What Does It Take to Become an AI-First Organization?

TL;DR: We ran our first internal AI Hackathon, building two AI-powered tools on real internal operations in 14 hours. Our teams from TMC/TMG and one from PowerApps911 built a Statement of Work (SOW) and Functional Requirements Document (FRD) Agent and a Time Tracking Reconciliation App, using only Microsoft Power Platform, Copilot Studio, Dataverse, and AI Builder. Every build had to pass a six-point Definition of Done before moving to beta, and both are now running in beta against real internal work. The format is simple enough for any organization to copy: pick a real problem, set a fixed window, and hold the result to a real standard.

What Is an AI-Ready Organization, and How Do You Become One?

Becoming AI-ready starts with a decision: build on your own operations first, before offering anything to a client.

We do not bring technology to clients we have not built ourselves.

That rule shaped our first internal AI Hackathon. We gave the teams a real operational problem, a fixed two-day window, and a standard the result had to meet before it counted as done.

What Happened at TMC Global's First Internal AI Hackathon?

TMC/TMG and PowerApps911 came together over two focused days to build these tools. Our AI leadership provided technical oversight throughout, with logistics support and executive sponsorship backing the event from day one.

It took fourteen working hours, split across two days, to tackle two real operational problems we deal with every week.

What Did We Actually Build?

The SOW and FRD Agent runs on a proprietary process for turning discovery work into a structured requirements document, built specifically to catch scope gaps before they become mid-project problems. The process itself stays internal. The results are measurable: faster turnaround from discovery to signed-off scope, and fewer disputes once a project is underway.

The Time Tracking Reconciliation App replaces a manual process every services business knows well: time entered across multiple systems, cross-referenced by hand, cleaned up before payroll and billing move forward. Our version runs that reconciliation through a proprietary workflow built for services businesses on the Microsoft stack, cutting manual review time and giving project managers a clean, ready-to-approve view.

Both tools sit entirely inside the Microsoft stack: Power Platform, Power Automate, Dataverse, AI Builder, Copilot Studio, SharePoint, and Teams, integrated with Business Central and Great Plains. Nothing outside the ecosystem.

Part of what made the pace possible was building on Power Platform's newer Code Apps capability, which let the teams build a real React and TypeScript front end inside the Power Platform ecosystem instead of working around its limits. That meant testing an idea, seeing it work, and adjusting it, all in minutes instead of days.

How Do You Know an AI Tool Is Actually Ready?

We measure every hackathon build against our own Definition of Done and the GIFT framework. A tool has to:

• Solve the actual job it was built for
• Hold the scope that was agreed upfront
• Fail safely when something unexpected comes in
• Deploy cleanly into the target environment
• Be tested with documented before-and-after metrics
• Be documented well enough that someone outside the build team can run and support it

All six have to be true. If they are not, it goes to the next hackathon. No half credit.

Both tools are currently in beta against real internal work, not demo data or a sandboxed example. Our own consultants are running them against actual internal engagements, and testing continues before either one rolls out organization-wide.

How Can Any Business Build the Same AI-Ready Culture?

The format behind our hackathon doesn't require a large team or a big budget to copy. It requires a real problem and a real standard.

• Start with a problem your team already deals with every week, not a hypothetical one
• Set the deadline before the team starts building, not after
• Define what counts as done before anyone touches a keyboard
• Test the result against real internal work before anyone else is asked to trust it

Any business already running on Microsoft's stack has access to the same tools we used here: Power Platform, Copilot Studio, Dataverse, and AI Builder. The tooling was never the hard part. The discipline is, and that's exactly what we help other organizations build.

The biggest lesson we took away wasn't about the tools themselves. It was the shift in what we now believe is possible on a two-day timeline, with tools already available to us. That shift is what turns a one-time event into a habit, and it's the same shift we help other businesses make.

Where Does This Usually Break Down Without a Partner?

The framework above is simple to describe. Holding to it internally, without an outside structure forcing the issue, is where most organizations lose the thread.

• The "real problem" quietly becomes a safe, low-stakes pilot instead of something that actually matters to the business
• The deadline slips the first time the team gets pulled back onto client work or a fire drill
• "Done" gets redefined partway through so the project can be called a success without ever shipping
• A tool goes straight into a client's hands without ever being tested against the business's own operations first

None of these are failures of ambition. They happen because internal teams rarely have someone whose job is to hold the deadline, enforce the standard, and say no when a result doesn't meet it. That's the part our AI Activation Launchpad is built to provide.

How Can a Business Bring This Same Process In-House?

The tools described here went through the same AI workflow discipline we apply to every client engagement.

We're not pointing to a roadmap item. We're showing what we built, how we tested it, and what it takes to do it right.

The framework above works at the team level: pick a problem, build it, test it, hold the standard. Getting AI into an organization in the first place also takes buy-in and a plan at the leadership level. If you're an executive or CFO looking for that broader AI readiness roadmap, our joint session with SingerLewak, AI Effectiveness Roadmap for CFOs and Executives, covers that side of the work.

Frequently Asked Questions

Key Takeaways

• Becoming AI-ready starts with building and testing on your own operations first.
• TMC/TMG and PowerApps911 came together to deliver two production-track AI tools in 14 working hours across two days.
• Every tool must pass a six-point Definition of Done and the GIFT framework before it moves to beta.
• Both tools run entirely on the Microsoft stack: Power Platform, Copilot Studio, Dataverse, and AI Builder.
• Beta testing happens against real internal work, not demo data or sandboxed examples.
• Any business running on Microsoft's stack can copy this same format: a real problem, a fixed window, and a real standard for done.
• Most internal attempts stall on enforcement, not ambition: deadlines slip, standards get redefined, or testing gets skipped.
• The real shift was realizing what's possible on a two-day timeline, not just the two tools themselves.