The technology industry has spent years asking what AI can do. Business is about to discover what happens when it actually has to depend on it.

That is the shift running through this issue. AI and automation are moving beyond demonstrations and controlled pilots into the machinery of everyday business – making decisions, forecasting demand, reshaping workflows and increasingly interacting with the physical world.

The challenge now is not proving the technology works. It is making it work at scale.

Pebl illustrates that challenge particularly clearly. For more than a decade, its global employment business depended on specialists navigating the messy realities of international hiring: different payroll systems, benefits, contracts, employment rules and compliance requirements. That expertise created trust, but it also created a scaling problem. Every complicated question could pull another specialist into another email chain.

Bill Tanner Editor

Pebl’ s AI shift is an attempt to break that equation. Rather than simply automating administrative tasks, the company is trying to encode specialist knowledge into a platform that can resolve routine issues immediately, anticipate what users need next and recognise when complexity or risk requires human intervention. The ambition is significant: scale the expertise without diluting the judgement that made it valuable( p14).

At Unilever, AI, digital twins and real-time data are becoming part of the infrastructure supporting one of the world’ s biggest supply chains. With consumer demand capable of changing within hours, predicting what happens next is increasingly more valuable than responding efficiently to what has already happened( p24).

Physical AI pushes that transformation further. Faraday Future and AIxC’ s RoboShare strategy asks whether robotics’ next breakthrough will come not simply from more capable machines, but easier access to them. Shared, rentable robots could alter the economics of adoption( p35).

Then there are the people. iCIMS research suggests employees are increasingly developing AI capabilities themselves while corporate training struggles to keep pace. Businesses may be investing heavily in technology while underinvesting in those expected to extract value from it( p18).

Together, these stories reveal technology becoming less visible precisely as it becomes more important.

Perhaps that is the real measure of technological maturity. Not the announcement, demonstration or pilot, but the moment innovation disappears into everyday operations.

The most important technology eventually stops looking like technology.

It simply becomes how business gets done.

Have an innovative month.

Make stuff happen. www. intelligentcio. com

INTELLIGENT CIO NORTH AMERICA

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