What exactly are AI Workers?
An AI Worker is a piece of software that takes over a well-defined task in your operations. Not a chatbot, not a dashboard full of insights. A Worker does something: it reads a document, extracts the relevant fields, compares them with what is already in your system, and presents a proposal or notification to the employee who needs to approve it. The Worker operates alongside your existing ERP, TMS, or WMS. It does not need access to your entire data model. It has a task, an input, and an output. That makes it fast to build and easy to audit.
What problem does it concretely solve?
The classic example: an employee receives twenty packing slips, invoices, or CMR documents by email every morning. They open each document, re-type the data into the system, and send a confirmation back. That takes two hours a day. Those two hours do not appear in their job profile as valuable working time, but they are there. An AI Worker reads those documents automatically, parses the fields, prepares a draft entry in the system, and only asks the employee to review and approve. The employee is still in the loop, but their attention goes to exceptions, not to retyping. The same principle applies to quote assembly, customs document checks, or assigning transport orders based on fixed rules.
Where do teams get stuck during implementation?
The biggest obstacle is not the technology, but the definition of the task. Teams working with AI Workers for the first time tend to expand the scope: 'can we also add X, and Y?' That is understandable, but it makes a Worker unmanageable. A Worker performs well when the task is sharply defined: one input, one output, one decision point. A second common issue is the quality of the source data. If the documents you are processing are inconsistent in formatting or completeness, the Worker will also perform inconsistently. You do not fix that with better AI, but by agreeing with your suppliers or customers on document formats. A third point: integrating with the existing system takes more time than people expect, especially when the package's API is limited or there is no API at all.
When are AI Workers not the right fit?
If your core system is itself the problem, Workers will not solve it. A Worker is a layer on top of an existing system. If that system is slow, error-prone, or fundamentally unsuitable for your current operations, adding a Worker means putting a fix on a problem that runs deeper. In that case, it is more honest to consider whether you would be better off rebuilding the core system from scratch, custom-built, with AI on the inside. That is what we call the Bonsai AI Digital Twin. Beyond that, Workers are not the right choice when the task you want to automate is materially different every time and therefore requires human judgment that cannot be captured in rules or examples. AI is good at recognising and applying patterns. It is not good at assessing situations that fall outside those patterns, unless you have explicitly trained it to do so.
How do Workers relate to products like Dottle and Quote?
Dottle and Quote are ready-made Workers for two specific tasks: document processing and quote automation. They are faster to implement because the core is already built and you configure them for your documents or your pricing logic. We build custom AI Workers when the task is too specific for a standard product, or when the integration with your system requires more tailoring. The distinction is practical: start with a product solution if it fits, choose custom when the context is too different. We help you make that call, even if the answer is that nothing needs to be built.