For CFOs, controllers, finance managers, and FP&A teams in SMEs. Month-end close, cashflow forecasting, variance analyses, and board packs without the whole week disappearing into it. Integrates with Exact, AFAS, NetSuite, Microsoft Dynamics, Unit4, and Power BI.
AI for finance teams in an SME addresses a specific problem. The team is small (one controller, sometimes two, a CFO who also covers HR and legal) but the output demand is large. Month-end has to be faster, board packs more concise, forecasts updated more often, scenario analyses on shareholder request. At the same time more reporting requirements keep arriving: CSRD, ESG data, banks checking quarterly covenants, investors who want to look directly into the dashboard.
AI does not change a controller's work, but it does change the pace. A variance between period and budget that needs investigating first gets an automated hypothesis: which general ledger account explains the largest part, which cost centre deviates most, what pattern existed in the same month last year. The controller reads instead of searches. A 13-week cashflow based on open AR, scheduled AP, and seasonal patterns is continuously ready, not built once a month by hand. A board pack receives a first textual explanation the CFO only has to sharpen.
DataDream works for finance teams in SME companies, especially in trade, manufacturing, services, and e-commerce. Builds happen on the source systems already there: the ERP, the BI layer, the data warehouse, the spreadsheets nobody wants but exist anyway. EU-only AI providers, no training on company data, audit trail for the annual accounts audit, and transparency per use case so the DPO and external auditor can read along.
Starting can be small. The first pilot is often an automated month-end narrative or a 13-week rolling cashflow. One process, measurable time difference, a team that will actually use it. Only when that works does expansion follow to consolidation, board packs, scenario modelling, or CSRD data. First lighten the workload, then larger ambitions. For external accounting firms and bookkeepers, see AI for accountants.
Organizations working with DataDream
Consolidation, intercompany matching, variance investigation, typing narrative for the management report: before you know it a week has passed before the figures reach the board. And the week after the next one starts again.
AI produces a first variance analysis with hypotheses per general ledger account and cost centre, and drafts a narrative the controller only has to validate. What took a week shrinks to days, with the same accuracy and a better audit trail.
Every week or month someone rebuilds the 13-week file: export AR aging, copy AP schedule, recurring items in, seasonal corrections. One formula broken and the whole forecast shifts.
AI builds a continuously updated 13-week cashflow directly on the source systems (ERP + bank), with automatic seasonal correction and scenario knobs. The controller checks instead of rebuilds and can run a what-if scenario within minutes on request.
The CFO builds the board pack themselves because otherwise the explanations would not be right. End result: a fundamentally strategic role turns into a PowerPoint editor, and that one EBITDA margin chart is rebuilt by hand every quarter.
AI generates the board pack from a fixed template, fills the visualisations based on current data, and writes a draft commentary per slide. The CFO refines the narrative and strategic conclusions, not the layout.
The shareholder asks for a scenario where revenue drops 10 percent. Or a disappointing cost line. Or the impact of an acquisition. Every question a new model in Excel, with the well-known formula-and-link issues.
AI builds a structured scenario model on the actual sources, with assumptions you change without the structure collapsing. Questions that normally took days get answered within a session, with traceable assumptions for the shareholder.
CSRD approaches, banks ask for additional covenant reporting, investors want ESG data, the management team wants weekly KPIs on the dashboard. With the same team. Every new report pulls time from real analysis.
AI helps with data collection and a first textual summary for CSRD disclosures, automates recurring covenant reports, and maintains KPI dashboards. The controller stays owner of what goes out but no longer types it.
Finance workflows often stop at the dashboard: data is visible, but the next step (processing an invoice, posting a write-off, sending a payment reminder) a team member still does manually in the ERP.
An AI agent gets API access to your ERP (Exact Online API, AFAS REST API, NetSuite) via an access token and executes defined actions autonomously: process inbound invoices and match to the right cost centre, send payment reminders on schedule, walk through month-end checklists, pull and aggregate CSRD data from source systems. You set the guardrails: which actions the agent may take, which booking rules it may not bypass, and what gets logged for the accountant and the DPO. For the broader agentic approach see AI agents.
Laurens helped us bring a programme offering to life that was yet to be launched. We worked very well together on this project. Here's to more great cases where we can use AI to improve our services!
Jordi Dooge
Business Scout, Dockwize
An accountancy firm delivers administration and annual accounts to a client. A finance team works internally: you or your controller build the management report, the forecast, the board pack. The questions differ. A firm wants to speed up invoice processing and Wwft checks. An internal finance function wants to know faster whether margin per business unit deviates, whether cashflow still fits over six weeks, and what a scenario does to DSO. DataDream builds for the internal team, with integrations on your ERP (NetSuite, Exact, AFAS, Microsoft Dynamics, Unit4) and BI layer. For firm work there is a separate page.
Yes. The integration runs through the source systems you already use: ERP (Exact, AFAS, NetSuite, Microsoft Dynamics, Unit4, SAP Business One), BI (Power BI, Tableau, Looker), data warehouse (BigQuery, Snowflake, Postgres), and the spreadsheets where it still happens anyway. The AI layer is placed on top without the team switching workflow. A controller stays in Power BI or Excel, only the variance analysis is no longer typed by hand and the management summary no longer written in four hours on a Sunday evening.
Honestly: AI is not a crystal ball. A 13-week cashflow forecast based on AR aging, AP schedule, and historical seasonality is usually accurate within reasonable margins, and above all consistent. That last part is what a controller gains: not building a forecast manually once a month, but a continuously updated baseline where variances stand out immediately. For strategic plans the finance manager keeps deciding which assumptions go in. AI calculates, the human picks the scenario.
This is usually the first question from a CFO and rightly so. Financial source data is sensitive: margins, client contracts, salary information, forecasts not yet shared externally. DataDream works with EU-only AI providers where data is not used for training and not stored after processing. For companies with shareholder requirements or a DPA that genuinely does not allow data to leave the company network, a setup with self-hosted models or an EU private cloud can be built. Per use case it is documented which data is processed and by which model, so the DPO and auditor can read along.
No, and that is not a marketing answer. AI is good at repeatable work: drafting variance analyses, writing a management narrative that explains the numbers, composing a board pack from fixed templates, anomaly detection on general ledger entries. AI is bad at judgement: deciding whether a price increase is strategically wise, assessing whether an investment fits the direction of the company, having a conversation with sales about a deviation. The controller and FP&A analyst therefore shift from reporting to analysing. That is exactly what they were hired for.
The AI Act imposes requirements on AI in financial decision-making, especially decisions affecting people (credit decisions, risk classifications, fraud detection). For internal management reporting and forecasting most work falls outside the high-risk categories, provided there is a human in the loop on decisions that go out. DataDream documents per use case which risk category the application falls into and what obligations apply (transparency, documentation, human oversight). See the AI Act page for broader context.
First a short scan: where in your finance process is the most time stuck? Often that is in the month-end close (consolidation, variance, narrative), in the cashflow forecast rebuilt every week, or in the board pack the CFO is typing in PowerPoint on Friday. Then a focused pilot on one of those processes, with measurable output. Only when the pilot works and the team values it does scale follow. No wholesale transformation, no "AI strategy" without ground under the feet.
Yes, and that is the step beyond reporting automation. A data layer gives visibility, but an agentic workflow also takes the next step. An AI agent gets API access to your ERP (Exact Online API, AFAS REST API, NetSuite) via an access token and handles defined ERP actions autonomously: process inbound invoices and match to purchase orders, send debtor reminders on schedule, walk through month-end checklists, aggregate CSRD data from multiple source systems. You set the guardrails: which actions the agent may take autonomously, which always require human approval, and what gets logged for the audit trail. This reduces the time between "data is available" and "action is done" to zero. For the broader agentic approach see AI agents.
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