Ryan Low

Jarvis works for Tony Stark. The Enterprise computer works for the whole ship. That difference matters more than most AI conversations admit. Most of us have a personal AI assistant by now. It's useful. But it works for one person at a time, and what it did yesterday is hard to check, test or repeat. In the first @rotationalio tech talk of our fall series, our CEO Dr. Benjamin Bengfort made the case for the Starship Enterprise model: one shared system that serves the whole crew. It works the same way every time, and it keeps a record of how it reached each answer. That's the thinking behind Endeavor. In 30 minutes, Ben walks through a real supply chain risk example: → Breaking a messy job into small tasks, each of which can be tested on its own → Screening what goes into the model and what comes out, including personal data → Running a free model and a stronger model on the same test cases, side by side → Versioning what goes live, so a model update doesn't quietly break the process Watch Session 1, "Customize, Control & Manage Change for AI Agents": youtu.be/yf9mZ1rpN8I Next up is Session 2, "Put Multimodal AI to Work on Business Docs," on Fri Sept 25, 12:30–1:00 pm ET. A lot of valuable work still runs on PDFs, forms, images and screenshots. Rotational software and ML engineer Patrick Deziel will show how Endeavor takes document processing past one-off extraction and into workflows you can measure, trace and rely on. Registration for Session 2 and the rest of the series is here: rotational.ai/endeavor-for-b… If you're wondering what this could look like for your own team, I'd be glad to talk it through. Send me a message or comment below. You can also see how we run a proof of value: rotational.ai/proof-of-value #EnterpriseAI #AgenticAI #AIGovernance #TaskAutomation #RotationalLabs #AIOperations #EndeavorForBetter youtu.be/yf9mZ1rpN8I

Three signs a first AI workflow is worth putting under control: 1. It repeats every week. 2. A wrong answer is easy to spot. 3. Exceptions can go to a person. Invoice matching, intake triage and exception reports usually qualify. Can't name one? You're still in shadow mode.

Shadow AI often shows up first on the invoice: the same assistant paid for on a card, on a team plan and through a key in a repo. For MSPs, that's the opening. Give each client one place where every AI workflow has an owner, an approved bar, a live version and a rollback.

Most saved AI hours never show up in revenue because they're scattered across ten small uses. Pick one workflow that eats a day a week, like quotes, receipts or month-end. Agree what correct looks like, have the team score a sample, then run it for real. Hours you can point to.

Start with the workflow your team repeats weekly and complains about, not the most impressive one. Have the manager approve what a good result looks like, let the people doing the work score a sample, and go live only once it passes. One workflow, running, beats ten pilots.

If you're an MSP, your clients' AI is about to look like their SaaS did in 2015: a different tool in every department. The MSPs that win will manage every client agent from one place: who owns it, what it was approved to do, which version is live, and how to roll it back.

Shadow AI isn't a discipline problem. It's a speed problem. People reach for unapproved tools when the approved path takes months. Give managers a way to approve one task in days, set the bar it must clear, and watch the workarounds dry up.

MSPs: your clients' AI is already spread across tools you don't manage. The service worth selling isn't another policy doc. It's one platform where every client agent has an owner's sign-off, version history and rollback, running as an endpoint you manage.

Three questions every manager should be able to answer about AI on their team: 1. Which tasks is it actually doing? 2. Who decided what a good result looks like? 3. If a new version is worse, how fast can we go back? If any answer is "not sure", that's your shadow AI.

For IT teams and MSPs, the hard part of AI isn't the first agent. It's the twentieth. What keeps it manageable across every client: one platform where each agent has approved metrics, a scored version history, an API endpoint and a clear rollback path.

For a 50–300 person company that leans on an MSP, a good first AI workflow has three traits: it repeats every week, a wrong answer is easy to spot, and exceptions can go to a person. Invoice matching, intake forms and weekly reports usually qualify. What's yours?

Your team is already using AI. Banning it just moves it out of sight. A better deal for managers: pick one task, approve what a good result looks like, let the people who do the work score real outputs, then put it into production. Control without slowing anyone down.

Most AI pilots don't fail on the model. They fail at sign-off. Before you build, have the person who owns the work approve what a correct output looks like. Have the people who do the job score real outputs. Ship only when it passes, and version it so you can roll back.

Grok Bot, "...Directory had hidden text on their pages addressed to AI assistants, and I ignored it." 🤔

The next service line for MSPs probably isn't another security stack. It's AI workflows built on your clients' real processes. Start with one client and one workflow. rotational.ai/proof-of-value

MSPs: your clients are already asking about AI. Most of you don't have an AI engineering bench, and you don't need one. Partner up, deliver a working AI workflow to a client in 1–2 weeks, and keep the relationship. rotational.ai/proof-of-value

Smaller companies have an AI advantage: you can decide in a week, not a quarter. Choose one workflow, prove it works, then scale. rotational.ai/proof-of-value

No in-house IT team? Your MSP keeps the lights on, but who builds your first AI workflow? Pick one task your team repeats every week, and Rotational Labs will build a working agent with you in 1–2 weeks, free. rotational.ai/proof-of-value