How Businesses Are Successfully Integrating AI With Legacy Systems in 2026

The conversation around artificial intelligence has shifted. A year ago, most businesses were debating whether to adopt AI. Today, the question is how to make AI work with the systems they already have.

This is not a small challenge. Most organizations run on technology stacks built over decades. ERPs installed in the 2000s. CRMs customized over years of use. Databases holding critical business logic that nobody fully documents anymore. The idea of ripping out these systems to make room for AI is not realistic for most companies.

The good news is that it is also not necessary.

The Real Barrier to AI Adoption

A 2024 survey from McKinsey found that 72% of organizations have adopted AI in at least one business function, up from 55% the previous year. But adoption does not mean integration. Many of these implementations exist as isolated tools rather than connected systems.

The gap between “using AI” and “integrating AI” matters because isolated AI tools deliver isolated results. A chatbot that cannot access order history provides generic responses. A demand forecasting model that cannot connect to inventory systems generates reports that someone has to manually act on. The value of AI compounds when it connects to the systems where work actually happens.

For technology leaders, this creates a practical problem. Legacy systems were not designed with AI in mind. They use older protocols, store data in proprietary formats, and often lack modern APIs. Connecting them to AI capabilities requires more than installing a plugin.

Three Approaches That Are Working

Organizations solving this problem tend to follow one of three paths, depending on their technical resources and risk tolerance.

The API Layer Approach

The most common method involves building an API layer that sits between legacy systems and AI tools. This middleware translates requests and responses, allowing AI models to interact with older systems without modifying the underlying technology.

This approach works well when legacy systems have some ability to expose data, even if not through modern REST APIs. Database connections, file exports, or older SOAP services can all feed into a translation layer that AI tools can query.

The advantage is minimal disruption. The legacy system continues operating exactly as before. The risk is complexity. Every integration point requires custom development, and changes to either the legacy system or the AI tool can break the connection.

The Data Lake Strategy

Some organizations take a different approach by extracting data from legacy systems into a centralized repository. AI tools then work with this data lake rather than connecting directly to source systems.

This method provides more flexibility for AI development since data scientists can work with clean, structured data without worrying about legacy system constraints. It also creates a foundation for analytics and business intelligence beyond AI use cases.

The tradeoff is latency. Data in the lake is only as fresh as the last extraction. For use cases requiring real-time information, this approach falls short. It also requires significant upfront investment in data engineering before any AI value materializes.

The Protocol-Based Method

A newer approach uses standardized protocols designed specifically for AI integration. The Model Context Protocol, developed to help AI systems interact with external tools and data sources, represents one example of this direction.

Rather than building custom connections for each integration, protocol-based methods establish a consistent interface that multiple systems can implement. This reduces the per-integration cost and makes it easier to swap components as technology evolves.

Advisor Labs, an AI consulting firm specializing in enterprise integration, has noted that protocol-based approaches work particularly well for organizations with multiple legacy systems. Instead of building dozens of point-to-point connections, they build a smaller number of protocol-compliant interfaces that serve many use cases.

Where to Start

The organizations seeing the best results from AI integration share a common trait. They start with a specific business problem rather than a technology initiative.

This means identifying a process where AI could deliver measurable value, then working backward to determine what system access that process requires. A customer service improvement initiative might need access to order history, product information, and previous support tickets. A demand planning project might need sales data, inventory levels, and supplier lead times.

Starting with the business problem accomplishes two things. It focuses technical work on integrations that matter. And it creates a clear way to measure whether the integration succeeded.

The Build vs. Buy Question

One decision that stalls many AI integration projects is whether to build custom solutions or buy packaged tools.

The answer depends on how central the capability is to your business. Commodity functions like document processing or basic customer service automation have mature vendor solutions that work well for most organizations. Processes that differentiate your business or require deep integration with proprietary systems often justify custom development.

Many organizations end up with a hybrid approach. They use vendor tools for standard functions and build custom integrations for their unique requirements. The key is being honest about what actually makes your business different versus what just feels different because you have always done it a certain way.

What Comes Next

AI integration will get easier over time. Standards are emerging. Vendors are building better connection options. The current generation of AI tools is more adaptable than previous technology waves.

But waiting for perfect conditions means falling behind competitors who are figuring it out now. The organizations building integration capabilities today are developing institutional knowledge that compounds over time. They learn what works in their specific environment. They build relationships between technical and business teams. They create foundations that support future AI use cases, not just current ones.

The legacy systems that feel like obstacles today can become advantages tomorrow. They contain years of business logic and historical data that newer competitors lack. The challenge is building bridges that let AI access that value.

That work is happening now, one integration at a time.

Frequently Asked Questions

What does it mean to “integrate AI” with legacy systems (not just “use AI”)?

Using AI often looks like a standalone tool that creates output you still have to copy into your systems. Integration means the AI can securely read and write to the tools that run your store operations, like your ERP, CRM, inventory, and support platform. The article notes that isolated tools deliver isolated results, like a chatbot that cannot access order history and can only give generic answers.

Why do so many AI projects stall in ecommerce, even when the AI tool is “good”?

The article’s core point is that the bottleneck is not model quality, it is system access. Many older systems were not built with modern APIs, use older protocols (like SOAP), and store data in formats that are hard to connect to AI. If your AI cannot pull real order, inventory, and customer context, you end up with reports and suggestions that require manual work, which kills ROI.

What is the fastest way for a Shopify business to get AI value without replacing core systems?

Build an API layer (middleware) that sits between your legacy tools and your AI, so the AI can query what it needs without changing the old system. The article calls this the most common method because it minimizes disruption: the legacy system keeps running as-is. For a Shopify brand, this can mean connecting AI to order history, product info, and support tickets so actions happen inside your existing workflows.

When does the “API layer approach” become risky or expensive?

It gets tricky when every new use case needs another custom connector and ongoing maintenance. The article warns that changes to either the legacy system or the AI tool can break the connection, which adds hidden cost over time. If you are a Shopify team moving fast, plan for monitoring, version control, and an owner for each integration so it does not become a fragile mess.

Should Shopify teams build a data lake for AI, and what is the tradeoff?

A data lake can be great when you need clean, structured data for forecasting, segmentation, and deeper analytics, because AI tools work from one centralized source. The article highlights the major drawback: latency, since data is only as fresh as the last extraction. If you need real-time use cases (like “Where is my order?” support or live inventory checks), a data lake alone will feel stale unless you add more frequent syncing.

How do you choose between an API layer and a data lake for ecommerce use cases?

Use an API layer for “right now” decisions (support answers, order status, inventory checks) and a data lake for “patterns over time” work (demand planning, LTV modeling, marketing insights). The article frames it as a choice based on technical resources and risk tolerance, plus how fresh the data must be. A practical move is to start with an API layer for one high-impact workflow, then add a data lake later to expand analytics.

What is a protocol-based method, and why does it matter for growing Shopify brands?

The article points to newer standards like the Model Context Protocol, which aim to create a consistent interface between AI and external tools. Instead of building dozens of one-off connections, you build fewer “protocol-compliant” interfaces that can power many AI use cases. Advisor Labs (cited in the article) notes this works especially well when you have multiple legacy systems, because it reduces the number of point-to-point integrations you must maintain.

What is the best place to start if my Shopify store has messy systems and scattered data?

Start with a specific business problem, not a tech project, then work backward to the exact system access you need. The article gives clear examples: improving customer service requires order history, product info, and previous support tickets; demand planning needs sales data, inventory levels, and supplier lead times. Pick one workflow with measurable impact (fewer tickets, faster replies, lower stockouts), then integrate only the data required for that win.

What common misconception about AI integration hurts ecommerce ROI the most?

The misconception is that “adoption equals integration,” when they are not the same thing. The article cites a 2024 McKinsey survey showing 72% of organizations have adopted AI in at least one function (up from 55%), yet many of those deployments remain isolated. For Shopify teams, this looks like paying for AI tools that produce ideas but do not connect to inventory, orders, or support, so the business impact stays small.

What are practical, high-ROI ecommerce use cases that benefit most from connecting AI to legacy systems?

Customer support is a top ROI play, because the article notes a chatbot without order history stays generic, while a connected one can answer with real context and reduce manual lookups. Demand forecasting and replenishment also improve when AI can connect to inventory systems, because you stop producing reports that someone has to manually act on. For Shopify marketers, integration can also mean AI that reads product margins, stock levels, and campaign history so it recommends promos you can actually fulfill.