I was going through the transcript for the new “Strategic Finance & Technology” course released this week. I paired it with the “Finance Under Pressure” report from insightsoftware. The numbers are alarming.
93% of finance teams say they are struggling with poor data management.
If you feel like you are fighting your spreadsheets, you are not crazy. You are just part of the statistical majority.
Here is what I found in the data and why the era of buying “more tools” is officially over.
The Reality of the “Franken-Stack”
I wanted to understand how we got here. How did we reach a point where 82% of finance teams are using four or more separate tools just to manage their data?
I looked at the historical trend. It is a pendulum swing.
- 1990s-2000s: We had the ERP era. SAP and Oracle were the kings. Everything was in one place. It was clunky and expensive but it was integrated.
- 2010s-2023: We moved to the Cloud and SaaS. This was the “Best of Breed” era. You bought one tool for expenses and another for tax. It was easy to swipe a credit card.
The result is what I call the “Franken-stack.”
We built collections of amazing tools that do not talk to each other. We created accidental silos. This worked fine when interest rates were low and we had time to fix things manually.
That time is gone.
The 2025 Stress Test
The course transcript highlights a specific shift. We are heading into 2026 and the external pressure is breaking these manual processes.
The report identifies three main drivers:
- Supply Chain Uncertainty (31%)
- Global Tariffs (30%)
- Interest Rates (27%)
In a stable economy, you can take five days to close the books. In late 2025, that is too slow. If a new tariff drops on Tuesday, the CEO needs an impact analysis by Wednesday. You cannot do that if you are manually exporting CSV files from four different systems.
The AI Problem
Everyone asks me about AI. They want the productivity boost. They want the automated analysis.
But here is the hard truth I found in the report: You cannot layer AI on top of messy data.
If your data is trapped in silos, your AI model is blind. The rush to adopt AI is actually forcing companies to look at their “data basement.” It is messy down there. You have to clean it up before you can build anything cool on top of it.
The “Swivel Chair” Accountant
This fragmentation has a human cost. The course describes the “swivel chair” problem.
Accountants are spending their days swiveling between screens. They copy data from a CRM. They paste it into Excel. They check it against the ERP.
We are turning high-value professionals into “human APIs.” We are using people to connect systems that should talk to each other automatically. It leads to burnout and it creates a strategic blind spot for the CFO.
Key Takeaways
I processed the course data and the report findings. Here is the bottom line for your 2026 planning:
- Audit Your Tools: Don’t buy anything new yet. List out what you have. If you are using more than four tools, map out how the data moves between them.
- Integration Over Dashboards: When I look at software now, I don’t care about the pretty charts. I care about the API. If it doesn’t talk to the ERP natively, it is just adding to the noise.
- Clean Data First: Digital simplification is the goal. You have to fix the data management plumbing before you can turn on the AI faucet.
Want to earn CPE for this topic?
- Compare Options: See how we stack up against others in our 2025 Flexible CPE Guide
- Understand the Format: Read how Nano-Learning works for CPAs.
- Check Your State: Ensure you are compliant with our State Requirements Guide.
- What is EverydayCPE?
Related Courses:
Latest Courses:
- When a Shoe Company Becomes an AI Company: The Allbirds Shell Pivot, Explained
- The Cognitive Offloading Paradox
- Confidence Is Not a Strategy: What the PwC C-Suite Outlook Means for Accountants
- AI Found Thousands of Zero-Days. What That Means for Accountants.
- AI Evals for Accountants: How to Know If Your Output Is Good Enough to Use
Response
[…] Topics: Instead of generic updates, you learn about The Great Data Crisis of 2025, the impact of the December 11 AI Executive Order, or why the Fed’s 2026 Inflation […]