AI's Tipping Point at Work
What it looks like and how to lead through it
Do you feel like you have to use AI to keep up with your colleagues?
Do the forces around AI use at work feel more like you are going uphill or downhill?
I’ve seen dozens of AI maturity models that attempt to assess an organization's readiness and capabilities in adopting and utilizing AI. However, I don’t find most of them very helpful because they miss a critical piece. They don’t speak to how it feels to get work done with AI at the employee level. They miss momentum.
Does using AI at work feel more like struggling uphill or accelerating downhill?
The Uphill Struggle
I’ve been thinking a lot about AI in the workplace and how adoption will play out in the coming years. In most organizations, AI is being used in some capacity. At a minimum, early adopters are increasing their productivity, and the company has authorized a handful of AI tools. Leadership may also have made proclamations about AI use and increased investments in programs and resources.
However, outside of a few AI-native tech companies, for most employees, using AI for day-to-day work still feels like struggling uphill. Even as you lean in and want to use the skills you are developing, you face resistance. Forms of resistance include:
Wondering if your manager or client is OK with you using AI
Worrying that AI threatens your value
Being stuck with limited work-approved AI apps while public models race ahead
Progress feels slow. Most knowledge workers can still do their jobs largely the way they have in the past. Even as talk of AI transformations at work accelerates, the uphill tilt creates a sense of safety and complacency. Employees hear the noise, and they know they “should” be doing more with AI to prepare for the change, but they don’t feel the pressure (yet).
The Tipping Point
The tipping point comes when the pressure to use AI to keep up with your colleagues exceeds the personal or institutional barriers to its use.
The tipping point has been reached for many software developers. It’s starting to happen in schools. However, many knowledge work organizations aren’t there yet.
Why is the tipping point important?
Big change rarely starts with a memo. It bubbles up from the middle as the people closest to the work try something, get value, and advocate for it (or peers copy them). When enough teams do that, the hill flips and momentum exceeds resistance.
Signs of the tipping point:
Your manager requests an AI-powered draft
Your colleague asks for your prompts and adapts/improves them
Your conversations shift to “Which AI did you use?” instead of “Is this allowed?”
Once the tipping point is reached, leadership can focus more on clearing obstacles than convincing laggards, and the AI use of you and your peers will accelerate.
Simple Steps to Take Now
Reaching the tipping point isn’t about formal learning programs or broad messaging campaigns. It’s about the day-to-day interactions that happen deep within teams. You can take relatively simple actions to get the forces of change working for AI adoption.
As a Leader
Demo one of your AI workflows in the next team meeting.
Assign a task to your direct reports that asks for AI use.
Set a short-term AI learning target for each direct report.
For Yourself
Show your manager an AI process you already use.
Interview an AI early adopter and adapt one of their prompts to your work.
Schedule a 30-60 minute weekly AI session focused on your core work.
These recommendations make collaborating with AI and talking about use an open expectation for how work will be done. AI becomes more a team member than a tool. Not using AI at all for a task starts to feel like the exception instead of the norm. The tipping point is reached, and pressure spreads to late adopters to engage more fully.
Use Your Agency
You have agency over how AI adoption at work will impact your role, your team, and your broader profession. You can operate in ways that accelerate reaching the tipping point or you can slow (but not stop) the progress. While timelines will differ, the forces for change will flip from resistance and uphill struggle to expectation and downhill acceleration.
Your next step: Pick one uphill task. Collaborate with AI. Share your experience to bring others along. That’s how momentum flips and you stay ahead.
