At the beginning of the 20th century, the rise of cars caused disruptions for blacksmiths and carriage-makers, who found it challenging to adapt to new technology. Now, over a hundred years later, artificial intelligence is bringing about a similar transformation. Just as automobiles replaced horse-drawn carriages, today entire industries are being transformed by algorithms.

The question isn’t whether your company will adopt AI, but how. And the answer hinges on one critical factor: culture.

What does an “AI culture” look like?

Creating a culture centered around AI isn’t solely about purchasing new technology or employing machine learning experts. It involves cultivating an environment where experimentation, ongoing learning, and collaboration between humans and AI are integral to the company’s foundation. Here are the steps to kickstart this process:

Model curiosity to dispel fear:

Leaders must advocate for AI, yet real integration happens through grassroots innovation. At CodeSignal, the engineering team not only leverages AI but also actively develops solutions with it. They use tools such as GitHub Copilot for complex code refactoring and fine-tune custom language model agents for internal purposes. Moreover, AI involvement extends beyond engineering; for example, our marketing team uses Claude to brainstorm campaign ideas and employs Gemini to test different messaging strategies.

The key? Leaders must model curiosity. Share your own AI experiments — and failures — with your team. CodeSignal has a Slack channel dedicated to experimentation with LLMs, where team members share how they’ve been using AI and what they’re learning (“productivity hacks” are a team favorite).

I have been studying AI technology and building AI-native products for over a decade, but this doesn’t stop me from continuing to learn. I regularly share my learnings, from using the latest LLM models for everything from code writing to email writing to image generation, and debate with my colleagues on how different models perform on complex math challenges.

The point of me doing this is to set the example that incorporating AI into your daily workflow doesn’t have to be intimidating, and in fact it can be quite enjoyable. It also reinforces that we’re all learning this new technology and figuring out how best to use it to do our work together.

Provide access to the right AI tools:

Today, tools like ChatGPT and Midjourney are free, yet many companies still gatekeep access. That’s a big mistake. We give every team member a ChatGPT Teams subscription, with the expectation that they’ll play around with it and even create their own GPTs to augment their workflow. In the past year, our employees have created over 50 custom GPTs that help them draft sales emails, gather market insights, extract data, answer HR questions and more.

Make AI literacy a core expectation — then build on it:

Giving people access to AI tools is necessary, but it’s just the first step. To create a meaningful impact, leaders must pair access to tools with training.

CodeSignal does this by asking every team member to complete AI literacy training, where they build skills in using and interacting with LLMs with hands-on practice. Our team recently finished a “spring training” in generative AI literacy, where everyone at the company (even me!) completed a series of experiential learning courses online and shared our learnings, questions and ah-ha moments in a Slack channel. We boosted motivation for completing the training by setting up a goal of 95% participation — rewarded by cool new swag when we met the goal.

Next, we’re building on this foundation of AI literacy by running an AI hackathon at our next in-person meetup. Here, team members will break into teams based on how they use AI and their depth of knowledge. Some teams will explore using LLMs to draft creative campaigns and set project timelines, for example, while others will be building custom GPTs to automate actual parts of their job. The machine learning experts on our team, meanwhile, will be working on building innovative new AI applications from the ground up.

The goal here is to set the expectation that everyone uses AI, yes — but more than that, to give team members ownership of what they do with it and the freedom to choose which parts of their job can best be complemented by AI.

The stakes have never been higher

For some organizations and teams, adopting AI will be uncomfortable at first. AI tools raise a range of new technical, regulatory and ethical questions. Many employees fear that AI will displace them from their jobs. That discomfort is real — and it deserves our attention.

As leaders, our responsibility is to guide our teams through uncertainty with integrity and transparency by showing how embracing AI can help them become even more impactful in their jobs. I do this by modeling AI use in my everyday work and openly sharing my learnings with my team. This gives team members permission to experiment on their own and helps move them from a mindset of fear to curiosity about how AI can be a partner to them in their jobs.

To return to the analogy of the automotive revolution: We’re teaching our carriage-makers how to build self-driving cars.

If you’re a business leader, ask yourself: Am I modeling what it looks like to learn and take risks? Am I giving my team the tools and training they need to build AI literacy? Am I fostering a culture of exploration and experimentation on my team?

The AI revolution is already here, and the future isn’t going to wait for companies to catch up. Neither should we.