Dominic Jainy is an industry veteran in the ERP and information technology landscape, possessing a deep reservoir of knowledge regarding Microsoft Dynamics 365 Business Central. With a career spanning the evolution of business intelligence from static spreadsheets to sophisticated artificial intelligence, Dominic has helped countless organizations navigate the treacherous waters of data reporting. His expertise lies not just in the software itself, but in the intersection of cloud architecture and practical finance operations. As an advocate for data governance, he focuses on how mid-market companies can transform their reporting from a reactive, manual burden into a strategic asset that is truly prepared for the AI era.
In this discussion, we explore the current state of reporting for Business Central users, moving through the five common paths organizations take—from native account schedules to cloud-native platforms like Cosmos. We delve into the psychological and operational toll of the “Excel trap,” where fragmented workbooks erode trust during critical business periods. The conversation also shifts toward the technical requirements of being “AI-ready,” emphasizing that successful implementation of tools like Copilot requires a solid architectural foundation rather than just flashy features. Finally, we discuss how to effectively pressure-test new reporting solutions during demos to ensure they can handle the real-world complexities of multi-entity consolidations and high-volume data.
When a finance team moves beyond basic P&Ls to manage multiple entities or regions, why do the native reporting tools in Business Central often start to feel like they are hitting a ceiling?
The native reporting layer in Business Central is quite reliable for a single-company setup where you just need to glance at a trial balance or a standard balance sheet. However, the moment your business grows and you introduce multiple entities, different currencies, or complex dimensions for specific departments and projects, that built-in layer starts to feel incredibly rigid. It is a common sight in finance departments: an executive asks for a regional performance view across all companies, and instead of clicking a button, the team goes silent and starts the long process of exporting data. You can feel the tension in the room during a close week when these reports are due, and the tools simply cannot pivot fast enough to show data across those different dimensions without significant manual intervention. When you reach that point, you aren’t just managing data; you are wrestling with a system that wasn’t designed for that level of multi-entity fluidity, and that is usually the signal that your reporting stack has been outgrown.
We often hear about the “Excel trap” in finance departments. How does a reliance on hand-built spreadsheets impact a company’s ability to scale or maintain a single version of the truth?
Excel is the ultimate comfort zone for any controller or finance manager because it is fast and infinitely flexible, but that flexibility is exactly what makes it dangerous at scale. I have seen workbooks with 47 different tabs, each one bearing a file name like “final_v2_updated,” and at that point, the “truth” becomes a matter of opinion rather than data. When your business depends on these fragile artifacts filled with complex formulas that only one person in the building understands, you are one broken link away from a reporting disaster. It’s a sensory nightmare during board meetings when two different department heads present two different numbers for the same margin, leading to a total erosion of trust in the numbers. This manual reshaping and constant refreshing of data is a massive hidden cost, slowing down the entire organization just when they need to be the most agile.
Many organizations are still holding onto legacy tools like Jet or Solver. What are the specific friction points these teams face as they try to modernize their cloud strategies?
Legacy tools like Jet or Solver have a massive footprint because they’ve been around for a long time and are deeply embedded in the culture of Dynamics shops, but they often struggle to keep pace with a modern cloud-first strategy. As data volumes grow and the number of entities increases, the performance of these older tools can start to lag significantly, turning a quick report refresh into a coffee-break-long wait. They often require a handful of internal experts to keep the gears turning, meaning if those people leave, the reporting system effectively breaks. Instead of being a platform that supports growth, these tools can become “technical debt” that you have to manage around while trying to implement newer cloud features. It creates a disjointed experience where you have a modern ERP like Business Central paired with a reporting engine that feels like it’s stuck in the previous decade.
Power BI is often touted as the ultimate solution for visibility, yet some finance teams find it frustrating for day-to-day reporting. Where does the gap between executive dashboards and “pixel-perfect” financial statements usually appear?
Power BI is absolutely brilliant for high-level interactive dashboards, trend analysis, and providing that “big screen” KPI visibility that executives love. However, the frustration creeps in when you try to use it for highly formatted, pixel-perfect financial statements or the type of Excel-centric workflows that finance teams use for deep-dive analysis. It is not naturally built for that level of granular, row-level formatting that a formal board pack requires. Furthermore, if every department starts spinning up its own Power BI datasets without centralized logic, you end up with a governance headache where “margin” or “backlog” is defined three different ways in three different places. You might get a demo that looks impressive with shiny charts, but without a governed layer underneath, you’re just visualizing a mess rather than solving the underlying reporting problem.
There is a lot of hype around AI and Copilot right now, but you’ve mentioned that “AI-ready” reporting is more about architecture than features. Could you explain what a truly AI-ready data structure looks like?
Being AI-ready isn’t about how many prompts you can type into a chat box; it is about whether your data and reporting logic are structured enough to be trusted and reused by an algorithm. In the context of Business Central, it means your key metrics—things like revenue, on-time delivery, and backlog—are defined exactly the same way across the entire organization. If you feed an AI a decade’s worth of shortcuts, manual exports, and contradictory logic, it will simply give you “bad answers faster,” which is a dangerous place for a business to be. A true AI-ready architecture uses a clean, governed reporting model that the AI can query without inheriting all the human errors of the past. It requires a shift in focus from the “flashy demo” to the boring but essential work of data governance and ensuring your backend logic is bulletproof.
How do cloud-native, BC-only platforms like Cosmos fill the gap for mid-market teams that don’t have a massive data engineering budget?
A platform like Cosmos is designed specifically for the Business Central user who is stuck between the limitations of basic exports and the overwhelming complexity of a full-scale custom BI project. Because it is “born-in-the-cloud” and built exclusively for BC, it doesn’t ask you to start from raw, confusing tables or design a data model from scratch. It comes out of the box with a prebuilt data model and over 30 prebuilt reports, which dramatically lowers the implementation risk and shortens the time it takes to see real value. The genius of this approach is that it keeps the familiar Excel front end that finance teams love, but it connects it to a governed, Azure-based data model. This means you get the speed of a cloud-native tool without needing a team of scarce internal developers to tweak every single report or data point.
When a company is sitting through a demo for a new reporting tool, what kind of “pressure tests” should they perform to see past the sales pitch?
A demo should never just be a tour of pretty visuals; it needs to be a stress test of your actual business reality. I always suggest asking the vendor to run a multi-company P&L with several years of history right there in the session to see if the performance holds up or if the system starts to chug. Ask a non-technical person in the room to try and make a live change to a report in Excel during the demo to see how user-friendly it actually is. Another great test is to ask them to change a fundamental KPI definition, like gross margin, and watch how quickly that change ripples through every other report in the system. You want to simulate the pressure of a “close week” by running several reports at once; this reveals if the tool is truly built for the way you work in Business Central or if it’s just designed to look good in a controlled environment.
What is your forecast for the future of ERP reporting as AI becomes more integrated into the Microsoft ecosystem?
I believe we are moving toward a world where the “report” as we know it—a static document you look at once a month—will become secondary to the “governed data stream.” As AI becomes more deeply integrated into Business Central, the winners will be the companies that stopped treating reporting as a series of one-off tasks and started treating it as a centralized, governed asset. We will see AI moving from simple data retrieval to proactive analysis, where it tells you why a margin dropped in a specific region before you even think to ask. However, this future is entirely dependent on the quality of the foundation; companies that continue to rely on ungoverned Excel exports will find themselves left behind because their data won’t be “clean” enough for the AI to process. The focus for the next few years shouldn’t be on buying the most expensive AI tool, but on building the most robust reporting layer that you can actually trust.