the product and run simulations to evaluate performance.
• Model supply chains. On top of predicting outcomes based on predetermined inputs, large AI models can make this process prescriptive. AI-powered digital twinning systems can recommend product or supply chain improvements.
• Optimize warehouse layouts. Have the digital twin simulate more orders to find inefficiencies in the order fulfillment process. 
• Simulate customer flow in physical retail stores. Replicate your store’s layout and layer in customer data. It can pinpoint where congestion builds, sell-through rates by product location, and where customers exit.
• Offer virtual try-ons. These display a 3D model of the product over a customer’s image or camera. Numerator’s 2026 research found 20% of customers are interested in this type of AI technology. Snapchat found two-thirds of shoppers think AR virtual try-on makes online purchase decisions easier.

6. AI in cybersecurity: navigating evolving threats

AI is susceptible to exploitation by malicious actors, enabling increasingly sophisticated cyberattacks, including advanced phishing schemes targeting customer data. 

AI also poses security concerns if you’re uploading sensitive data to the platform. Platforms like OpenAI use this for AI training purposes (data sharing on the free plan is on by default), which means any confidential information you upload can feed into the AI model.

IBM reports that 97% of organizations experiencing an AI-related security incident lacked proper AI access controls, while 63% had no AI governance policies in place. It also found that organizations with high levels of shadow AI—where employees use unapproved AI tools—saw $670,000 added to their average breach cost.

On the flip side, Morgan Stanley identified several key applications of AI in cybersecurity:

• Identifying attacks. AI tools can detect cyberattacks, determine risk levels, and respond accordingly. Because AI can process large volumes of data quickly, it may catch threats human users miss.
• Providing phishing defense. AI systems can flag messages that are likely part of phishing campaigns and alert users before any damage is done—helping protect store accounts from compromise.
• Exposing vulnerabilities. Security teams can simulate attacks using AI tools to identify weaknesses and patch them before a real threat strikes.

Shopify offers enterprise-grade security right inside the ecommerce platform. Features include:

• Machine-learning fraud analysis that flags risky orders in real time
• Shopify Protect, whose training data included 10 billion transactions, to flag risky orders and reimburse merchants for eligible Shop Pay chargebacks 
• No-code Shopify Flow to automatically cancel or hold suspicious transactions before shipping 

Maine Lobster Now uses Shop Pay’s fraud detection technology to reduce chargebacks due to fraud by 93% without hiring extra team members as they grow.

“I used to have an agent, and almost her whole job was fraud,” says founder and CEO Julian Klenda. “Now she doesn’t have to deal with that anymore. She’s able to focus her time on enhancing customer experience, especially with our bigger customers, making sure everything is the way they want.”

7. Multimodal AI applications

Multimodal AI simultaneously processes multiple types of data, like text, images, audio. and video. 

For example, visual search lets customers find products with computer vision by uploading photos. AI can also analyze customer feedback across reviews, social media posts, and videos to uncover actionable insights.

Tinker is an example of multimodal AI. It uses models from OpenAI, Google, and Anthropic to combine more than 100 AI tools into a single app. 

“We write these very long prompts that are optimized for quality, and boil it all down to a few simple inputs for you to fill,” says Rousseau Kazi, director of product at Shopify. 

Ecommerce brands can use multimodal AI within Tinker to:

• Create social media images
• Redesign product photos
• Design 360-product views

“Through play, you find the creative limits of technology without any cost or consequence,” says Rousseau. “Once you understand these limits, you feel confident to reach for these tools for when it matters. Before they were our tools, they were our toys.”

8. AI governance and evolving regulations

NCSL reports that all 50 states introduced artificial intelligence legislation in 2025, and 38 states adopted or enacted around 100 measures. The FTC has also taken action on brands that allegedly mislead consumers into thinking their AI tools are more accurate than they are.

Compliance with privacy regulations like GDPR and CCPA is also a legal obligation and foundation for customer trust. Deloitte’s 2025 Connected Consumer survey found 24% of Gen Z have faced data privacy issues when using AI—the second-highest challenge beaten only by inaccurate output. 

As regulations and customer privacy concerns evolve, protect your brand from the dangers of AI. That might mean:

• Using only approved AI tools
• Limiting access controls
• Transparency around what customer data is shared with AI
• Reviewing outputs for bias or inaccuracy
• Verifying vendor claims before using new software 
• Having an internal owner accountable for your AI projects 
• Asking for legal help to make sure your AI usage is compliant with relevant laws

“AI has human-like reasoning and decision-making skills, but it’s important to remember that at its core it is still an algorithm,” says Alex Pilon. “It can produce reasoning and decisions that look perfectly reasonable on the surface yet are actually wrong in a specific context.”

How AI search is changing ecommerce discovery

AI search engines help customers research, compare, and buy products within one conversation. 

They’re already proving popular: Nosto’s 2026 study found 72% of customers expect AI shopping agents to help them shop online, and almost half would be open to an AI assistant from their favorite retailer if it was introduced this year.

“I think that’s going to be a paradigm shift over the next couple of years,” says Paul Tran, founder of Manscaped, in a Shopify Masters interview. “Shoppers’ exploration of new products will be disrupted by AI.” 

Google already processes over five trillion searches annually through Google Search. It reports that with AI Overviews, people are searching more, including a more than 10% increase in Google usage for query types that show AI Overviews.

Ecommerce brands can prepare product data for AI-driven discovery through:

• Agentic storefronts. Shopify structures your product data through Universal Commerce Protocol (UCP) to make it easy for AI agents to crawl, so you show up accurately when AI discusses your brand in conversations with shoppers. Sales run through the world’s highest-converting checkout, and orders processed through AI show up in Shopify Analytics for accurate reporting.
• Smart on-site search. On-site search users have 2.5 times higher conversion rates than those who don’t use the feature. Shopify’s Search & Discovery app lets you customize these search results with synonym grouping, advanced filters, and semantic understanding.
• Visual search. Let customers search with an image instead of a text-based query. Google Lens leads the way here: Google reports it’s the fastest-growing type of search, with more than 25 billion monthly queries. Apply the same idea to your website with visual search apps.
• Shopping apps.Shop app supports conversational search. Users can search “I’m looking for a pair of pink running sneakers for a marathon” and compare relevant products from several brands at once. 

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**Based on a 2025 survey of 500 Shopify merchants conducted in English across Australia, Canada, the United Kingdom, Ireland, New Zealand, and the United States. Respondents were established merchants with two or more years on the platform. Results reflect the experiences of this specific sample and may not be representative of all merchants.