Every technological revolution has extended human power—steam
extended muscle, electricity extended reach, the internet extended
connectivity. AI extends cognition itself.

Annual AI investment already surpasses hundreds of billions of dollars, and IDC projects enterprise AI spending to exceed $630 billion by 2028, with infrastructure-level capital outlays approaching the $500 billion-per-year mark by 2030¹.

And yet, most organizations have not built the organizational metabolism to absorb such exponential capability. The opportunity is no longer about the next algorithm—it is about how humans and machines co-evolve.

When Henry Ford re-imagined manufacturing, he didn’t invent the automobile; he re-invented the system around it. The assembly line converted invention into scalable production.

Today, most financial institutions resemble Stanley Motor Carriage Co.: highly precise, but rigid and built for an earlier era. Their processes are masterpieces of control, but brittle in change.

The modern “Ford moment” for banking is about shifting from innovation as craft to intelligence as workflow—embedding AI directly into decision-making loops, linking data, people, and processes into a single adaptive system.

Those who succeed will democratize intelligence across the enterprise—creating decision factories as fluid and scalable as Ford’s assembly line of mobility.

The industrial age was defined by machines that multiplied our strength. The AI age is defined by systems that multiply our understanding.

The future is no longer humans versus machines, but humans and machines. Every employee will soon collaborate with digital coworkers—copilots, assistants, and autonomous agents.

But these AI teammates are only as smart as the knowledge they can access—and that knowledge largely resides in people’s heads. To make AI intelligent, we must teach it what we know. To make humans adaptive, we must teach them how to work with AI.

This reciprocal learning cycle is the essence of the dual-engine system:

Humans make machines smarter. Machines make humans smarter. Together, they form collective intelligence.

When people grow, organizations grow. When people stop learning, transformation stops.

Accelerated reskilling and upskilling could deliver $6.5
trillion in additional global GDP by 2030⁵, while Japan alone could unlock more than $1 trillion in new capacity by 2035 through combined AI adoption and workforce upskilling⁶.

AI transformation is, therefore, people transformation. Every time employees learn to co-create with AI, they reclaim time for creativity, analysis, and judgment—the domains of human advantage.

Most corporate learning remains episodic, classroom-based, and disconnected from daily work. Humans, however, learn best in context and in the flow of work.

Synapse—SMBC Group’s AI-native skilling and knowledge platform—brings learning into that flow. It embeds AI tutors across enterprise systems, guiding employees through real tasks while capturing and redistributing tacit knowledge.

Inspired by the Asian mentorship lineage (Sensei → Senpai → Kohai), Synapse creates a living “supermind”—a workforce where human and machine learning continually reinforce each other.

The goal of automation is not replacement—it’s agency. Agency empowers every employee to shape AI rather than fear it.

When people have the tools to configure agents, prompt systems, and codify their expertise, they become innovators. This cultural shift—from passive users to active creators—ensures that technology amplifies human potential rather than displacing it.

By raising Velocity, Capacity, and Capability together, transformation becomes not an event but a living process.