Generative AI in e-commerce: Risks, rewards, and the road ahead

What is generative AI in e-commerce?

Generative AI creates new content, including text, images, video, audio, code, and even conversations, by learning from existing data. In e-commerce, that means it can draft product descriptions, generate lifestyle imagery, localize copy, or power real-time chat with shoppers.

Its appeal for e-commerce is speed and scale. What once took hours, like writing SEO text or translating catalogs, now takes seconds. 

But there’s a catch. The outputs are only as good as the data you feed them. If your product information isn’t accurate, governed, and complete, generative AI can amplify errors — producing content that’s off-brand, non-compliant, or misleading.

You need structured, trusted product data if you want AI outputs that build customer trust instead of breaking it.

Where are e-commerce leaders investing in generative AI right now?

McKinsey reports that nearly 20% of leaders already list it as their number-one priority, and almost 30% plan to dedicate more than 10% of their budget to it this year. 

It’s not just brands in the B2C space that are investing in AI. B2B leaders are also investing even more heavily, recognizing the payoff of AI-powered personalization in complex buying journeys.

What does that mean for you? Results depend on where you start. Viktor recommends focusing on quick wins: “Automating product descriptions or enriching product data are smart first steps. They save hours of manual work and deliver productivity gains from day one.”

Some leaders are also piloting semantic search to address “zero results” queries or applying AI to operations such as forecasting and fraud detection. These targeted projects demonstrate a quick ROI and build momentum for broader adoption. If you choose the right entry point, you’ll prove value fast and secure buy-in for scaling.

How are e-commerce businesses using generative AI?

Gartner finds that leaders invest primarily to improve customer experience, grow revenue, and reduce costs, the same pressures you face daily. 

Here’s how you can apply it today:

• Product content generation and enrichment: drafting product descriptions, translations, and SEO text at scale, powered by structured data in your PIM to keep content consistent across every channel.

• Conversational shopping and service: AI assistants help customers through catalogs, answer complex questions, and recommend products. Retailers using AI chat show higher conversions and lower service costs.

• Personalized promotions: generative AI leverages unstructured signals, such as reviews or chat histories, to tailor offers in real-time.

• Visual content creation: produce lifestyle images, product mockups, or videos to speed up merchandising.

Gartner stresses that while the use cases are compelling, value only emerges when tied to clear KPIs and supported by effective governance. 

Viktor also cautions that the story isn’t only about the customer-facing side: “A lot of focus goes to chatbots and recommendations, but back-office wins often drive the biggest efficiency gains. Automating supplier feeds, extracting specs from PDFs, or generating post-purchase care guides can cut costs and strengthen margins in ways shoppers never see.”

What are the risks of generative AI in e-commerce?

Despite its enormous potential, generative AI in e-commerce also has vulnerabilities. At Inriver, we see four risks that matter most when your product data isn’t tightly governed:

1. Scaling inaccuracies: Messy product data turns into thousands of errors at scale, especially when businesses apply AI for e-commerce without structured controls. Incomplete specs or attributes spread across every channel instantly.
   
2. Compliance blind spots: AI models can generate claims that breach industry regulations, putting brands at risk of fines or recalls.
   
3. Customer trust erosion: A mistranslation or misleading image can prompt a shopper to abandon a purchase and lose trust in a brand.
   
4. Fragmented brand voice: Without a single source of truth, content may look slick but stray from tone, terminology, or sustainability messaging.

Analysts echo these concerns. They warn of: 

• Accuracy errors
• Bias in training data
• Intellectual property misuse
• Fraud and cyber misuse
• Sustainability costs of running large models

McKinsey also highlights the problem of “hallucinations” — outputs that sound credible but are wrong, biased, or legally risky — which makes human oversight essential. 

Viktor emphasized the importance of governance: “Generative AI might produce subtle inaccuracies, so you need editors or product experts reviewing outputs rather than blindly publishing them. Always keep a human-in-the-loop.”

To turn these risks into safeguards, you connect generative AI with governed product information through PIM. Doing so ensures that outputs remain accurate, compliant, and aligned with your brand. That’s how you build confidence within your team in every AI-driven interaction.

Balancing risk and reward: The road ahead for generative AI in e-commerce

In Inriver’s latest research, The State of AI in PIM, e-commerce leaders report the most substantial AI impact in customer-facing experiences and enrichment. This shows a shift from isolated pilots toward cross-functional deployment.

However, your success will depend on how well you balance opportunity with oversight. You need AI systems that manage risk intelligently, build customer trust, and deliver results at scale.

At this stage, you should move past the AI “if” and lean into the “how.” When you embed AI into workflows with accountability, adaptability, and a clear definition of success, you create lasting advantage. At the same time, you must study the implications carefully, monitor performance continuously, and avoid rushing into deployment without safeguards.

Ultimately, your challenge is to act with both optimism and discipline. Book a personalized demo with Inriver to see how you can balance the risks and rewards of AI in e-commerce.