TeamViewer's CEO on the New AI That Fixes Machines Before They Break

Walk into any modern Singapore facility — a hospital with MRI machines, a factory floor with robotic arms, a data centre humming with servers — and the machines are quietly talking to an AI. Telemetry from connected equipment is being analysed in real time to catch failures before they happen, a shift that is turning maintenance from a reactive chore into a predictive discipline. The company betting hardest on this transition is TeamViewer, whose CEO sat down with Techgoondu in Singapore this week to explain where the technology is heading.

TeamViewer, best known for its remote desktop software, has spent the last 18 months "shifting left" — moving from simply connecting people to computers, to selling AI-driven maintenance. More than 600,000 customers use its remote control software to reach devices ranging from PCs to MRI machines, but most have not yet subscribed to the new self-healing AI service. The pitch: record machine telemetry, let AI learn what normal looks like, and resolve issues automatically before a breakdown disrupts operations. A coffee machine tracking temperature, an MRI unit flagging an early fault — the same pattern applies.

Chief executive Oliver Steil is careful, though, to frame this as augmentation rather than replacement. The AI still needs the institutional knowledge of human operators to know what to do, and in sectors like healthcare the connected data must be protected stringently. He also points to a growing skills gap: plenty of senior workers with deep experience, but too few younger ones picking up the trade. TeamViewer's answer is augmented reality — interactive AR glasses that let a new factory worker learn on the job, guided by pre-programmed or AI-assisted instruction, or by an experienced colleague connecting in remotely.

The interview lands at a moment when Singapore is wrestling with the same questions on a national scale. The push to upskill 100,000 finance professionals, compulsory AI training for teachers, and NTUC's worker-first AI agenda all reflect the same underlying reality: the bottleneck in AI adoption is not the models, it is the people and processes around them. Steil's emphasis on adoption through genuine use rather than management mandate resonates here — workers who are forced into new tools develop fatigue, while those who see the value keep using them.

Why it matters for Singapore: With manufacturing and healthcare forming the backbone of the local economy, predictive maintenance is one of the most practical near-term uses of AI for Singapore businesses. The country's chronic shortage of skilled technicians makes AR-assisted training and AI-driven fault detection attractive, not futuristic. The lesson from TeamViewer's pivot is that the winning AI deployments in Singapore will be the ones that work alongside experienced workers — preserving their knowledge, easing the skills gap, and letting the technology earn its place through daily use rather than top-down decree.