Someone in your organization just built a working “app” in an afternoon with an approved AI tool, and now they want you to deploy it in your Microsoft 365 tenant.
At first glance, it’s impressive. It looks polished and seems to work. Look closer, though, and the gaps appear. The underlying data came from a static, point-in-time export wrapped in HTML and JavaScript. There’s no reliable way to add or update information without rebuilding the entire solution.
So, what do you do?
It’s tempting to feel frustrated. Why didn’t they come to us in IT first?
Microsoft is encouraging broad adoption of Copilot and AI in the workplace. Your colleague may be an early adopter who used AI to turn a long-standing frustration into something tangible. Instead of submitting a traditional IT request, they built a prototype to show you what they needed. It’s not production-ready, but it’s an excellent brief.
We are entering an era in which innovation will increasingly come from outside IT. Department leaders and frontline employees will use AI to tackle problems they understand first-hand. That is part of AI’s promise: giving more people the ability to improve how work gets done.
IT teams, however, are already balancing migrations, application rollouts, updates, backups, security, support, and the rest of the day-to-day work needed to keep the business running.
So, this is where Cloudwell can help you.
We turn promising AI prototypes into secure, reliable Microsoft 365 solutions that are grounded in maintainable data, designed with proper permissions, scalable as your organization evolves, and built to fit your governance model.
We’re being asked to do this more often. Take a look at two recent (intentionally anonymized) examples of what that looked like in practice.
Example one: a dashboard built for a monthly meeting
The first prototype came from someone in an operations role, not IT. Their organization was encouraging employees to use AI, and this person had embraced it. They collected examples of where AI was saving time across the business, fed them into an AI tool, and asked it to create a dashboard for a monthly all-hands meeting.
What arrived was a ZIP file containing a static HTML dashboard. Users could sort and filter examples by category, submitter, and time saved. It looked polished, and the request seemed simple: Can you host this on our intranet?
Underneath, however, every record was frozen at the moment the dashboard was generated. Adding a new example meant returning to the AI tool and recreating the entire output. And because generative AI can produce slightly different results each time, regeneration is not the same as a controlled update.
The concept itself was strong.
Rather than host a static file, we focused on the real need: a place to collect examples, showcase them, and keep the content current. They did not need a fixed page; they needed a sustainable tool on a platform they already owned.
We rebuilt the prototype as a living Microsoft 365 solution. Anyone in the organization could submit a new example, and a review workflow routed each submission for approval before publication.
What they received was not simply a hosted version of what they sent. They sent us an interactive page in a browser; they got a tool the entire organization could use to capture and share where AI was delivering value.
Example two: an interactive office directory
The second prototype arrived around the same time from a different department. The pattern was familiar: built with an AI tool, then handed to IT for deployment.
This one was an interactive floor plan for finding colleagues across multiple offices. A user could choose a location and a person, then see where that person sat. The experience included navigation and zoom controls, an impressive result for something created without IT support.
But everything was hardcoded into a single file. The moment someone changed desks, joined, or left the organization, the solution became outdated. In any sizable organization, that creates an endless maintenance burden with no clear owner.
Deploying it as-is would have been easy and reasonable if the goal were simply to gather feedback on a prototype. A production solution built to last needed a different approach.
We separated the experience from the data, storing people and location information inside the Microsoft 365 environment and driving the page from those records. Updating one record could now update the experience without regenerating the solution or gambling on a different AI output.
The user experience remained familiar, but the underlying product became supportable, maintainable, and ready for long-term ownership.
In both cases, we treated the AI-generated solution as the brief, not the build. We preserved the valuable parts of the idea and rebuilt them on Microsoft 365 foundations the organization could own, secure, govern, and maintain.
What should you do with the next AI-built app?
If this is starting to happen in your organization, our advice is simple: don’t approve it without review, but don’t shut it down either.
Send it to Cloudwell.
We’ll identify the problem your colleague is trying to solve and determine whether the prototype is worth turning into a production solution. If it is, we’ll build it to be secure, data-driven, appropriately permissioned, and governed within your Microsoft 365 tenant.
You can say yes to innovation. We’ll make sure the solution is ready for the business to rely on.
Questions we’re now being asked
Is an AI-built app safe to deploy in our Microsoft 365 tenant?
Not until it has been carefully reviewed. Something that works in a demo is not automatically safe or supportable in production. Common issues include hardcoded data, unpredictable regeneration, unclear permissions, and no maintenance plan. We assess the prototype and tell you whether it is ready to deploy, needs to be rebuilt, or should remain a proof of concept.
How should we manage employees building tools with Copilot and other AI products?
Give employees a clear path to IT rather than a closed door. AI-assisted building is already happening, and motivated people will not always wait for a formal project. A lightweight intake and review process lets IT see what is being created before it goes live while preserving the momentum behind good ideas. We help organizations put that process in place.
How do you turn an AI-generated prototype into a production application?
Start by treating the prototype as the brief, not the finished build. It reveals the user’s need and the desired experience. From there, we rebuild the solution using Microsoft 365 capabilities you already own, connect it to reliable data, apply proper security and governance, and structure it to evolve as the business changes.
Who supports an AI-built tool after the employee who created it moves on?
That is a core risk of hardcoded, AI-generated files: support often depends on the person who created them. When we rebuild these solutions on Microsoft 365 foundations, data can be updated in place, permissions are explicit, and support no longer depends on one individual.
How do we prevent AI-built tools from sprawling across Microsoft 365?
Put a lightweight intake and governance process in place before solutions go live, not a cleanup effort afterward. Review what employees are building, decide which ideas merit support, and apply proper ownership and governance to the solutions that move forward. That keeps useful tools viable and prevents orphaned ones from accumulating.
Should IT let non-technical employees build with AI?
Yes, with a safety net. Some of the best ideas will come from people closest to the problem, including those who have never submitted a formal change request. IT’s role is not to stop that innovation; it is to make sure solutions are reviewed, secure, grounded in reliable data, scalable, governed appropriately, and supportable before the business depends on them. That is where we help.
Been sent an AI-built prototype to deploy in your Microsoft 365 tenant? Contact Cloudwell. We will help you turn it into a secure, maintainable solution.