The Invisible Interface

內容簡介

I've had the opportunity to watch and experience decades of technology cycles eat industries that thought they were safe. Travel agencies didn't lose to better travel agencies. Bureau of Labor Statistics data shows travel agent employment dropped roughly 70 percent between 2000 and 2021 - not because travelers stopped traveling, but because a software layer got between the agent and the customer and never moved. Banks didn't lose deposits to payment apps. The money stayed exactly where it was. What moved was the transaction moment - the behavioral data, the customer touchpoint, the ability to see and act on what customers do with their money in real time. The relationship didn't disappear. It became invisible to the bank that thought it owned it. AI is doing the same thing. Right now. Across every industry simultaneously. When Klarna reported in February 2024 that their AI assistant was handling the equivalent work of 700 customer service agents - two-thirds of all their customer service interactions - they weren't describing an efficiency gain. They were describing a customer relationship that had moved into a system that learns, compounds, and gets harder to displace every day. When Morgan Stanley announced that 98 percent of its financial advisor teams had adopted its AI assistant, they weren't describing a productivity tool. They were describing a new default - the layer every advisor now works through, every client interaction now flows across, every piece of institutional knowledge now passes through before it reaches a human hand. That's not a technology story. That's a competitive architecture story. The Invisible Interface is about what's at stake in that shift - and what separates the organizations that capture AI's value from the ones that fund it for everyone else. The pattern holds across healthcare, financial services, and enterprise technology: most management teams are building AI strategies around capabilities that will commoditize. The model is not the moat. Data alone is not the moat - not when the models accessing it are available to everyone. The moat is whether your customers and your workforce delegate to your systems by habit - or someone else's. Once that habit forms, it compounds. Quietly. Structurally. In ways that don't show up in a quarterly review until it's too late. This book gives boards and management teams - and those deciding where to place the next bet - the framework to get ahead of three questions most organizations aren't asking yet. Where are your defaults already being set by a competitor you haven't identified? What does it cost when a decision gets routed around you - competitively, operationally, and legally? And what specifically does it take to become the system people trust enough to delegate to? Because delegation without accountability is its own risk. The organizations getting this right aren't just building capable AI systems. They're building systems their boards can govern, their regulators can audit, and their customers can trust when something goes wrong - and it will. That architecture is what creates durable advantage. Everything else is rented. If you're treating AI as a faster version of what you already do, you've already misread the shift.

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詳細資料

詳細資料

• • 語言
  • 英文
  • 裝訂
  • 精裝
• • ISBN
  • 9781646872480
  • 分級
  • 普通級
• • 頁數
  • 0
  • 商品規格
• • 出版地
  • 美國
  • 適讀年齡
  • 全齡適讀
• • 注音
  • 級別

英文書＞財經企管＞企業/經濟＞經營管理

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