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Lessons learned from a lifetime of doing things the hard way, the first time
Yesterday, the team at Zapier released their new AI fluency rubric.
Yesterday, the team at Zapier released their new AI fluency rubric.
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How using AI as a bridge when doctors aren't available can improve patient-to-doctor communications in real time emergencies
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A few things you’ll notice right off the bat:
There are four levels of using AI across every department. This is a much more nuanced take from simply, “Are you using AI at work?”
Their take on “unacceptable” use is where most non-technical teams stop today. I am consistently seeing teams of sales, marketing, and G&A experts who have not moved beyond a “level one” access of AI use in their workflows (see pyramid below). If you’re wondering what comes next, this is it.
The scale moves from transactional to systems engineering. Across every single vertical, you can tell that Zapier is looking for one key thing: That more of their employees literally re-engineer how work gets done.
As I wrote about yesterday, learning how to think like an engineer might be the most important mindset to adopt of our generation of work. We are about to see more and more companies figure this out.
The ones that don’t will get left behind.
While I appreciated Zapier’s rubric, I couldn’t help but notice a few things missing that I might incorporate in my own Build First version. Here they are:
To me, one of the most interesting side effects of learning AI has not simply been that I learn how to do my own job better — it’s that I learn how to do more jobs better. I was surprised to see so little emphasis on how marketing people could collaborate with say, sales and product people on cross-functional AI-native builds.
Each of us now has a supercomputer in our pockets that can enable us to quickly level up to learn about new things outside our normal spheres of influence. It’s time to start acting like it.
The only area where “teaching” comes up in the rubric is in the transformative block for the people team. This is incredibly short-sighted.
It’s not only a missed opportunity to cross-pollinate good ideas, but it’s a surefire way to continue to facilitated siloed thinking and building. I believe the best teams and organizations in the AI age will be those who actively promoted a shared culture and teaching and building.
I wasn’t surprised to see the transformative aspect of how engineers need to apply AI to their own work. But I was surprised not to see an emphasis and focus on getting engineers to actively seek out ways to build engineering processes into other organizational domains. This to me is another side effect of the missed opportunity of cross-functional AI building.
If the language of engineering is lowering the barrier about who can build, then it makes sense that more of us can think like engineers. But I also wonder - what might it look like to see engineers use AI to apply more business-first systems into the work?
As I’ve been writing about for the past year, there’s a big difference between being AI forward and being AI fluent.
The best way to build fluency is not to shove learning down people’s throats. It’s to develop continued communities of practice, a shared vocabulary for building, and sandbox environments where people feel safe experimenting and working their way up this ladder (in Zapier’s case, from “unacceptable” to “transformative.”)
This means making safe spaces to practice without judgment.
It means rewarding people for sharing back what they learn with others.
It means recognizing that learning is slow, that it is hard, and that true transformative change takes time.
Zapier’s rubric is a good starting point toward getting us thinking in the right direction. But we still have a long way to go.
No matter where you’re at in your own AI learning journey, it’s important to keep building toward what’s next. If you’re ready to learn how your team, company, or organization can move from being AI forward to AI fluent builders, reach out to bethany@buildfirst.ai.
A few things you’ll notice right off the bat:
There are four levels of using AI across every department. This is a much more nuanced take from simply, “Are you using AI at work?”
Their take on “unacceptable” use is where most non-technical teams stop today. I am consistently seeing teams of sales, marketing, and G&A experts who have not moved beyond a “level one” access of AI use in their workflows (see pyramid below). If you’re wondering what comes next, this is it.
The scale moves from transactional to systems engineering. Across every single vertical, you can tell that Zapier is looking for one key thing: That more of their employees literally re-engineer how work gets done.
As I wrote about yesterday, learning how to think like an engineer might be the most important mindset to adopt of our generation of work. We are about to see more and more companies figure this out.
The ones that don’t will get left behind.
While I appreciated Zapier’s rubric, I couldn’t help but notice a few things missing that I might incorporate in my own Build First version. Here they are:
To me, one of the most interesting side effects of learning AI has not simply been that I learn how to do my own job better — it’s that I learn how to do more jobs better. I was surprised to see so little emphasis on how marketing people could collaborate with say, sales and product people on cross-functional AI-native builds.
Each of us now has a supercomputer in our pockets that can enable us to quickly level up to learn about new things outside our normal spheres of influence. It’s time to start acting like it.
The only area where “teaching” comes up in the rubric is in the transformative block for the people team. This is incredibly short-sighted.
It’s not only a missed opportunity to cross-pollinate good ideas, but it’s a surefire way to continue to facilitated siloed thinking and building. I believe the best teams and organizations in the AI age will be those who actively promoted a shared culture and teaching and building.
I wasn’t surprised to see the transformative aspect of how engineers need to apply AI to their own work. But I was surprised not to see an emphasis and focus on getting engineers to actively seek out ways to build engineering processes into other organizational domains. This to me is another side effect of the missed opportunity of cross-functional AI building.
If the language of engineering is lowering the barrier about who can build, then it makes sense that more of us can think like engineers. But I also wonder - what might it look like to see engineers use AI to apply more business-first systems into the work?
As I’ve been writing about for the past year, there’s a big difference between being AI forward and being AI fluent.
The best way to build fluency is not to shove learning down people’s throats. It’s to develop continued communities of practice, a shared vocabulary for building, and sandbox environments where people feel safe experimenting and working their way up this ladder (in Zapier’s case, from “unacceptable” to “transformative.”)
This means making safe spaces to practice without judgment.
It means rewarding people for sharing back what they learn with others.
It means recognizing that learning is slow, that it is hard, and that true transformative change takes time.
Zapier’s rubric is a good starting point toward getting us thinking in the right direction. But we still have a long way to go.
No matter where you’re at in your own AI learning journey, it’s important to keep building toward what’s next. If you’re ready to learn how your team, company, or organization can move from being AI forward to AI fluent builders, reach out to bethany@buildfirst.ai.
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