Is AI Going to Screw Our Organizational Culture?

Photo by Redd Francisco on Unsplash

Artificial Intelligence remains a hot topic, and for many, it’s a source of apprehension rather than excitement. Most people still view AI as a threat instead of an opportunity to leverage. It’s not surprising, considering the uncertainty around the rapid development of large language models. Common questions like, “Will I still have a job?” or “What does this mean for my career and my children’s future?” reflect widespread unease.

At an individual level, people are generally keen to learn more. They attend training sessions and knowledge-sharing events to stay informed about the sector’s advancements and grow more familiar with AI. But here are the bigger questions: how does this growing individual interest intersect with organizational culture? Will AI significantly alter how we work together and how we engage with external stakeholders?

We are already witnessing a division between people who have tried to use LLMs just one or two times, and those who are adopting AI to help make their work more effective daily. The anticipation here is that those who manage to integrate AI by using it as an assistant to enhance their tasks are going to be at an advantage. In order to influence the working culture overall, more efforts are needed from organizations themselves in terms of providing the right tools to make adoption happen.

AI won’t change organizational cultures organically unless there is a push from decision-makers to facilitate adoption by giving employees the tools they need. At the moment, this widespread adoption is costly, as rightly put in this LinkedIn article. There are direct costs that normally come to mind, but there are a range of indirect costs to consider as well.

Culture Change Is One of These “Costs”

A survey by IBM done in early 2024 identified that amongst the biggest barriers stated by companies to implement AI systems were the lack of skills and expertise, followed by data complexity and ethical considerations. This goes to show that the AI revolution is something that organizations need to invest in, rather than waiting to be influenced by. Organizational culture, then, needs to be shaped in order to take full advantage of AI.

The message for organizations still wondering if they should invest or not is clear: do it. Otherwise, they’ll be conditioned by partners who have already embedded these systems into their processes with their own ethical considerations. Without investment, organizations risk falling behind, unable to use those systems or judge them on their own terms.

AI won’t change our organizational culture overnight if there isn’t an intent for it to do so. Decision-makers shouldn’t fear AI changing things alone—they should fear not knowing how to steer it in the right direction so that organizations reap full benefits.

Here Are Two Items for Decision-Makers to Have in Mind:

1. AI Is Not Like Social Media

We use comparisons from time to time to explain how technology disrupts our lives. With social media, we didn’t anticipate how much impact it would have on how we communicate, witness others’ lives, or even consume and advertise products. Social media created a new channel—and even a new profession: influencers.

We often say that we should pay attention to AI and regulate it before struggling to impose limits, as we’ve seen with social media. And this is right, especially because AI is not creating a new channel—it’s permeating everything we do online, from search to content creation to social media itself. In fact, AI has been there since the beginning of the internet and is going to evolve in ways we are yet to imagine.

Decision-makers should be aware of this because it implies that not only communication or IT professionals should learn how to use AI tools—everyone needs to.

2. We Need to Embrace a Culture of Experimentation with the Right Judgment

Ethical concerns like bias, transparency, and accountability often dominate discussions about AI adoption—and rightly so. But alongside these considerations, organizations must cultivate a culture of experimentation to truly unlock AI’s potential.

Experimentation allows teams to explore how AI can be integrated into workflows without committing to full-scale adoption before understanding its impact. This means testing tools, refining approaches, and evaluating outcomes iteratively. However, experimentation should be guided by sound judgment—clear goals, ethical boundaries, and a strong focus on real-world needs.

Leaders play a critical role in striking this balance. They must create safe environments where teams can experiment without fear of failure while ensuring those experiments align with the organization’s mission and values. This approach not only encourages innovation but also builds trust in AI systems by demonstrating their potential benefits in a controlled and responsible way.

Organizations that embrace experimentation thoughtfully are more likely to develop AI-driven solutions that are effective, ethical, and aligned with their broader objectives.