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AI for SMEs starts with one question and two sources

You do not need to redesign your entire organisation to investigate whether AI is useful. Start with one recognisable business question and two relevant data sources. This keeps the connections manageable and lets you assess what an application adds. For an SME, the first step is not about using as much technology as possible, but about a workable combination of information, responsibilities and a clear decision point.

The examples on this page are hypothetical situations, not customer cases.

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Choose a question someone needs to answer

Find a recurring question that currently lacks a sufficiently clear answer. Example: 'Which items have high inventory but limited recent sales?' Start with inventory data and sales records as two sources. Define the period you will examine and what you mean by high or limited. The result should enable a specific assessment.

This scope is a starting point, not a rule that two sources are always sufficient. Lead times or planned orders may prove necessary later. Add that information only when it is clear which limitation it resolves. This prevents an initial investigation from becoming a broad integration project without a recognisable first outcome.

Assign roles within your organisation

The director determines why the question matters and which decision it supports. The controller helps assess definitions and calculations. Operations contributes knowledge about day-to-day exceptions and feasibility. In a small organisation, one person may hold several of these roles, but the different perspectives remain necessary to evaluate the outcome properly.

Also appoint someone to collect questions and monitor progress. Agree who arranges corrections to source errors and who decides whether the application may be used. Without these agreements, discrepancies can easily fall between departments. Even a modest initial application needs ownership, regardless of how limited its technical setup may be.

Make the data understandable and accessible

Check whether the two sources can be combined usefully. Do they use the same item codes, periods and units? Friday's inventory balance is not directly comparable with an undefined sales average. Describe the calculation and manually check a few familiar items. This helps uncover discrepancies before they become part of an answer.

ENABLE is the Data & AI platform from Data Analytics for connecting and structuring business data, with controlled access for dashboards, analysis, and developing and sharing apps and AI. Data Analytics supports setup and management. Together, you determine which foundation the chosen question and users require.

Set measurable goals before the trial

First record how the current process works. Note, for example, how many actions are needed, which information is missing and how often corrections occur. Then choose trial assessment criteria, such as correctly answered test questions, traceable results or required manual adjustments. Measure the same aspects again so the comparison uses consistent assumptions.

Avoid assumed savings or a target based solely on frequency of use. Frequent opening says little about usefulness. Also assess whether employees understand the answer and can use it responsibly. Agree before starting when to continue, adjust or stop; rejecting an application can also be a useful outcome.

Expand only after substantive review

Example: the first analysis flags items with limited sales, but operations recognises some as essential spare parts. This is no reason either to follow the signal blindly or to abandon the entire approach. Add an appropriate category or review step and test again. Employees' practical knowledge makes the distinction useful.

Then decide whether a dashboard, conversational interface or another application fits best. Not every SME question requires AI. Also ensure that access, model use and management suit your data. For private platform choices and financial dashboards, see the existing pages; expand only when the initial application has demonstrated practical usefulness.

Frequently asked questions

Why start with two sources?

Two relevant sources let you investigate a specific relationship without connecting everything immediately. This is a practical scope choice. If your question requires different or additional data, adjust the scope deliberately.

Do we need our own AI specialist?

At a minimum, you need someone internally to oversee business context and responsibilities. The technical support required depends on the application. Data Analytics can support setup and management of the data foundation and platform.

What if a dashboard already answers the question?

That may be the appropriate choice. Do not make AI a mandatory project component. Investigate whether additional support for asking questions or interpretation genuinely adds anything to existing reporting that already works well.

When does expansion make sense?

When the initial application has been substantively reviewed, employees can use it and management arrangements are in place. Then add one clear requirement. More sources or users also increase the need to recheck definitions, access and responsibilities.

Discuss your question with Data Analytics

Which sources, users and checks does your application need? Discuss your question in an ENABLE demo.