A lot of conversations about AI jump straight to the end state. Leaders envision a future where the human workforce is focused on higher-order thinking and augmented with agentic capabilities across the enterprise, and where operational costs are much lower. While visionary thinking is not negative, we’re discovering that the transition from today’s version of the organization to a more automated state will require a massive transformation to achieve. Effective, sticky change requires active work and leadership to truly pivot processes, integrate technology, cultivate new skillsets, establish the cultural foundations, reformat the organizational structure, and ramp to new ways of working. Machines can’t steer that kind of change; humans still have to.
That’s where leadership comes in. AI doesn’t change the fundamentals of how organizations move from one state to the next. Leaders still must decide where AI belongs, where it doesn’t, and how they still require human attention and intellect. They have to anchor those decisions in the values of the business and the experience they want employees and customers to have. And if they want to drive true innovation, the company’s culture has to make room for it.
None of this happens on its own. It takes deliberate thinking on culture, clear positioning of the role AI will play for the business, and a way to strategically guide management and front-line employees through the change. That’s the real work of AI adoption, and it starts long before a single tool goes live.
Build the hidden infrastructure of culture
When people talk about company culture, they often describe it as if it’s abstract—values on a wall or an idea they reinforce at company meetings. But culture is what people experience every day. Leaders set the tone in how they make decisions, large and small. Teams follow this lead in how they execute, how they evaluate risk, how they innovate, and how they respond when something doesn’t go as planned.
Being in a state of transformation will magnify whatever culture already exists. If your environment is low on trust or has grown tired of change, AI transformation will make those limitations more visible. If your organization embraces safe risk-taking, creates space for innovation, and respectfully spars on business decisions, you have a more solid platform to work through the messy parts of transformation and adoption.
Culture also shapes how people interpret the opportunity (or threat) of involving more AI in their jobs and functions. This work of establishing or changing culture can’t be handed off to HR or IT; all leadership must set the tone. Leaders need to get comfortable talking about AI in a way that keeps learning and purpose at the center: “Here is what this means for us and the opportunity it creates. Here is where we should be scaling it, and here is where we will still require human judgment to lead.” When employees hear that message firmly and consistently, trust—and even excitement—starts to replace fear.
How to get started:
- In your next all-hands meeting, zoom out from the project list and talk directly about the headwinds and the competitive pressure the business is under. Explain your position on how the business can stay healthy and how AI capabilities will be critical to that position.
- Sit down with your leadership team to name the culture you have and the culture you will need to make room for innovation. Invite their concerns and reservations, and discuss them. Ask your leaders to cascade and reinforce the right values on their teams. When you later see actions rooted in the desired culture, offer visible gratitude as positive reinforcement.
- When making even small day-to-day decisions, explain your thinking each time—even briefly. Connect your decision to your cultural values, providing a blueprint for decisions that will be made when you’re not in the room.
Position AI adoption as a business story
Intentional adoption also requires clear positioning. That means deciding where AI fits in the business and what it’s meant to accomplish. Too often, organizations lead with technology instead of purpose. They spin up experiments because they feel behind, not because they’ve built a clear case for what those experiments are meant to change.
A more deliberate approach starts with the business problem, not the tool. What are the pain points your teams are already wrestling with? Where could faster insight or automation improve performance? And what guardrails will you set so that experimentation feels safe?
Positioning lives at the level of a specific opportunity, not “AI technology in general.” For any given project, the business owner who stands to gain the most should be the one driving and sponsoring it. The case for using AI has to be rooted in each functional leader’s goals, their customers, and their teams—not in an incubator off to the side that is looking for places to land an experiment.
When positioning is clear, employees can see why AI matters. It’s no longer a vague corporate initiative—it’s part of how they get work done. Good positioning also clarifies accountability. The sponsor should be the business leader whose team stands to benefit most from the change, and they should own the outcome—not the AI team or a separate innovation lab. That’s what keeps adoption from stalling after the first few pilots.
How to get started:
- Bestow responsibility on each functional leader in your organization and ask them to consider ways AI or automation could add value. Ask them to build a business case to quantify the impact for the most compelling use cases. Sponsor those that stand to move the needle the most, and be clear about who will own the outcome.
- If you’re leading a team, don’t wait for a knock on your office door; carve out the time and brain space to learn, research, and derive a shortlist of AI ideas you believe would help customers or make work more efficient. Treat AI as a new tool in your toolbox, not a side project that only “AI people” get to touch.
Be a champion for change management
Leaders often underestimate the amount of change management AI requires. They assume that once a new tool or policy is launched, people will simply use it. But meaningful adoption doesn’t happen through memos or training alone. It grows through reinforcement, modeling, and iteration.
When the culture already makes room for innovation and the project has a clear business owner and business case, the nature of the effort shifts. Managing change becomes the work of bringing visibility to the value, understanding how the new solution will land for different groups of people, and supporting each group to adjust and adopt.
Change management is what turns intention into behavior. It is how organizations translate a new capability into daily practice. That includes communication—saying what’s changing and why—but it also includes how you listen. If teams feel that their feedback shapes the rollout and they understand “the why,”, they’ll stay engaged. If they don’t, they’ll disengage quietly and go back to the old way of working.
To make change management effective, you have to understand the value the change is meant to unlock—how it makes work faster or better for employees and customers. If your company doesn’t yet have strong change capabilities, someone has to champion them. That might mean creating a small team that owns this discipline, training people in the art and science of change, or both. The point is to treat it as a capability you invest in, not a nice-to-have.
How to get started:
- Pick one AI initiative that is already in motion and invest in the change and adoption. Map the current and future states of the business process, and be specific about how the shift will land for the people involved. Then name a single owner for the change effort and give them the remit to pull in the right partners—whether that’s a small internal team, external specialists, or both—so change management shows up as a real discipline on the project, not a side task.
- Invest to cultivate change management skills in the organization that steers strategic projects—a PMO, a central product team, or other execution arm. This will help fortify upcoming efforts with best-in-class tactics, increasing the likelihood of adoption and return on all investments.
Own the human work ahead
Every organization says it wants to move faster and innovate, and AI raises the stakes. When leaders skip the people side of the work, it hampers their ability to pivot and transform. When they invest in these elements, transformation (and its business impact) comes faster and more naturally.
The wave of AI-powered transformation will sharpen, rather than erase, the fundamentals of leadership. Having clarity of vision, grounding in business value, and working from a healthy cultural foundation have always defined strong organizations. The difference is that now, the consequences of getting those wrong will surface faster and more visibly than ever before. Which is why, even in an AI-driven future, the real challenge remains deeply human—guiding teams with intention, empathy, and trust.
Molly Lebowitz is the Vice President of Industry Consulting at Propeller
January 28, 2026
Intentional AI adoption is a leadership challenge, not just a technology problem
by Molly Lebowitz • AI, Comment
That’s where leadership comes in. AI doesn’t change the fundamentals of how organizations move from one state to the next. Leaders still must decide where AI belongs, where it doesn’t, and how they still require human attention and intellect. They have to anchor those decisions in the values of the business and the experience they want employees and customers to have. And if they want to drive true innovation, the company’s culture has to make room for it.
None of this happens on its own. It takes deliberate thinking on culture, clear positioning of the role AI will play for the business, and a way to strategically guide management and front-line employees through the change. That’s the real work of AI adoption, and it starts long before a single tool goes live.
Build the hidden infrastructure of culture
When people talk about company culture, they often describe it as if it’s abstract—values on a wall or an idea they reinforce at company meetings. But culture is what people experience every day. Leaders set the tone in how they make decisions, large and small. Teams follow this lead in how they execute, how they evaluate risk, how they innovate, and how they respond when something doesn’t go as planned.
Being in a state of transformation will magnify whatever culture already exists. If your environment is low on trust or has grown tired of change, AI transformation will make those limitations more visible. If your organization embraces safe risk-taking, creates space for innovation, and respectfully spars on business decisions, you have a more solid platform to work through the messy parts of transformation and adoption.
Culture also shapes how people interpret the opportunity (or threat) of involving more AI in their jobs and functions. This work of establishing or changing culture can’t be handed off to HR or IT; all leadership must set the tone. Leaders need to get comfortable talking about AI in a way that keeps learning and purpose at the center: “Here is what this means for us and the opportunity it creates. Here is where we should be scaling it, and here is where we will still require human judgment to lead.” When employees hear that message firmly and consistently, trust—and even excitement—starts to replace fear.
How to get started:
Position AI adoption as a business story
Intentional adoption also requires clear positioning. That means deciding where AI fits in the business and what it’s meant to accomplish. Too often, organizations lead with technology instead of purpose. They spin up experiments because they feel behind, not because they’ve built a clear case for what those experiments are meant to change.
A more deliberate approach starts with the business problem, not the tool. What are the pain points your teams are already wrestling with? Where could faster insight or automation improve performance? And what guardrails will you set so that experimentation feels safe?
Positioning lives at the level of a specific opportunity, not “AI technology in general.” For any given project, the business owner who stands to gain the most should be the one driving and sponsoring it. The case for using AI has to be rooted in each functional leader’s goals, their customers, and their teams—not in an incubator off to the side that is looking for places to land an experiment.
When positioning is clear, employees can see why AI matters. It’s no longer a vague corporate initiative—it’s part of how they get work done. Good positioning also clarifies accountability. The sponsor should be the business leader whose team stands to benefit most from the change, and they should own the outcome—not the AI team or a separate innovation lab. That’s what keeps adoption from stalling after the first few pilots.
How to get started:
Be a champion for change management
Leaders often underestimate the amount of change management AI requires. They assume that once a new tool or policy is launched, people will simply use it. But meaningful adoption doesn’t happen through memos or training alone. It grows through reinforcement, modeling, and iteration.
When the culture already makes room for innovation and the project has a clear business owner and business case, the nature of the effort shifts. Managing change becomes the work of bringing visibility to the value, understanding how the new solution will land for different groups of people, and supporting each group to adjust and adopt.
Change management is what turns intention into behavior. It is how organizations translate a new capability into daily practice. That includes communication—saying what’s changing and why—but it also includes how you listen. If teams feel that their feedback shapes the rollout and they understand “the why,”, they’ll stay engaged. If they don’t, they’ll disengage quietly and go back to the old way of working.
To make change management effective, you have to understand the value the change is meant to unlock—how it makes work faster or better for employees and customers. If your company doesn’t yet have strong change capabilities, someone has to champion them. That might mean creating a small team that owns this discipline, training people in the art and science of change, or both. The point is to treat it as a capability you invest in, not a nice-to-have.
How to get started:
Own the human work ahead
Every organization says it wants to move faster and innovate, and AI raises the stakes. When leaders skip the people side of the work, it hampers their ability to pivot and transform. When they invest in these elements, transformation (and its business impact) comes faster and more naturally.
The wave of AI-powered transformation will sharpen, rather than erase, the fundamentals of leadership. Having clarity of vision, grounding in business value, and working from a healthy cultural foundation have always defined strong organizations. The difference is that now, the consequences of getting those wrong will surface faster and more visibly than ever before. Which is why, even in an AI-driven future, the real challenge remains deeply human—guiding teams with intention, empathy, and trust.
Molly Lebowitz is the Vice President of Industry Consulting at Propeller