Artificial intelligence has moved from experimentation to expectation. Boards pressure CEOs on ROI. CEO initiates enterprise deployment. Leaders invest in tools, platforms, and governance. However, recruitment remains stagnant. Workarounds have spread. Increased risk. Values are delayed.
Technology rarely fails. The details are in the recruitment design. Many organizations treat A.I. As an IT deployment or standard change effort. Tool gets approval. Policies are cyclical. Training will begin. What is missing is the rigor that leaders apply to external products. Employees receive tools with no clear value proposition. Managers face delivery pressure if they don’t add capacity. Governance prioritizes control over learning.
The results are predictable. Hesitation increases. Burnout increases. Especially fragments of execution in the middle of the organization.
Dana, a vice president leading AI enablement at a global B2B services company, has seen this firsthand. The mission was clear. It means deploying approved AI tools across the company. Achieve marketing, sales, and customer success in less than 8 months. Cooperation between legal affairs and public relations. Training sessions and a dashboard to track usage have been launched.
On paper, the deployment appeared disciplined. The usage dashboard showed login, prompt, and license activity. In fact, the team struggled to use the tool in live client work. Approved platforms add steps, limit output, or don’t match your actual workflow. Under pressure to deliver, some teams took a quick test and moved on. Some superficially followed. Many companies moved core tasks to external tools that felt faster and more flexible, while using approved systems only for registering activities.
Dana encountered what we call the “obligation trap.” Leaders command the AI from the top. Work is in progress to make it usable.
“There were no resistance issues,” Dana recalled. “There was a design problem.”
Her experience reflects what she has seen in AI implementation workshops across organizations and with executives and senior leaders. Teams return to familiar workflows. Learning time is lost as daily performance goals take precedence over competency building. Worse, leaders often label this gap as resistance to AI rather than identifying and solving the underlying problem.
Through our advisory work and research, jenny As an executive coach and learning and development expert, gnome As AI strategists, we believe three practices separate organizations that are able to scale AI within their organizations from those that are stagnant.
Reframing “resistance” as a workflow problem
Leaders often label hesitation as a mindset problem. In reality, hesitation reflects risk. Employees become disengaged when expectations are met, when results feel unachievable, or when policies are unclear. Under pressure of delivery, people choose speed and safety. Adoption stagnates when AI complicates rather than simplifies execution.
middle management Absorb the burden. They must deliver incentives, capabilities, and decision-making rights faster, guide new behaviors, manage risk, and preserve uncertainty without changing incentives, capabilities, and decision-making rights. Recruitment is disrupted where pressure is concentrated. The problem isn’t motivation. This is an internal product-market fit issue.
Internal product-market fit exists when a tool solves a real-world workflow problem well enough that teams continue to use it under real-world constraints. This insight changed the course of events for Dana. She stopped driving compliance and paused implementation to focus on solving issues facing internal teams.
What leaders can do:
- Diagnose hesitation: Identify where trust breaks down. Unreliable output. Revision path is unclear. Approval is slow. Please correct the friction before use.
- Let’s start small: Focus on one workflow, one outcome, and one team learning together.
- Name your fear. Directly addresses unemployment concerns. Clarify what remains human-driven and how AI fits into workforce planning. psychological safety Generate engagement.
- Relieve pressure: Observe study time. Resetting and implementing goals remains at a superficial level.
When leaders treat resistance as a design signal, adoption moves from compliance to progress.
Treat employees like “customer zero”
Successful leaders stop deploying AI and start selling it internally. strong Introduction of AI Follow a different playbook. leader Anchor change Focus on outcomes, redesign workflows, engage employees as co-creators, and invest in learning as a core competency. Dana brought in leaders from the platform team, product marketing, communications, and departments. Teams receive a clear value proposition related to actual workflow frictions, not feature lists or policy decks. Trust increases when people understand how outcomes are formed, how risks are managed, and that human judgment still matters.
Early wins rarely show up as benefits. These manifest in shorter cycles, improved quality of work, fewer errors, and less rework. tool get traction If you want to simplify your work.
Dana ran a short discovery sprint for marketing, sales, and operations. She stopped asking if the team used the tool. She asked where work is slowing down, where rework is accumulating, and where judgment is most important.
What leaders can do:
- Achievement anchor: Define what feels faster, easier, or more reliable.
- Build trust early. Set clear governance and human-involved guardrails.
- Rethink your workflow. Integrate AI into existing systems and moments of execution.
- Co-creation with employees: Involve your team in discovery and testing.
- Treat learning as a central task. Give yourself time to experiment and build confidence.
When leaders treat employees as “customer zero,” adoption moves from compliance to lasting change.
Protect the middle layer and unlock learning
AI deployments are almost always halted. Managers need to change the way they work while achieving the same goals. Meanwhile, the manager drives most of the cars team engagement While shouldering the heaviest burden. When learning and delivering compete, delivering wins.
capable leader Redesign these conditions. They reset expectations to protect study time. Reward experiments that reduce risk over time. Before scaling, they ask two questions. Does this eliminate friction in real-world workflows? Do people trust it enough to use it?
Danna acted on this insight. She gave managers protected time to test their workflows and share their results. Early wins resulted in a simple playbook. Only proven practices are scaled. Managers have moved from fighting fires to coaching. Governance has moved from gatekeeping to enablement.
Dana narrowed her focus instead of broadening it. The team submitted a real-world workflow test. Dana chose only those that had a clear impact and ran them end-to-end, protecting the entire quarter. Some tools have removed friction and earned trust. Others added noise. She evaluated the winners and retired the rest.
What leaders can do:
- Identify what works. Identify teams that are already using AI to reduce friction. Turn those efforts into repeatable practices.
- Reward learning: Recognize managers who build capacity and share insights, not tools.
- Perform disciplined experiments. It requires clear hypotheses, small pilots, and documented learning.
- Hold the bar high: To maintain scale reliability, reward honest reporting of failures.
AI transformation is an organizational design challenge, not an IT deployment. Obligation traps are avoidable. Leaders get away with it when they stop driving adoption and start acquiring it.
About the author
jenny fernandesMBAs are leadership and development consultants and executive coaches who work with senior leaders and their teams to become more adaptable, effective, resilient, and able to navigate change and transformation. You can download her for free PPersonal branding and self-promotion e-book—A practical toolkit designed to empower your team and improve their skills more
Norm Barevis an AI strategist and former senior AI creative strategist at Amazon, specializing in the intersection of generative technology and marketing. a forbes A 30 Under 30 winner, Norm advises leaders on moving beyond AI experimentation to building AI-driven strategic positioning and narrative design.
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