DataMinds Connect & FabCon 2026: 5 data and AI trends you cannot ignore as a finance professional

Between the deep dives into lakehouses, AI agents, and data models, we noticed one clear shift: the gap between raw data and the boardroom is disappearing. Technology is no longer exclusively the domain of IT; it is the engine driving strategic leadership in finance and the organization as a whole.

These are the five key trends that will determine whether AI becomes a success or a stumbling block in your organization.

Generative AI functionalities are becoming deeply embedded within Power BI and Microsoft Fabric. From automatically drafting monthly reports to detecting anomalies in GL accounts: the use cases for AI in finance are promising. In practice, however, the daily reality for finance teams is often sobering. You cannot make reliable predictions if your basic P&L and cash flow reporting in Power BI is not scalable or accurate.

At TriFinance, we look beyond the newest buttons in the interface. AI is not a magic wand that automatically straightens out sloppy administration or diffuse KPI definitions. In fact, unleashing AI on a fragmented data landscape only increases the risks of process automation.

Building future-proof financial reporting requires more than just loading data. You need a well-thought-out star schema, robust DAX measures, and a functional visual layer, potentially reinforced with solutions like Aimplan for write-back functionalities. Additionally, metadata about the semantic model is essential: use clear definitions and descriptions for columns and measures, so AI assistants can make decisions based on validated context.

"We often see finance teams get stuck on the hierarchical complexity of a P&L in Power BI. By embedding the financial logic directly into the architecture of Microsoft Fabric, you not only solve your current reporting pain points, but you immediately lay the foundation on which advanced forecasting and AI can run."
Evert Augustyns, Senior Project Manager Data & Reporting at TriFinance

The real breakthrough of AI in finance is therefore not in the software updates themselves, but in the preconditions. Only when your data management and governance are up to standard, and finance professionals learn to ask the right critical questions, will AI evolve from a handy assistant into a reliable sparring partner. AI does not replace your critical eye; it enhances it.

“In projects and conversations with finance teams, we often see the urge to immediately start working with advanced AI features. Our advice remains pragmatic: don't start with the algorithms or prompt engineering, but with the robustness of your data quality, the maturity of your processes, and clear semantic models. Only when that foundation is in place does AI deliver real strategic value and time savings."
 Jonas Arnout, Expert Lead Data & Reporting at TriFinance

AI tools (like Copilot) have no use for isolated tables. AI needs business context and meaning.

Organizations must start treating data models and business definitions (semantic models) as strategic assets. At FabCon, Microsoft positioned Ontology as the intermediary layer connecting end-user questions to the data through the right context. After all, when an AI agent answers questions based on data without clear definitions, hidden data quality issues suddenly become major risks. Therefore, invest in clear definitions, data ownership, and security.

A single source of truth (SSOT) has been the dream of every CFO for years. Yet, many organizations still steer based on shadow Excel sheets. Previously, architecture forced us to copy data from ERP to data warehouse to reporting, resulting in slow data and debates about whose numbers were correct.

Thanks to innovations in Microsoft Fabric (like OneLake), data no longer needs to move for real-time reporting.

But beware: the real breakthrough is not in the software alone. A faster platform is only useful if IT and finance speak the same language. Only when you link this technology to strict data governance and clear ownership within the business, does that SSOT truly become a reality.

The traditional dashboard is passive by definition: you look at historical numbers, spot an anomaly, and then have to switch screens to act on it in your ERP or via email. That fragmentation is disappearing.

One of the most important developments we saw is that analytics and application development are increasingly merging. With innovations like Fabric Apps, the reporting environment is evolving into an interactive platform. Finance teams can not only analyze anomalies within the same secure data environment, but directly adjust forecasts, initiate workflows, or register approvals. You close the loop between insight and action right at the source.

Planning in Microsoft Fabric IQ is designed to bring budgets, forecasts, targets, and scenario models together on the same managed foundation as actuals. It connects planning directly with Power BI semantic models for reliable business metrics, Fabric SQL for write-back operations, and OneLake for both analytical and operational data. For finance teams, this narrows the gap between reporting on what happened in hindsight and actively steering based on what is happening now.

The announcements at FabCon Europe sharpen this valuable starting point. 
Key updates and capabilities include:

• Faster connections to existing semantic models
• A native planning engine in preview for larger and more complex models
• Multi-driver and multi-measure simulations
• A tighter integration between semantic models and Fabric IQ
• Event-driven automation
• Broader distribution via organizational apps and additional governance controls

Additionally, OneLake write-back and Private Link support were announced as future functionalities. The practical implication is clear: finance can view Fabric Planning as a managed extension of the existing Power BI and Fabric landscape rather than an isolated budgeting tool. This helps reduce reconciliation work and shortens the cycle between actuals, assumptions, forecasts, and action. However, designing such a planning model still requires conscious choices regarding model grain, scenarios, workflows, security, capacity, and ownership.

The takeaways from dataMinds Connect show that the role of finance is fundamentally changing. Data drives decision-making, but translating raw metrics into reliable, AI-driven insights requires a structured approach.

What is your next step?

• For organizations: Is your organization struggling with scalable financial reporting, or do you want to discover how AI can optimize your finance processes? At TriFinance, we bridge the gap between complex data architecture and your finance strategy. 
• For data talent: Are you a data engineer, Power BI consultant, or EPM consultant who looks beyond the code and wants to make a real impact in the boardroom? Join our team and help build the finance department of the future. Check out our job openings.