The Next Wave Of AI Agents Won't Live On Your Screen

Brandon Barbello is co-founder and COO of Archetype AI, with over 10 years of product leadership across Fortune 500 and startups.

​Most of us have had a “wow” moment with an AI agent. As agentic AI has advanced, we’ve seen it help complete more of our digital tasks: booking flights, clearing inboxes and drafting memos. The first time an agent did that for me, it felt like “Clarke’s third law“ made real: technology indistinguishable from magic.​

Most AI has lived in the browser. If a digital agent gets something wrong, you catch it, fix it, move on.

But in factories, grids, fleets and roads, a mistake can mean a serious accident or a week of output that never comes back.​

The physical world is also where agentic AI’s greatest promise lies, and we might not be too far off from bringing that reality to fruition. The physical world has been producing the data for years. The missing piece has been intelligence that can interpret those signals and help people act on them.​

Why Almost No One Can Act On Sensor Data

As COO of Archetype AI, I’ve spent the last few years working with operators and engineers on one of the industry’s most persistent challenges: how to turn the enormous volume of data generated by physical assets into intelligence that helps people make better decisions.

The consequences of failing to make that connection are significant. Siemens reports that unplanned downtime accounted for 11% of annual revenue among the world’s 500 largest companies in 2024, roughly $1.4 trillion, up from 8% in 2020.​

Recovery is also getting slower: The average restart time after a stoppage was 81 minutes, up from 49 in 2019. An idle line at a major automotive plant can cost up to $2.3 million per hour of downtime.​

After a decade of industrial IoT investment, both numbers moved in the wrong direction. The problem isn’t a lack of data. It’s that much of that data remains difficult to interpret and act on before something goes wrong.