English | EN 
When Ray Wang bounds onto a stage, you can feel the caffeine in the air. His signature mix of Silicon Valley hyper-energy, sharp humor, and hard data makes his talks part stand up, part strategy masterclass. And in this keynote — “AI-Native Business Transformation and Decision Velocity” — he painted a vivid picture of the coming Agentic AI revolution. Ray’s message resounded well with an audience that was doubting their readiness for the coming revolution. The question “Is your organization ready for Gen-AI — data in order, AI-native apps development in place?” was answered as follows:
- 9% Locked and Loaded
- 56% Still patching things up
- 30% Barely out of the gate
- 5% Wait…what are we talking about
From Steam Engines to Thinking Machines
Wang began, characteristically, with a quiz: “What do you see?” he asked, showing an image of an old 19th-century machine. The audience guessed: a steam engine, a still, a distillery. “Yes,” he said, “and it was revolutionary in its time. It was supposed to change everything — work, healthcare, supply chains. Sound familiar?” The analogy set the tone. Just as refrigeration — not steam — created the real economic winners of the 19th century, Wang argued that AI’s true winners won’t be the toolmakers like Open AI or Google, but those who apply the technology to transform their business models. “Don’t build the engine,” he said. “Build the ice cream company.”
The Transition from Internet Age to AI Age
Forget the buzzwords about the “fourth industrial revolution,” he told the crowd —“That
was marketing from the World Economic Forum.” The real story, he explained, is a decades-long arc of digitization leading to the AI age. But there’s a twist: while the internet was decentralized and open, the AI era so far is centralized and scarce. “We’re living in centralized scarcity,” Wang said. “A few players, high costs, limited access. That’s not the future we want. We need to get to decentralized abundance.” For that, he argued, the key lies in agents — autonomous, reasoning digital entities that can work, transact, and decide faster than humans. “These agents,” he said, “are already coding, managing finance, matching invoices, optimizing logistics. They are arbitraging your business.” He smiled. “If that doesn’t scare you, it should excite you.”
The Exponential Efficiency Revolution
Wang’s central theme was “decision velocity” — the speed and accuracy of business decisions as a competitive differentiator. “Machines are making hundreds of decisions per second,” he said, “and most organizations can barely make one per week.” He illustrated this with examples from the field. At GE Appliances, every factory worker now uses AI. Marketing campaigns that took six months now take six weeks. Software rewrites that took three months now happen in three days. Product simulations that took a quarter year are done in a week. “That,” Wang said, “is exponential efficiency.” To drive the point home, he invoked the 10x Rule. “If you’re not ten times better, faster, or cheaper — you’ve already lost.” He compared a million-dollar U.S. missile to a $10,000 drone. “Who wins? The drone. Every time.” And he wasn’t joking when he added, “You’re competing with zero. In India, banking transactions are free. Salesforce costs $400 a user per month. Zoho does it for $30. Which one wins?”
Tiny Teams, Huge Impact
If the crowd wasn’t yet convinced that AI changes the economics of scale, Wang pulled up
examples from Silicon Valley. He described “tiny teams” — startups of ten or fifteen people making tens or even hundreds of millions in annual recurring revenue, powered entirely by AI. “Cursor,” he said, “twenty people, $200 million ARR. Midjourney — ten people, $200 million ARR. Bolt — fifteen people, $40 million ARR. This is happening right now.” The takeaway was blunt: “If you’re still measuring productivity in human hours, you’re already behind. The new metric is profit per employee — and it’s exploding.” He paused for effect. “We’ve gone from augmentation to acceleration to automation to agentic — and soon, to autonomous advisory. This isn’t the future. It’s next quarter.”
Refactoring the World
Wang told a story about a friend — a former tech executive — who founded a startup that rewrote an entire ERP system in three months with ten people. Five weeks later, they did the same job in one day, using a mix of AI tools like Claude, Cursor, and Sigma. “Why do you even need apps anymore?” Wang asked. “An agent is just an API with reasoning. It doesn’t need a user interface — it needs an outcome.” He predicted that by early next year, an ERP company will announce a tool that can extract data from SAP, migrate it to a marketplace, and “free companies from the shackles of legacy systems.” He grinned: “You think I’m crazy. That’s fine. In 1999, I was describing the internet, and all anyone saw was Pets.com.”
Decision Velocity: Competing on Speed of Thought
At the core of Wang’s vision lies a single idea: the companies that make decisions fastest — safely and intelligently — will win. He urged leaders to identify their top ten business questions and start automating their answers. “Do I add ten people in finance, or do I automate? Two people in marketing, or one AI agent? Start asking the right questions. That’s how you build decision velocity.” AI, he said, will enable organizations to move from data collection to automated precision decisions — and ultimately to situational awareness, where systems predict and prevent issues before they arise. “We’ve spent decades building dashboards to tell us what happened yesterday,” he said. “Agentic AI tells you what’s about to happen next week — and what to do about it.”
The Human in the Loop
Wang then flipped the automation debate on its head. “Stop asking where you can automate,” he said. “Assume everything is automated. The real question is: where do you still need a human?” He gave examples — regulatory decisions, exceptions requiring empathy, or high-value customer moments. “One day,” he warned, “your customer lifetime value might not be high enough to talk to a human.” The audience laughed nervously. His point: humans will remain essential — but selectively so. “We’ll insert humans for creativity, ethics, and complex judgment. But the rest — machines will do
better, faster, and safer.”
Breaking the Business Model
Agentic AI, he said, will force companies to rethink their business models entirely. “Every time you build a product or service, ask yourself— why not make it an outcome?” He outlined three priorities for organizations: (1) Automate repetitive, high-volume processes; (2) Augment human expertise in areas requiring creativity or empathy; (3) Build data partnerships that unlock new insights and valuations. “Your company’s worth,” he predicted, “won’t just be measured in revenue — but in the value of your data ecosystems.”
Humans Still Matter
Despite his techno-optimism, Wang closed on a surprisingly human note. “Don’t worry,” he said to laughter, “if you’re in your fifties, your job isn’t going away. We still need experience. We just need to pair it with young talent who know the tools.” He called this “pairing wisdom with speed.” The older generation brings pattern recognition and context; the younger brings code and velocity. “Together,” he said, “they build exponential organizations.” His final advice was equal parts pragmatic and poetic: “Operate at machine scale — but protect humanity. The machines don’t care about meaning. That’s our job.” As the applause filled the room, it was clear that Wang’s message had landed. His talk was not just about technology — it was a call to reimagine what business, leadership, and human purpose look like in an AI-native world.
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Please note – This report was created by almost exclusively using available AI-tools except for minor editorial tweaks and some limited lay-out changes.