To determine if a task is automatable, ask three questions: 1) Does it move data between apps? 2) Does it involve complex decisions? 3) Are inputs/outputs consistent? If the answers are yes, no, and yes, it's a prime candidate.

The most critical skill in the age of AI is dedicating time to "work on your job," not just in it. This involves actively observing your daily workflows, identifying repetitive or low-value tasks, and then methodically building AI agents to automate them, thereby creating leverage.

The biggest gains from AI come not from automating steps in an existing process, but from starting with the desired outcome and co-creating a new workflow with AI. This "first principles" approach leverages AI's capabilities far more effectively than piecemeal automation.

Instead of manually preparing a recurring PDF report or slide deck, knowledge workers can build a live dashboard or web application. This "upgrade" transforms a recurring task into a persistent, self-serve asset, increasing value for the end user and creating a competitive advantage.

To find valuable AI use cases, start with projects that save time (efficiency gains). Next, focus on improving the quality of existing outputs. Finally, pursue entirely new capabilities that were previously impossible, creating a roadmap from immediate to transformative value.

A new wave of AI automation is being driven by non-technical staff using agent-based platforms. These knowledge workers are building custom AI solutions for complex business processes, bypassing the need for new software purchases or dedicated engineering resources.

A new role is emerging for employees who identify business inefficiencies and direct AI agents to build custom software to solve them. This 'vibe coder' doesn't need to write code but acts as a problem-finder and agent-manager, creating bespoke internal tools that are superior to off-the-shelf software.

In the Code AGI era, the ability to build software is commoditized. The scarce and highly valuable skill for business operators is now the mindset to proactively identify any operational challenge or workflow friction and reframe it as a problem that can be quickly solved with custom software.

The most significant shift in knowledge work is the new ability for non-engineers to build and use code via AI. Professionals can now create software-based solutions, like automated analytics dashboards, fundamentally changing their job scope from performing tasks to building engines that perform tasks.