The bottleneck used to be the technology. Now it's the people running it.
But you suddenly realize you have no idea how to integrate that speed and sophistication into the decision-making structure your company has used for 30 years.
you're using Jarvis to do work faster, not to do different work.
You don't know how to evaluate them. You don't know which trade-offs matter most in your specific context. You don't trust your own judgment against a system that's demonstrably "smarter" than you in pure analytical capability.
So you do what feels safe: you pick the option that looks most like what you would have done anyway. The familiar choice. The defendable choice.
it threatens the power structure that took two decades to build.
AI agents (systems that don't just answer questions but reason, plan, and act autonomously on your behalf) can now automate roughly 70% of routine professional tasks at twice the speed of humans.4 Yet employee burnout now touches 55% of the US workforce, according to a recent survey of 1,400 US employees.5 Why? Because most companies are using AI to do more of the same work, not to do fundamentally different work. They’re filling the "saved time" with more tasks instead of better thinking.
even if AI development stopped today, with no further improvements, it would still take organizations 5 to 10 years to fully capture the value of the technology we already have.7
The Efficiency Paradox is real: speed without rethinking just creates more work. We’re treating AI as a plug-and-play software upgrade. What it actually demands is a redesign of the work itself.
The senior analysts who’d done the pilot? They were thriving. They treated the AI like junior staff: they delegated outcomes, gave feedback, iterated on prompts, and caught errors. But the mid-level analysts, those with three to five years of experience, struggled. They were using the AI, but their error rates spiked. They were approving AI analyses with subtle mistakes. Missing context that was obvious to the seniors.










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