Why generic AI mostly gets the insight wrong for distributors

A recent Phocas survey found that half of the distribution respondents are testing general-purpose AI tools like ChatGPT and Claude on their own, outside of any business system. That's a reasonable thing to do but it is also where generic AI gets the numbers wrong for distributors in ways that are hard to know, because the answer looks exactly like a right one. It is important to know why a powerful generic AI tool can still be a wrong one and some of the important considerations of handing over your data to an AI engine.

AI can easily connect to an ERP but it doesn't know a distributors' data rules

AI can connect to ERP tables or data cubes today and probably assumes that everyone in a business already agrees on data rules and how various transactions are handled. The ERP data is the authority at the transaction level because it's the most accurate record a distributor has. However, ERP providers and their BI development teams internally or externally know the data only becomes trustworthy for analysis once it's been layered and validated so it can be queried consistently across departments. This is an important step most generic AI tools skip or many IT managers learn the hard way. They can connect a system to an ERP but joining the data between systems is much harder and requires collaboration across the business.

When using generic AI for data analysis you need to tell it exactly what you want. You also need to know what a right answer looks like well enough to spot a wrong one and understand the business logic behind the question you're asking.

We know people share prompts and get AI guides to help them use generic AI tools but using Claude for data analysis in a distribution business is starting from scratch. The best prompts are not usually specific to distribution businesses and often don't know the intricacies of let’s say, an auto-parts margin definition. If ‘margin’ means something slightly different in your inventory system than it does in your finance reports, a skilled prompt engineer and an unskilled one will both get an answer, probably both wrong.

A validation step sometimes works

Some IT teams in distribution build a data validation agent to help spot problems. This is a second AI step that checks the first answer before it serves up the final figure. It's a sensible practice and can catch real errors like a wrong data join, a stale export or a percentage applied to the wrong number.

A validation agent checks whether a number is internally consistent but is not so good on your specific business rules. The AI doesn't know if active customer means what your sales team agreed the term to be twelve months ago. The person typing the original prompt often doesn’t know a validation step exists at all, so they don’t activate it. To make AI work when you build it inhouse comes back to getting the AI process or rules consistent across your business for all the workers as well as having shared and governed definitions for your AI tool to check.

Why connected data is necessary for distribution businesses

Connected data is the difference between a distribution-specific AI tool and a generic AI tool because it gives AI the context to understand distribution. In a recent Distribution Strategy Group survey about the State of Technology in Distribution found AI implementation problems usually trace back to data and connectivity. 

AI produces reliable business understanding when it's built on mapped definitions, governed data, historical context and industry logic rather than just a live connection to raw data tables.

Mapped definitions ensure that margin, active customer and sale value mean the same thing on every dashboard and in every AI answer, because they are defined for all use cases. Governed data ensures the same customer, product and account across your ERP, CRM and e-commerce platform is one record. Historical context means a slow-moving SKU or a growing account is judged against your business’s own trend or target. Industry logic gives the AI model distribution-specific data, so it understands your product mix or business model.

Phocas’ ERP partners are adding strong built-in AI capabilities, and they would agree these advances don’t replace the need for analytics and connected data. The only instance would be for the ERP super user who knows the data and knows exactly what question to ask, maybe then this form of AI can be a substitute for an extra analytics layer.

Phocas AI-powered Analytics and Insights help everyday users access the same trustworthy numbers, with validation and updates built in. Users can drill down into dashboards and get what they need without learning every rule, because Phocas handles that work for them. AI needs to sit on top of the same trusted data foundation your business runs on rather than becoming its own separate silo when using generic AI. Phocas continues its distribution-specific game plan by connecting to industry ERPs and other systems. We uses definitions, history and governance already built in, so the AI answer and the ERP source are connected and accurate.