Over the past few months, I’ve started offering embedded corporate and team trainings through Build First, my new AI learning lab that’s part training program, part product studio.

In these sessions, I work with groups typically of 15-20 people and we progress from the building blocks of design thinking with AI to building mini-apps and GPTs in just three hours. It’s been incredibly powerful to see the paradigm shift in real time for people who have never before used AI suddenly recognize their own power and agency to build digital tools on the Internet.

One thing I’ve noticed: This process often starts with correcting a few misconceptions about AI. Just last week, I received this comment after a session:

“I came into the session having never used AI before and now, with the framework Bethany provided, I feel like I will definitely have the ability to create an app.”

I believe the people and teams who will thrive in the AI age are those who challenge their assumptions and learn how to push past these biases or misconceptions. Here are five takeaways I’ve seen again and again in these trainings that can help shift your mindset around AI adoption.

That may have been true ten years ago (or even two years ago!), but not anymore. Thanks to no-code platforms like Replit, Lovable, Cursor, ChatGPT Codex, and many more, you can build software by talking to your computer like a person (vs. writing code). This completely changes the paradigm around software development. Individuals can build their own, home-cooked software and companies can reconsider their relationship with SaaS platforms.

Where companies once faced a binary choice—buy software off the shelf or task engineers to build it—AI introduces a third path: build it together. Employees at every level can collaborate to create custom tools that fit their needs, without waiting in line for engineering resources or squeezing into an off-the-shelf solution.

Privacy and security are major concerns, but they don’t have to be blockers. Too many organizations are letting the lack of a unified AI policy get in the way of tinkering and experimentation. The cost of waiting is this market is too high: It slows innovation and strips employees of the agency to collaboratively learn how to use these new tools.

Instead of waiting for the perfect system, consider starting with the are hundreds of AI-powered tools you can build that don’t touch personally identifiable information. Even simple mini-apps, when designed well, can unlock major productivity gains while helping teams build fluency with AI systems.

By the way, the most forward-thinking organizations are already finding secure ways to work with sensitive data. Their engineering teams are finding ways to run with locally hosted models, enhanced encryption, or enterprise-grade access. So don’t make the mistake of waiting until you’re ready. Experiment safely now, and build the muscle around building that your organization will need later.

I hear this often when I suggest dedicating three hours to an AI workshop. Yes, that’s a big block of time…but that’s the point. Carving out two to three hours signals to your company that this matters, and even more important, it acknowledges to your colleagues that real learning takes time and facilitation. (And that you encourage it anyway.)

Think back to anything hard you learned in school school. Teachers don’t expect students to get things right on the first try. Most of us need guidance, support, and a peer group to cross that chasm of learning the hard thing. It’s a mistake for leaders to expect their entire organizations to simply “figure out AI” on their own, without a shared vocabulary or dedicated practice time.

By creating micro-trainings where teams and leaders learn together, you normalize a culture of “learning out loud.” The companies that build trust for employees to raise their hands, admit when they’re stuck, and practice together will be the ones that thrive in the AI age.

This mindset is a holdover from the last wave of the internet, when non-engineers had to wait for software to be built for us and then use it in whatever way someone else predetermined. AI changes that. Limiting yourself to out-of-the-box tools means missing the bigger opportunity, such as learning how to connect, adapt, and extend those tools to solve more complex problems.

Yes, tool awareness matters, especially at the rate of change that new tools are entering the market. (I personally commit to trying a new AI tool or workflow every week!) But learning AI tools is a little more complicated than simply rolling out a new CRM and teaching people to fill in form fields with keyword matching. Learning AI is way more nuanced, like learning a language.

Leaders can help their colleagues through this shift by teaching them how to identify the best AI-shaped problems for their organization, experiment with solutions, and weave AI into workflows at the individual, team, and customer/product level.

I get why it can feel that way. If you’ve built decades of expertise, it’s easy to assume no machine could add value to your life’s work. But this is an important assumption for all of us to challenge.

Artificial Intelligence is not an alien force or a separate species, it’s a normal technology, with as much day-to-day impact on our lives as things like electricity, or the internet.

As compute costs drop and chips can store more AI-enabled software on smaller devices, we’ll see an explosion of new wearables and hardware. The people who learn how to get these computers and machines to execute tasks on their behalf will be the ones who thrive in the AI age.

If you’ve spent years building deep knowledge, you’re in a perfect position to repurpose and remix it with AI. And if you’re just starting your career, AI can help you accelerate your learning and build expertise faster. Two sides of the same coin, both pointing to the same truth: AI matters for all of us.

No matter who you are, learning something new takes time, patience, and compassion. The people who adapt fastest are the ones willing to lean into the initial discomfort and push past any pre-conceived notions and biases. Learning how to build with AI is the same.

That’s why the companies that thrive in the AI age will be those that embed AI learning into their workflows and culture. Not once a quarter, but every week, if not every day.

So if you’re a leader looking to cultivate a habit of AI adoption and upskilling across your team, let’s talk about what you and your team can Build First.

Over the past few months, I’ve started offering embedded corporate and team trainings through Build First, my new AI learning lab that’s part training program, part product studio.

In these sessions, I work with groups typically of 15-20 people and we progress from the building blocks of design thinking with AI to building mini-apps and GPTs in just three hours. It’s been incredibly powerful to see the paradigm shift in real time for people who have never before used AI suddenly recognize their own power and agency to build digital tools on the Internet.

One thing I’ve noticed: This process often starts with correcting a few misconceptions about AI. Just last week, I received this comment after a session:

“I came into the session having never used AI before and now, with the framework Bethany provided, I feel like I will definitely have the ability to create an app.”

I believe the people and teams who will thrive in the AI age are those who challenge their assumptions and learn how to push past these biases or misconceptions. Here are five takeaways I’ve seen again and again in these trainings that can help shift your mindset around AI adoption.

That may have been true ten years ago (or even two years ago!), but not anymore. Thanks to no-code platforms like Replit, Lovable, Cursor, ChatGPT Codex, and many more, you can build software by talking to your computer like a person (vs. writing code). This completely changes the paradigm around software development. Individuals can build their own, home-cooked software and companies can reconsider their relationship with SaaS platforms.

Where companies once faced a binary choice—buy software off the shelf or task engineers to build it—AI introduces a third path: build it together. Employees at every level can collaborate to create custom tools that fit their needs, without waiting in line for engineering resources or squeezing into an off-the-shelf solution.

Privacy and security are major concerns, but they don’t have to be blockers. Too many organizations are letting the lack of a unified AI policy get in the way of tinkering and experimentation. The cost of waiting is this market is too high: It slows innovation and strips employees of the agency to collaboratively learn how to use these new tools.

Instead of waiting for the perfect system, consider starting with the are hundreds of AI-powered tools you can build that don’t touch personally identifiable information. Even simple mini-apps, when designed well, can unlock major productivity gains while helping teams build fluency with AI systems.

By the way, the most forward-thinking organizations are already finding secure ways to work with sensitive data. Their engineering teams are finding ways to run with locally hosted models, enhanced encryption, or enterprise-grade access. So don’t make the mistake of waiting until you’re ready. Experiment safely now, and build the muscle around building that your organization will need later.

I hear this often when I suggest dedicating three hours to an AI workshop. Yes, that’s a big block of time…but that’s the point. Carving out two to three hours signals to your company that this matters, and even more important, it acknowledges to your colleagues that real learning takes time and facilitation. (And that you encourage it anyway.)

Think back to anything hard you learned in school school. Teachers don’t expect students to get things right on the first try. Most of us need guidance, support, and a peer group to cross that chasm of learning the hard thing. It’s a mistake for leaders to expect their entire organizations to simply “figure out AI” on their own, without a shared vocabulary or dedicated practice time.

By creating micro-trainings where teams and leaders learn together, you normalize a culture of “learning out loud.” The companies that build trust for employees to raise their hands, admit when they’re stuck, and practice together will be the ones that thrive in the AI age.

This mindset is a holdover from the last wave of the internet, when non-engineers had to wait for software to be built for us and then use it in whatever way someone else predetermined. AI changes that. Limiting yourself to out-of-the-box tools means missing the bigger opportunity, such as learning how to connect, adapt, and extend those tools to solve more complex problems.

Yes, tool awareness matters, especially at the rate of change that new tools are entering the market. (I personally commit to trying a new AI tool or workflow every week!) But learning AI tools is a little more complicated than simply rolling out a new CRM and teaching people to fill in form fields with keyword matching. Learning AI is way more nuanced, like learning a language.

Leaders can help their colleagues through this shift by teaching them how to identify the best AI-shaped problems for their organization, experiment with solutions, and weave AI into workflows at the individual, team, and customer/product level.

I get why it can feel that way. If you’ve built decades of expertise, it’s easy to assume no machine could add value to your life’s work. But this is an important assumption for all of us to challenge.

Artificial Intelligence is not an alien force or a separate species, it’s a normal technology, with as much day-to-day impact on our lives as things like electricity, or the internet.

As compute costs drop and chips can store more AI-enabled software on smaller devices, we’ll see an explosion of new wearables and hardware. The people who learn how to get these computers and machines to execute tasks on their behalf will be the ones who thrive in the AI age.

If you’ve spent years building deep knowledge, you’re in a perfect position to repurpose and remix it with AI. And if you’re just starting your career, AI can help you accelerate your learning and build expertise faster. Two sides of the same coin, both pointing to the same truth: AI matters for all of us.

No matter who you are, learning something new takes time, patience, and compassion. The people who adapt fastest are the ones willing to lean into the initial discomfort and push past any pre-conceived notions and biases. Learning how to build with AI is the same.

That’s why the companies that thrive in the AI age will be those that embed AI learning into their workflows and culture. Not once a quarter, but every week, if not every day.

So if you’re a leader looking to cultivate a habit of AI adoption and upskilling across your team, let’s talk about what you and your team can Build First.