Measuring False Guidance: The Untracked Risk in AI-Driven Ticket Resolution

Dave Sobel (0:02)

A technician reads the AI's recommended fix. It's plausible, confident, and wrong. And it was planted there on purpose. Whether she catches it is the one number no MSP is tracking. This is The Business of Tech. I'm Dave Sobel.

The newest AI products for IT work aren't being sold as tools, they're being sold as staff.

Start with Shield Technology Partners, an MSP investment and operating platform with 20 partner companies that together serve more than 3,700 businesses. Shield has built an AI operating system called Forge. Its helpdesk agent is named, plainly, the AI engineer, and it takes a ticket from intake to close.

Every figure here is Shield's about Shield. Shield says Forge resolves half of all actionable helpdesk tickets. Of those, 92% involve neither a human approval nor any log technician time. On comparable tickets, it says Forge closes them 25 times as fast as a technician with a median of 16 minutes. At one Shield provider, technicians now spend roughly 70% less time resolving tickets. Half the actionable queue. And for 9 in 10 of those, no technician logged time on it and no person signed off.

Now Microsoft. What Microsoft bills as its biggest copilot update introduces autopilot, which Satya Nadella calls a proactive, long-running agent built for the enterprise. You give it a role and a goal and it keeps working in the background without a new prompt at each step. Each one gets its own governed Entra ID separate from the person who set it up. Built through Microsoft's Foundry platform, it gets a full user account, its own email, its own calendar, its own Teams access, and a place in the org chart. It acts as itself, not on behalf of a user. Employees add instances of it in Teams, and the new stack's word for that is hire.

Autopilot is headed into private preview, not general release. But it lands on an install base of more than 30 million paid Microsoft 365 Copilot seats. Gartner's Larry Cannell told CIO Dive that Microsoft would normally hold a change this size for its Ignite conference. His read, it's really their market to lose. Two companies at two ends of the market. One closes the ticket with nobody signing off. The other gives the agent a mailbox and a line on the org chart. Neither is describing something you pick up and put down. Both are describing someone you work next to. That's what's being installed. The harder question is what it does to the person who's supposed to check it. If you're listening to this and haven't hit follow yet on Apple Podcast Search Business of Tech. Takes five seconds and you'll get the next episode automatically.

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Knowing when an answer is wrong isn't a separate skill. It comes from having produced the answer yourself, many times, the slow way. A technician learns what a bad fix looks like by making bad fixes and cleaning them up. They learn which log line matters by reading a thousand that don't. The check and the work are built by the same repetitions. When the work goes to the machine, the repetitions go with it. The check stops being built even while the output looks better than ever.

IBM surveyed 1,500 chief HR officers and 8,800 employees on exactly this. IBM sells AI and the consulting to deploy it, so read the findings with that in mind.

71% of the HR chiefs named the ability to supervise, validate and override AI output as the workforce's most essential skill. Only 38% of employees agree. And 60% of employees said AI is eroding their skills with critical thinking named most often. So the people setting strategy think checking the machine is the job. Most of the people doing the job don't. And a majority say the capacity underneath it is wearing down. Now look at what happens when checking is the whole job. OpenAI relies on thousands of contractors, hired through AI training firms to review ChachiPD's prompts and answers. They rate them for accuracy and try to keep the model from getting too eager to please. Internal documents obtained by 404 Media tell them not to use AI for any of it, not even grammarly. Some did anyway, and they were fired. OpenAI declined to comment, so it hasn't said why. But the risk is well documented. An AI grading an AI shares its blind spots, and a 2024 study in Nature found that training models indiscriminately on machine-generated content causes defects that can't be undone. The people training the model aren't allowed to hand the checking to a model. The job requires a human who can see what the machine can't. You might reasonably say that's a model lab problem, not a helpdesk. It's the same problem at a smaller scale. The technician who reviews the agent's work is the check, and every ticket that closes without them is one fewer they learn from. In plain terms, the check is made of the work, and the work is what's being handed over. Which means the only way to know whether the check still works is to test it on purpose.