If you talk to anyone in the AI industry or any early adopters outside of it, you'll hear the same thing: there's more work to do than ever before.
AI commoditizes the residue of human expertise, which creates a new reality where the more we automate, the more expert human work there is to do.
Where this is true today
The first concept to understand is the difference between intelligence-heavy and judgment-heavy work. Some also use the terms knowledge vs. wisdom.
Lets take coding for example: writing code is mostly intelligence, knowing what to build next is judgement. In customer service, answering a known question from documentation is intelligence, handling exceptions and determining fair outcomes is judgement.
Yet out of all domains, coding is by far the domain that feels this shift the most. AI has crossed the threshold where it can do most of the intelligence work fully autonomously, and leave the judgement to humans.
As a result, many software engineers are now heavily feeling this effect -- of there being more expert work to do than ever before.
To put it the most simply: if a software engineer works 8 hours a day, 1/8 hours of that day was actually spent making larger judgement-heavy decisions; now it's probably closer to 6/8.
This is part of what's now enabling software companies to deliver products at incredible speeds like never before.
What this means for insurance brokerages
In producer-led industries, including insurance brokerages, this will be a very exciting shift for how these businesses scale and compete.
Brokers are trusted experts who own customer relationships, and unlocking their ability to spend more time building relationships and making strategic decisions instead of doing manual work will increase the quality and revenue per employee.
Our thesis is that the brokerages that double down on hiring more, as now the output per employee is higher, will have an unfair advantage. Historically, there have only been 2 main ways to scale insurance brokerages: hire or acquire. Now, as long as the right AI systems are adopted, there is a 3rd way to scale.