In most organizations the order is reversed. Select your AI platform. Build your use case. Tell people to use it. I wonder why recruitment is stagnant.
I advocate reversing it completely. First, evaluate your own culture. Strengthen your weak points. Then select and deploy AI tools that have the foundation to actually support them.
The data supports this: Organizations that invest in change management are 1.6 times more likely to report that their AI initiatives are exceeding expectations. (Deloitte). That’s not a slight improvement. That’s a radically different result.
Three approaches to AI adoption
From my experience working with organizations across a variety of industries, I believe there are three approaches to AI adoption.
Technology first. This is the default. Choose a platform, build your use case, and deploy it to your users. This is how most organizations approach AI because it feels tangible and action-oriented. Also, Scale failure rate 74% (BCG, 2024). That should tell you something.
parallel track. Pursuing technology and culture at the same time. It’s better than technology-first, but in reality, the technology track almost always outweighs the culture efforts. You’ll be introducing tools to organizations that are “committed” to cultural readiness but aren’t actually achieving it.
Culture first. Assess and strengthen your company culture before choosing and implementing AI. This is an approach that produces dramatically different results. Because by the time you deploy the technology, your organization is ready for it.
What Culture First Actually Means
This is not abstract. It’s a step-by-step approach that I’ve seen organizations from midsize businesses to large government agencies take on.
Phase 1: Assess your current culture using validated tools. This is not a SurveyMonkey poll. It’s not a listening tour where everyone says what the leader wants to hear. A rigorous diagnostic that highlights what’s really going on in your culture, including psychological safety levels, learning orientations, collaboration patterns, resistance to change, leadership dynamics, and more. Your next decision depends on your data, so you need reliable data.
Phase 2: Address cultural gaps that impede AI adoption. Conduct targeted cultural development work based on what the evaluation reveals. Where psychological safety is low, build it through changes in leader behavior, structural changes in how you deal with failure, and clear norms around learning. If cross-functional collaboration is weak, redesign how your team works together before asking them to collaborate on your AI initiative.
Phase 3: Select and pilot AI tools with culturally prepared teams. Let’s start where the culture is strongest. Choose the teams and features that are most ready for initial pilot. This creates early wins and increases trust in the organization. Success breeds success, but only if the first attempt is actually successful.
Phase 4: Scale with culturally aligned change management. This is not a uniform development. Adapt your implementation approach based on what you learn about your company’s culture. Teams with higher psychological safety can handle more ambiguous situations and faster timelines. Teams that are still preparing culturally need more support and a longer runway.
Four elements that make culture possible
Organizations that are successful in scaling AI share four cultural characteristics. I’m sure because I’ve seen this pattern many times.
Directions for learning. Organizations treat skill development as an ongoing process rather than an event. People are expected to learn and are given the time, resources, and permission to do it. Mistakes are reported to learn, not to be blamed. This is the basics. Without it, the introduction of AI becomes a new obligation that people superficially follow.
Cooperative norms. AI does not respect org chart boundaries. Successful AI implementation requires people from different departments to collaborate in ways that most organizations are not structured to do. Organizations with strong collaboration norms (where cross-functional work is the norm rather than the exception) can adapt to AI faster because collaboration patterns already exist.
Adaptive leadership. A leader who accepts ambiguity. Is there anyone who can say, “I don’t understand” or “Let’s think about it together”? People who demonstrate leadership by asking questions rather than having all the answers. A leader’s job in the AI era is not to know more about the technology than their team. This is because it creates conditions for teams to learn and adapt faster.
Ethical clarity. A common understanding of how AI will and will not be used. It is not a policy document, but a set of living principles that people can apply in practice. When ethical guardrails are clear, people can experiment more safely because they know where the boundaries are. If they are vague, people either freeze or freelance, neither of which yields good results.
pattern
I’ve seen this dynamic play out in dozens of organizations. Companies that invest in cultural readiness before implementing AI consistently outperform those that don’t. Even if technology-first organizations have bigger budgets and more sophisticated tools.
Culturally ready organizations not only adopt AI faster; They employ it better. Their people are more engaged. Their use cases are more creative. The results are more sustainable. Because they’re not fighting their culture all the time.
Culturally rigid organizations follow a depressingly predictable curve. An enthusiastic launch. Adoption rate is low. Frustrated leadership. More training. The adoption rate is still low. Eventually, this effort will be quietly absorbed into “business as usual.” This means that very few people actually use the tools. Sound familiar?
The difference is not in resources or technology. It’s whether or not your organization is the first to make a cultural commitment.
gotham culture approach
This is what we do. We help organizations build AI-enabled cultures, not by adding another layer of technology, but by strengthening the cultural foundations on which everything else depends.
culture dig Provides diagnosis. A detailed, research-based assessment of your organization’s cultural dynamics across aspects critical to AI adoption. Get data, not impressions or anecdotes. data.
cultural mosaic Provides continuous measurements. Culture is not static. When implementing changes, you need to track whether the changes are working. Culture Mosaic allows you to see your progress in real time and adjust your course as needed.
Targeted consulting Turn diagnosis into action. Based on what the data reveals, we work with your leadership team to develop and implement specific cultural changes to enable AI adoption. It’s not typical change management. Interventions designed to fit your culture, gaps, and goals.
If you’ve read this far, you’re probably thinking one of two things. “This makes sense and I’d like to learn more” or “This sounds great in theory, but how do I sell it internally?” are both good starting points for the conversation.
Consider the current state of your organization and what you should do about it. Schedule a consultation. One conversation can change the trajectory.
This article is part of our AI and organizational culture content series. Start with our comprehensive guide to get the full picture.
Source: gothamCulture – gothamculture.com
