Data & AI platform by Data Analytics
AI for your SME starts with useful business information
As a business owner, you want to know how your company is performing without repeatedly gathering figures from different files. Your bookkeeper holds part of the picture, while operational information sits with employees and in other packages. AI can only provide focused support when that information is connected meaningfully. ENABLE, the Data & AI platform from Data Analytics, connects and structures reliable data for controlled dashboards, analysis, and developing and sharing apps and AI. Data Analytics supports setup and management.
The examples on this page are hypothetical situations, not customer cases.
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01
Make management reporting useful for your decisions
Figures used for bookkeeping do not automatically answer every question involved in running your SME. You may want to track margin by activity, work in progress or the relationship between staff effort and invoiced work. Agree with your bookkeeper which additional data is needed and who supplies it.
Example: an installation company in Flanders wants a monthly view of which types of jobs produce lower margins. Purchase invoices alone are not enough; hours worked, materials and work not yet invoiced may also matter. Start with a clear reporting question so an AI explanation does not get ahead of missing project information.
02
Divide responsibilities between business owner and bookkeeper
Your bookkeeper can explain the financial records but may not know every commercial agreement or situation on a job site. You and your employees add that business context. Record who investigates variances, who submits corrections and when figures are sufficiently complete for a discussion about performance and expectations.
A shared review structure helps distinguish provisional information from closed periods. A missing invoice or a stock adjustment still to be processed can affect the picture. You may use AI for a draft explanation, but have the responsible people assess whether it reflects the reality of your business.
03
Examine administrative workload one step at a time
Administrative workload often lies in small handovers: someone searches for a document, asks for clarification and retypes the same context. Map that process before choosing an AI application. What information is missing, where does duplicate work arise and which assessment genuinely requires an employee's experience?
Example: before a monthly meeting, your administrative employee repeatedly gathers explanations for exceptional costs. A scoped application could help summarise available notes and figures. Whether that is appropriate depends on the quality and accessibility of that information. Do not treat an unchecked summary as accounting evidence.
04
Include your budget and internal effort in the decision
For an SME, the question is not only what is technically possible, but also what you can maintain. Alongside setup, discuss management, any model services and the time your employees need. Ask which components are necessary for the first application and which only become relevant with a later expansion.
Choose a first step whose use you can evaluate, such as preparing one recurring management meeting. Agree in advance when you will review it and what you will assess: usefulness, answer quality and the follow-up required. This lets you work from observed experience without assuming savings or a return beforehand.
05
Keep access and model choice understandable
Determine who needs which information: the business owner, a department lead and an external bookkeeper do not automatically have the same role. Discuss access to employee data, customer information and financial details separately. A small organisation still needs clear agreements, even when employees combine several functions.
Also ask where the chosen AI processes data. A local LLM is not the same as an external AI service receiving context through MCP. External processing depends on the choice and setup. Data Analytics helps you consider the required data, application and management arrangements together, in a way that fits your organisation.
Frequently asked questions
Does your SME need to centralise all data first?
No, not necessarily for an initial, scoped application. Start with the data your chosen question requires. Check its quality and relationships, however, so a limited start does not produce unclear or contradictory figures.
Does your bookkeeper remain involved in reporting?
You agree that together. The bookkeeper can help explain financial definitions and entries; you provide operational context. AI does not replace this division of responsibilities or take over responsibility for the substance of your reporting.
How do you keep the first step budget-focused?
Limit sources, users and questions to a concrete need. Discuss setup, ongoing management and internal effort in advance. Assess expansion only after it is clear how the first application fits into your daily operations.
Can AI make financial decisions independently?
Do not treat an analysis or proposal as permission to act. Define who checks and decides, for example for payments or credit arrangements. Any action-executing functions require separate setup, explicit authority and appropriate review.
Discuss your question with Data Analytics
Which sources, users and checks does your application need? Discuss your question in an ENABLE demo.
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