The Road to Predictive Remediation: What Has to Be True Before You Trust AI at Scale

A connected support journey already looks very different today than it once did. Endpoint context arrives from Tanium, the resolution gets validated by a human through a ScreenMeet session, and a structured record writes back into ServiceNow, all built in rather than pieced together after the fact. That entire part of the journey runs in production right now. The next move, the direct flow of that session intelligence into Tanium's Atlas, is where this is all headed. It is not a shipping integration today, and it is worth being precise about that distinction up front.

We asked Brandon Brandon, Field CTO at Tanium, what has to be built, proven, and measured before any organization can trust AI with fleet-wide remediation.

Where AI ends and human judgment begins

Brandon drew the line between AI and people about as cleanly as anyone can. It comes down to pattern versus judgment, and that single distinction decides who handles what.

"AI can deal with known symptoms that have proven fixes. But oftentimes there's a judgment call, or there's an empathy call, or it's something that nobody has ever seen before, and that's really where you need that human in the loop."

That split maps directly onto the daily reality of a busy service desk.

How to reach automated remediation without breaking production

Getting to more automation is not a leap, in Brandon's telling. It is a slow, deliberate build that earns trust one proven fix at a time.

1. Start with high-volume, low-risk fixes that will not touch production systems or force a machine to restart at the end.
2. Prove those fixes resolve accurately over time, with the employee confirming that the problem is genuinely gone.
3. Extend AI's authority to run those specific, proven fixes on its own only once that confidence has been earned.

Business context decides how far that trust can safely travel. A user's role and where they sit in the organization, the services and dependencies riding on their machine, and the potential impact on the people they support all shape whether a fix needs a human first. That context lives almost entirely inside ServiceNow, not in endpoint telemetry or session notes on their own.

What human-validated sessions could unlock for the fleet

Atlas already works from Tanium's real-time endpoint telemetry to detect and remediate across a whole fleet. What it does not have yet, as Brandon described it, is human-validated remediation paired with the anomaly itself, meaning confirmation from an actual session that a given fix genuinely solved the employee's problem.

His example was a Windows update that keeps failing with an error the endpoint tools cannot interpret. The real cause is an unsaved Word document sitting open in the background, which blocks the update until the employee closes the program. Nothing about that situation reads as a machine-detectable error on its own. It is exactly the kind of cause a ScreenMeet session surfaces, because the session already lives inside the ticket natively. Once that pattern gets documented, Atlas could eventually recognize it and flag it proactively across the fleet, before a single new ticket ever gets opened.

Brandon was careful to frame this as something Tanium and ScreenMeet are working toward, not something running in production today. It earns a mention here because it is a real opportunity, not because it already exists.

Why data quality is the real AI objection

We asked Brandon what skepticism he actually hears from the enterprises he works with. His answer was not about whether AI works, because organizations are already watching it succeed in the right use cases. The doubt sits one level down, on the data itself:

"Is my data good enough for AI?"

His point is that a weak recommendation from Now Assist is rarely a model problem at all. The model is best-in-breed, and the thin, memory-based data feeding it is the actual culprit. Notes written after the fact from assumption drift from what really happened, and no amount of prompting fixes inputs that were never accurate to begin with, which is the whole mechanism behind why AI keeps giving confident but wrong answers in IT support. The only real path forward is capturing rich, accurate data from now on, which is exactly what human-in-the-loop sessions native to ServiceNow produce.

Brandon flagged a second, quieter misconception during the same conversation. Flipping on an AI agent does not produce immediate ROI, at least not in an environment built around unpredictable human requests. AI trained on clean, well-documented technical product content pays off fast, because that content went through product managers and QA review. A service desk full of messy human incidents is a very different animal entirely. The organizations seeing outsized returns treat AI like a new employee, teaching it deliberately and earning confidence before handing over any real trust.

Where connected support is headed over the next eighteen months

Brandon's forecast for the better-together vision, twelve to eighteen months out and landing by the end of 2027, is not about speed at all. Three specific shifts stand out clearly from the rest of the noise:

• Fewer tickets get created in the first place, because more issues get fixed before anyone opens one.
• Technician and employee satisfaction climbs as repetitive, low-value work drains out of the daily queue.
• Fleet-wide remediation starts happening before a single person even notices that anything is wrong.

The real headline here is not faster resolution at all, in his framing. It is tickets that never needed to exist in the first place. Brandon also pushed back on the assumption that this kind of automation cuts headcount. The strongest organizations in his view are not eliminating technicians at all. They are redirecting skilled people away from the repetitive fixes nobody wants to touch, toward the work that genuinely needs judgment and empathy.

Fix the data before you buy the workflows

Asked for the one thing a VP should prioritize this quarter, Brandon did not hesitate for a second.

"It's not about, don't buy the workflows. Fix the data first."

The metric worth tracking is not ticket volume or raw resolution speed. It is how much richer and more detailed your resolution records are getting over time. A rising line there is the leading indicator that you are building the data foundation AI actually needs, instead of layering more automation on top of the same thin, memory-based notes that created the problem. Getting started is as simple as one honest question about your own environment: what are you running for remote support today, and is it truly built into Tanium and ServiceNow, or merely connected to them from the outside?

That question is the throughline running under everything covered here. Disconnected remote support does more than frustrate technicians and stall the metrics a VP reports on. It quietly starves every AI investment the enterprise is already paying for. 

Bringing ScreenMeet, Tanium, and ServiceNow together is not about adding one more tool to the stack. It is about capturing the one thing every future AI capability depends on, a true and human-validated record of what actually happened, through a remote support layer that is built in rather than bolted on.