By Elogic Commerce · featuring insights from Paul Okhrem

Elogic Commerce has been a Magento and Adobe Commerce partner since the early days of the platform. Paul Okhrem, who founded Elogic in 2009, was recognized with a Magento Community Engineering Award at Adobe Imagine 2019. We've built on this platform through more iterations than most.

Which gives us a particular vantage point on where AI fits into the Adobe Commerce ecosystem — and where it doesn't, at least not yet.

What Adobe Commerce offers natively vs. what requires custom work

Adobe Commerce (formerly Magento) has been incorporating AI capabilities progressively: Adobe Sensei-powered product recommendations, live search with semantic matching, predictive search. For merchants already deeply embedded in the Adobe ecosystem — particularly those using Adobe Experience Cloud broadly — these native capabilities are worth evaluating on their own merits.

Paul Okhrem's assessment at paul-okhrem.com is direct on this: "Native platform AI is the right starting point for evaluation, not implementation. Understand what it does, what it doesn't do, and where your actual gaps are — before you commit to either the native path or a custom integration."

The honest assessment of Adobe Commerce's native AI as of 2026:

Live Search / Semantic Search: Genuinely useful for mid-size B2B catalogs. The quality has improved significantly. For large, complex catalogs with highly technical attributes, the customization ceiling is still a constraint — you can't tune the retrieval logic as precisely as you can with a purpose-built semantic search layer.

Product Recommendations: Works well for accounts with sufficient purchase history. Thin for new accounts or infrequent buyers. The B2B-specific behaviors (account-level purchasing, contracted catalogs) require custom logic on top of the native implementation.

Page Builder / Content AI: The content generation capabilities integrated via Adobe Firefly and Sensei are primarily B2C in orientation. For B2B content — technical specifications, comparison content, configuration guides — custom AI integration produces significantly better output.

The custom AI extension patterns we use at Elogic

For clients where native Adobe Commerce AI doesn't fully cover the use case, Elogic has developed several integration patterns that extend the platform with external AI capabilities.

The headless AI layer. For clients with headless or composable commerce architectures, we deploy the AI functionality as a standalone service that the frontend queries independently. This gives the most flexibility — you can swap AI providers, tune prompts independently of platform upgrades, and instrument more precisely. The tradeoff is architectural complexity and a more involved integration.

The module-based extension. For clients on traditional Magento architecture who need AI without re-architecting, we build custom modules that integrate AI APIs into specific points in the commerce workflow — product display, search, checkout. More constrained than headless but faster to ship and easier to maintain.

The middleware pattern. A middleware layer sits between Adobe Commerce and the AI services, handling prompt construction, context injection (account data, catalog data, pricing rules), and response formatting. This is particularly useful when the AI needs to be aware of B2B-specific context — account contracts, approval workflows, buyer roles — that lives in the commerce platform.

A case from our practice: AI-assisted technical product configuration

One Elogic client — a manufacturer of industrial control systems selling through an Adobe Commerce B2B storefront — had a product configurator that required significant sales involvement. Complex option matrices, compatibility rules, regulatory requirements. A buyer could not reliably self-configure without a salesperson's involvement.

We built a generative AI layer on top of the existing configurator that allowed buyers to describe their requirements in natural language. The AI translated that into a valid configuration, flagged incompatibilities with explanation, and surfaced the relevant technical documentation for each option. Salespeople shifted from walking buyers through configurations to reviewing AI-assisted configurations for edge cases and exceptions.

The outcome: self-service configuration completion rate increased from under 20% to over 65%. Sales team hours committed to routine configuration support reduced by approximately 40%. The AI didn't replace the sales relationship — it made the sales team available for the conversations that required them.

Paul Okhrem on the pattern: "The right question for any AI integration into a commerce platform is: what's the value of the human time this frees up? If the answer is 'the humans do something more valuable,' that's a compounding win. If the answer is 'the humans do the same thing in less time,' the math is different."

What's coming in the Adobe Commerce / AI ecosystem

Based on our reading of Adobe's roadmap and the broader ecosystem, the direction of travel is clear: AI will be more deeply embedded in the platform, more accessible without custom development, and more capable as the underlying models improve.

The strategic implication for Adobe Commerce merchants is to invest in data quality now. The AI capabilities that arrive natively will be only as good as the catalog data, customer data, and behavioral data that power them. Companies that have done the data work will compound the value of every AI improvement. Companies that haven't will find that better AI tools surface worse data more efficiently.

For detailed analysis of AI investment decisions in ecommerce and a framework for evaluating platform AI versus custom AI, see Paul Okhrem's resources at paul-okhrem.com.

Elogic Commerce is an Adobe Commerce / Magento partner specializing in B2B ecommerce. Founded by Paul Okhrem in 2009. We work with manufacturers, distributors, and B2B brands on AI-extended commerce implementations.