Company cultures will need to change if AI is to thrive in them. After all, powerful AI can ultimately upend work practices and jobs.
An AI-ready culture will enable the right AI approaches to be matched with the right use cases, with minimal resistance, and with surrounding workflows and customer experiences adapted appropriately. Because AI is going to impact work in many little ways before it transforms companies in giant leaps, creating a path for those small successes will enable the big moves later on. And it won’t be possible to supervise all these quick wins from the top, so they’ll rely on a supportive culture to make them occur.
How does it happen? How can you change the culture for AI even with employees fully aware that AI might make many jobs outmoded? There are two key ingredients: pull and structure.
First, pull. It’s quite hard to transform culture through fiat. Because culture isn’t a set of written rules and overt decisions, but rather exists at an implicit level, it can’t just be wiped clean. Rather, you need to develop constituencies pulling change toward them. Then they’ll adapt their outlook and work practices to follow suit.
To create the pull for culture change, cultivate a set of champions who have an innate interest in the topic and a baseline knowledge of what’s possible with AI. Work with them to find the use cases in their various parts of the business, and to outline what can be done through a set of broad, fast, and inexpensive experiments. Importantly, train them to focus first on the customer’s Jobs to be Done (whether the customer is outside your company or internal to it), and to think through the full solution including but also beyond the AI (workflows, customer experience, business model, and more). You want to make sure this campaign is about solving business problems, not adopting technology for its own sake or isolated from the surrounding context.
Second, structure. Culture change makes people uncomfortable, as does AI. So, build a structure that enables it to happen in a routine way. Have a common methodology (like Jobs to be Done) as a way to state problems to solve, provide mechanisms to find challenges particularly suited for AI solutions, and supply a toolkit of approaches that can be rapidly deployed. Make it easy.
You can set the structure for AI adoption within three waves. The initial wave is to enable many quick experiments that not only prototype and test use cases but also uncover other learnings or required capabilities, like how to supply the appropriate data that the AI can learn from. Next, experiments can be more rigorous and seek to measure changes in behaviors, error rates, and other key information. Last, you can roll out more long-term solutions, cognizant that these will always be evolving given the nature of AI and machine learning. The early waves should be fast, cheap, and low-risk. They create the proof points that will generate the pull you’ll rely on.
AI has been around for years, but it usually hasn’t been terribly intelligent, and many people weren’t intimidated. That’s changing fast. As the power of AI systems grows, so do the cultural impediments to their use.
Pull and structure will make the needed AI culture change happen. With that change, you’ll create a key precondition for the technology to flourish.