This came off the back of a comment with @Mira Bradshaw and seeing her experience in real time. Thank you for the inspiration, Mira, and April version of Mira too. I forgot to mention that @Don Roy - Do you use loops? 🔁 post, further compounded to me writing this post too. Attached is a visual representation of Kolb's Learning Cycle beside the Competency Ladder you climb (and slide back down) as you learn ICM. This demonstrates @Jake Van Clief's ethos of learning by doing, and in turn gaining competency. As Thomas Edison said: "Vision without execution is hallucination." You cannot theorise your way up the ladder, you climb each rung by doing the loop. So if you're finding this hard, awkward, or you're getting stuck: that's normal. It's meant to feel uncomfortable. The more you go through the cycle, the more you progress up the ladder, one rung at a time. I did try to render it in the post but no joy, so attached as image. Here is a link to a graphical representation. Personally I prefer the attached image layout.

​After a high-level meeting with the Head of AI where everything felt possible, I’ve just hit the "Corporate Wall." ​In my last post, I shared how the Head of AI personally backed my IT ticket to enable the D365 MCP server. But now, reality is sinking in. In a global corporation of 14,000 people, even a "Go" from the top has to pass through the gatekeepers of the legacy systems. ​The "Soon" Trap ​The manager responsible for our ERP system is hesitant. The argument? We are eventually moving from D365 to Business Central. ​But here’s the kicker: The rollout happens in waves, and our turn isn't until Q2 2027. ​That’s over a year away. In corporate time, "soon" is a dangerous word. It’s an excuse to stay in "manual hell" for another 12+ months. I refuse to let my team—and teams across 72 countries—waste thousands of hours clicking buttons that an AI agent could handle today. ​90% Done: The Solution is Already Built ​What makes this frustration even more real is that I’ve already done 90% of the work in Copilot Cowork. The logic is mapped, the agent is configured, and it’s ready for its first dry run. The only thing standing between a 4-day manual task and a 1-hour automated process is a single IT toggle. We aren't waiting for development; we are just waiting for a door to be unlocked. ​Expanding the Frontline ​The ERP manager sees this as a "finance request." He doesn't yet realize that I’m not just building a script; I’m building a global AI infrastructure for Finance. ​I’ve already started reaching out to colleagues in other countries to build a coalition. The feedback from Germany was clear: ​"It's definitely a really cool idea. I hope they react quickly so you can start development. If you need any support or testers, feel free to reach out. We'd love to help." ​ The Next Move ​On Monday, I’m meeting with the Head of Finance for the Nordics to get even more weight behind this. Then, I’m going back to the ERP manager. ​My mission is to show him that this isn't an "IT project" that adds to his workload. It’s a transformation that makes the entire organization ready for the future. Whether we use D365 or Business Central, the AI logic and the agentic workflows I’m building now will be the foundation.

Even before the meetings happen, preparation is everything. I’ve been brainstorming how to take the principles we learn here and scale them for a global corporation (14,000 employees, 72 countries). This is the "North Star" for my journey toward becoming an AI Finance Manager. It’s a technical skeleton that ensures security, scalability, and—most importantly—the K.I.S.S. (Keep It Simple Stupid) model for the end-user. The Infrastructure (The Engine Room) Using the folder structure we’ve mastered, I’ve mapped out how a global hub would actually look "under the hood":Plaintext/ ├── CLAUDE.md # The Master Router (Direct OData Logic) ├── /identity # ACCESS CONTROL GATE │ ├── global_admins.md # Full access (Admin level) │ ├── DK.md # Validated users for Denmark │ └── [70+ countries].md # Global validation files ├── /library # THE COMMAND CATALOG │ ├── /global # Standard tools (e.g., /extract_ledger) │ └── /local # Country-specific tools (e.g., /dk_vat_report) ├── /core-logic # THE ENGINE ROOM │ ├── date_parser.md # Translates "MMYY" into OData parameters │ └── odata_templates.md # Master OData entity mapping ├── /countries # LOCAL CONTEXT & AUDIT │ └── /DK │ ├── audit-log.md # Record: Date | User | Command | Status │ └── local_settings.md # Local Legal Entity IDs / Currencies ├── /technical # SYSTEM RELIABILITY │ ├── mcp_config.md # OData read-only parameters │ └── health_check.md # System status triggers └── /schemas # EXCEL TEMPLATES └── ledger_standard.xlsx # Raw data structure The User Journey: From Teams to "Done" I want a user (let’s call him Lars) to get what he needs in under 60 seconds without ever touching a complex ERP menu. 1. The Request: Lars types /dk_vat_report 0126 in Microsoft Teams. 2. The Gatekeeper: The system checks /identity/DK.md. Lars is validated. 3. The Health Check: The system pings the MCP (Model Context Protocol). Connection is green. 4. The Data Pull: The AI uses the date_parser to see that 0126 means January 2026. It reaches directly into D365 via OData. 5. Delivery: The AI maps the data into a clean Excel template and uploads it to the chat. 6. Audit: The system silently logs the action for compliance.

Month-end closing is notoriously the most stressful time of the month in finance. But instead of just survival, I’ve been focusing on transformation. By applying what we are learning here, I just optimized an entire department's service invoicing process by ca. 20% using a simple, intelligent report—and I have the receipts to prove it. The Manual Nightmare (Before) Every month, our service department needs to ensure we have the correct billable amounts ready for customer invoicing. The old process was a classic case of corporate friction: 1. The service team pulled raw, unstructured hour logs from our system. 2. Each Project Manager (PM) had to manually comb through this massive text file. 3. They then had to open a separate, master Excel sheet called "Signed", manually input the hours for their specific projects, and calculate the totals based on hourly or daily rates. 4. Only after this grueling process was finished could I step in to actually generate and send the invoices. Step 1: The Bottom-Up Approach (Change Management) Instead of trying to push a top-down solution, I went straight to the operational frontline. I sat down with a single PM to map out the workflow and identify the lowest-hanging fruit. We realized that simply transforming the raw data dump into a clean, intuitive overview report would save them massive amounts of time. I built a prototype, tested it with that exact PM to make sure it met their real-world needs, and then booked a meeting with the department manager. Step 2: The "Concierge MVP" Strategy When I met with the manager to show her the first draft, she loved it. Her deep experience allowed her to provide invaluable feedback to refine the report even further. To get this live immediately without waiting for complex integrations, we deployed what is known as a "Concierge MVP" via Microsoft Copilot Cowork: - The Current Workflow: Right now, the manager simply emails the raw data export directly to me. - The AI Skill: I run it through my custom Copilot Cowork skill, which instantly structures the data, cleans it up, and generates the overview report. - The Delivery: I send the clean report back to her team.

Sometimes you just have to stop talking, take action, and prove what is possible. Tonight, I’m sitting with my laptop finishing up a PowerPoint presentation. Tomorrow, I am hosting a Global Copilot Cowork Workshop for colleagues across multiple departments and countries. Looking back, things have moved at absolute lightning speed. The Timeline - March: I started experimenting with Claude Code and joined this amazing community shortly after. - May 4th: I had a high-level meeting with our global Head of AI. He gave me the green light to use Copilot Cowork since Claude Code isn't corporate-approved yet. - Today: I'm teaching others how to do what I just did. In just a short month and a half, I’ve built several custom skills focused entirely on our hardest finance bottlenecks. And the timing couldn't have been better. The Ultimate Test: A Compressed Month-End We were recently acquired, which meant our standard 6-day month-end closing window was aggressively cut down to just 4 days. Normally, this would mean pure panic and stress. We had never missed a deadline, but we always had to fight for it. During the May closing, I put my new Copilot skills to work. The result? It went so smoothly that on day 4, my boss literally looked at me and asked: "Did we forget something?" Normally, she works grueling hours, staying up late to log back on after putting her kids to bed. Not this time. I only had to work a truly long day on Day 1. Day 2 was slightly extended, and Days 3 and 4 were completely normal, quiet, peaceful workdays. We smashed a compressed timeline with zero stress. Tomorrow's Mission: Giving Back Tomorrow, I'm stepping up to the whiteboard. I'm running a workshop to show people what this tech can actually do. We are going to build a "light" finance skill together from scratch. The core lesson is simple but powerful: How to feed raw data into an AI agent and get a perfectly formatted, HQ-compliant report out the other side. What the Future Holds