AI use cases for SMEs: what works in daily business

AI use cases that hold up in everyday SME operations have three things in common: they concern a recurring task, the inputs are digital and unambiguous, and a person can check the result quickly. Typical examples are reading receipts, drafting quotes, searching internal knowledge via RAG, pre-sorting emails, quality inspection with image recognition and support assistance. Each of these use cases has a prerequisite and a limit.

How do you recognise a viable AI use case?

A viable AI use case replaces a specific activity that someone performs regularly today, such as retyping, searching, summarising or sorting. It is not an end in itself but tied to a workflow you can show on site. If you cannot explain a use case using a real case from last Tuesday, you probably do not have one yet.

The following examples are typical scenarios from small and mid-sized companies. Each one lists what needs to be in place first and where the AI stops being reliable.

Can AI reliably read receipts and documents?

AI can read incoming invoices, delivery notes, order confirmations or forms and pass the relevant fields such as supplier, date, amounts and line items in structured form to accounting or the ERP system. Current models cope with different layouts without a separate template having to be built for each supplier.

• Prerequisite: a single inbound channel for receipts, such as a dedicated mailbox, and a target system with an interface that takes over the data.
• Limit: handwritten notes, poor scans and unusual document types lead to errors. Amounts and account assignments remain suggestions until a person confirms them.

Can AI prepare quotes?

AI can prepare a draft quote from a customer enquiry, the item master and similar past quotes: suggest suitable line items, write service descriptions and flag open points that need clarifying with the customer. Sales staff no longer have to gather everything by hand and can focus on reviewing and on the conversation.

• Prerequisite: a maintained item master with current prices and a findable archive of past quotes in which it is clear which version was sent.
• Limit: prices, discounts and delivery dates must never come from the language model, only from the leading system. A quote leaves the company only after human approval.

How does RAG make internal knowledge searchable?

RAG (Retrieval Augmented Generation) combines a search across your own documents with a language model. An employee asks a question such as “What torque applies when assembling on line three?” and gets an answer based on the matching passages from manuals or work instructions, including the source. This is particularly helpful when knowledge has so far depended on a few experienced people.

• Prerequisite: the documents are in one place, old versions are marked or removed, and the access rights of the file storage are carried over into the search.
• Limit: RAG cannot find what is not in any document. With contradictory versions, the model picks one and phrases it convincingly – which is why the source must remain visible.

Can AI pre-sort incoming emails?

AI can read incoming emails in a shared mailbox, classify them by concern – order, complaint, invoice, appointment request – and forward them to the responsible person or department. It can also extract key details such as order numbers and suggest a draft reply. This is especially helpful where one person currently distributes a mailbox by hand.

• Prerequisite: clearly defined categories and responsibilities, which the business should have anyway, and a rule for what happens with unclear emails.
• Limit: irony, several concerns in one message or new types of request get misclassified. Replies are suggested, not sent automatically.

When is quality inspection with image recognition worthwhile?

Quality inspection with image recognition is worthwhile when the same visual check happens often and under the same conditions, such as checking surfaces, completeness or labels on a production line. A camera captures every part, a trained model detects deviations and reports them or rejects the part. The inspection becomes more consistent because it no longer depends on the shift or on how someone feels that day. The camera, network and computing hardware at the inspection station are as much part of the project as the model.

• Prerequisite: constant lighting and camera position, a clear definition of what counts as a defect, and enough example images of good and defective parts.
• Limit: rare defect types that hardly appeared in training and frequently changing products still require human inspection or retraining of the model.

How can AI take load off support?

In customer support, AI can answer recurring questions, for example about delivery status, opening hours, operation or spare parts, and hand requests over to an employee with all the required details already gathered. Internally, it can help staff answer by finding matching passages from manuals and past cases.

• Prerequisite: maintained answers and documents, integration with the systems that provide status information, and a visible route to a human.
• Limit: complaints, goodwill decisions and special cases belong with a person. A bot that keeps customers stuck in a loop does more harm than good.

Which AI applications add little value in daily SME work?

AI applications that sit next to the actual workflow add little value. A chat window where you can ask questions, but whose answers then have to be copied by hand into the ERP system, saves hardly any work. The same applies to tools that write nice meeting summaries nobody reads afterwards, because the tasks from them never end up in the ticketing system or calendar.

Applications without a clarified data foundation are equally weak. A knowledge search over a file server holding the same work instruction in several versions gives contradictory answers and quickly loses employees' trust. The same applies to applications for which nobody is responsible: if it is unclear who checks and approves an AI suggestion, it is either adopted unchecked or not used at all.

• Results have to be transferred by hand into another system.
• The underlying documents are contradictory or outdated.
• Nobody is responsible for checking the results.
• The task occurs so rarely that nobody gets used to the tool.

In which order should an SME approach AI use cases?

For most SMEs it makes sense to start with a use case that works internally and whose mistakes have no external impact, and only then tackle customer-facing applications. That way the business gains experience with review effort, data quality and acceptance before a mistake becomes visible to a customer.

1. 01Pick a task that recurs regularly and stays internal, such as reading receipts or a knowledge search.
2. 02Organise the documents for this task: one location, one valid version, clear responsibility.
3. 03Define in advance what a usable result is and who checks it.
4. 04Run the pilot alongside the existing workflow and compare using real cases.
5. 05Only after stable internal use, move on to customer-facing applications. If several steps are later meant to run on their own across different systems, that is a case for an AI agent with clear approval points.

How does NDVDL put AI use cases into practice?

NDVDL looks at the workflow in your business before suggesting a tool. We let your employees show us which receipts, mailboxes, manuals or inspection stations are involved and which systems process the data further. You then receive a written proposal for a first, tightly scoped use case, including the groundwork it requires.

We implement the solution, connect it via interfaces to the ERP, accounting or ticketing system, and then operate and maintain it, from access rights to monitoring. For image recognition, we also look after the cameras, network and computing hardware on site. You have one fixed contact person for technology and workflow.