How a culture of experimentation can enable AI adoption
As is often the case with any significant societal or global shift, AI adoption in the workplace has prompted a broad range of responses and varying degrees of distrust and scepticism towards its usefulness and safety. Managing the psychological curve of acceptance towards AI adoption presents one of the biggest challenges to employers wanting to implement the technology in the workplace in 2025.
Why is the relationship between culture and AI so important?
The speed of AI proliferation combined with a low barrier of entry to its use has made AI a particularly unique technology to manage in the workplace. Unlike with many technological advancements, its use isn’t tied to employee seniority, expertise or a single workplace use-case, meaning that it can positively and negatively impact all employees equally.
As a result, we’re seeing and experiencing culture being a cornerstone for AI adoption, perhaps even more so than that of traditional tech adoption. For example, due to the nature of their work, certain charities have progressive cultures built on high levels of trust that have enabled them to be streets ahead on their AI journey than commercial behemoths that are struggling to adapt. This is because the former already have core cultural foundations in place that have enabled them to embrace the AI era, such as having a greater appetite for experimentation and encouraging their employees to innovate.
On the other end of the spectrum sit some financial organisations operating in a highly regulated, risk adverse landscape. Against this backdrop, many are struggling to clarify their position around AI adoption and are having to manage internal pushback from employees who are eager to move forward.
Why AI uncertainty slows adoption in the workplace
At Kin&Co, we’re hearing from our clients that employees are feeling uncertain, uneasy and in many cases, impatient around AI, whilst leaders of organisations are having to think strategically about how and where to enable their teams to use AI, to reap the benefits it could present.
According to government research one in six UK organisations, totalling 432,000, have embraced at least one AI technology – to differing levels of success. In more regulated industries, we are seeing that AI adoption is slow or uneven, often restricted to certain team members and teams. In others, the ‘official stance’ is one of ‘go for it’, but people are scared of ‘productivity cheating’ and the inherent worry that comes with a lack of clarity around not being told explicitly how or when to be using the technology.
As a result, many employees have been left in a grey area, unsure of the degree to which they should be experimenting with AI at work. This is leading to a disconnect between those wanting the business to slow down and set clear parameters and others pushing for acceleration. Furthermore businesses may also be missing out on the potential benefits that come from embracing innovation and encouraging experimentation because their employees are not using AI as much as they could be. Research shows for example, that industries more exposed to AI are predicted to have three times higher growth in revenue per employee.
With AI, the potential for application is so broad that a command and control style of adoption simply won’t work. Instead, organisations need to focus on creating the conditions for experimenting with AI, and establishing the permission and psychological safety around experimenting and failing.
From our conversations with clients, it is clear that organisations that already have a foundational “change-ready culture” that values adaptability, experimentation, and innovation, have found themselves in a much stronger position to embrace AI. This could also be looked at as developing a level of “change fitness” where there is an organisational capability to continuously adapt without depleting human resources.
Why does AI present an ideal test bed for experimentation?
In order to ride the wave of constant transformation, organisations and their employees need to flex, adapt and evolve on a daily basis. Much like adopting AI itself, we believe that building a culture of experimentation is no longer a choice, it’s a mandate.
Chris Carmen, CTO at our partner Indigometrics, believes that GenAI is a unique kind of technology in that it “requires a level of creativity and a growth mindset around experimentation, risk taking and deep learning, to be used effectively.”
Organisations that foster a culture of experimentation are more resilient, more adaptable, more engaged and more likely to innovate, grow and perform better long term. At Kin&Co, we define a Culture of Experimentation as “A place where people are encouraged to act, adapt, play, fail and learn.”
With this in mind, it is easy to see how AI presents an ideal opportunity for experimentation, because of its low cost and low barrier of entry. Employees can quickly and easily try out AI solutions with little or no cost to the company and without an entire digital transformation overhaul, and, because the technology is accessible to all (not just tech teams) it will also benefit from perspectives from all levels of a business. In other words, it enables problem solving from all angles, not just from a tech standpoint.
So how can you help increase AI adoption, through building a culture of experimentation?
1. Set clear parameters for AI experimentation
Organisations with strong cultures of experimentation have leaders who visibly role-model their willingness to test, play, fail and learn, and are comfortable with not having all the answers. They are open to the idea that the way something has always been done is not necessarily the best way to do it today and are willing to let their people find a new way to an answer, where it’s appropriate to do so.
Building a deliberate culture of experimentation with AI doesn’t mean applying it to everything. Leaders in the business need to determine where the red and blue lines sit around AI and establish key parameters for their teams. For instance, outlining areas where experimentation around AI is actively encouraged or where, either for strategic or regulatory reasons, it can’t be used. This will enable employees to feel more confident in exploring the technology independently, without supervision and when they make mistakes, they know it’s in an area that it’s safe to do so.
Setting the boundaries for experimentation is also crucial from a customer perspective. Leaders can determine which elements of the business they want to safeguard and keep truly human, because of the effect that AI might have on customer experience. This also helps to build employee trust around the technology in knowing that many of their tasks and responsibilities remain with them, and AI is seen as additive, rather than as a replacement.
2. Treat AI like a new teammate
Leaders need to feel comfortable standing up and giving explicit permission for their people to experiment with, and that includes failing whilst trying AI tools. This needs to be true for leadership throughout the organisation, not just at the very top. It’s no use having a CEO say ‘try AI’ if your immediate line manager wants things done in the way they always have been.
For this to then feel true for team members on the ground, AI needs to be treated as a new teammate and viewed as something additional rather than as a replacement. In the same way you would train a new person and work alongside them, businesses can help their employees to see AI as a colleague rather than a threat – learning to give it feedback, challenge it, stretch its capabilities and be willing for it not to work, just like you would with a new starter.
This explicit experimentation approach then builds trust in both directions, helping team members trust their leaders in removing some of the ‘will it take our jobs’ fear that exists around AI, and enables leaders to trust their team members experimenting with AI in feeling confident that tasks won’t be completed without appropriate supervision and double checking, limiting the risk for error.
3. Identify the specific mindsets & behaviours for AI experimentation
From his work in the space Chris Carmen argues that until organisations start working on employee mindsets, the impact of AI capability training, for example, will be limited.
Chris says: “By starting to define the behaviours that you’re looking for at a company, you start to define what good looks like. Once you define what good looks like, you can then figure out where each one of your employees is in relation to that good and help them move as quickly as possible along that curve, because that’s what’s going to drive your AI adoption.”
Setting clear behavioural expectations around AI, will mean that AI adoption becomes less a choice, and more a part of the expected ways of working. These expectations will not only help employees to know how they’re meant to show up around AI and make them less worried that they’re doing the wrong thing, which is often the case with experimentation, but it will also help to dispel any fears about being left behind the AI curve.
Empower your employees
In many transformations, employees are not ready for change, but AI is different. Employee readiness and familiarity are already high, which gives business leaders the permission to act and to set out clear behavioural parameters, and then create the space for people to experiment with those behaviours For example, Canva recently gave its staff a week off to experiment with AI, citing that: ‘Learning can be deprioritised when work is busy.’
As highlighted in our thriving cultures models, in a strong culture of experimentation and agility, teams feel empowered to regularly test new approaches, learn quickly from outcomes, and iterate based on data, reducing reliance on slow, top-down decision making. Experimentation also drives engagement by putting change in the hands of teams, and greater engagement leads to a 21% increase in profitability.
Employees at organisations unable to embrace experimentation are unlikely to put new behaviours into practice due to a fear of failure, which may be further prevented by organisational policies, processes, and rigid structures. None of which is conducive to the successful adoption to emerging technologies such as AI.
Creating a culture of experimentation where AI can thrive will be crucial in the next 12 months. Want to learn more? Download our Thriving Cultures™ whitepaper today designed to offer leaders a more practical, intuitive and hopeful vision of what is possible when your people thrive.