JPMorgan’s AI rollout shows that successful adoption depends less on the technology itself and more on trust, transparency and ownership. Nick Petschek argues that L&D leaders must treat AI implementation as a cultural change effort, helping people feel safe to experiment, shaping use through feedback and building confidence over time.
The decision by American multinational banking institution JPMorgan to give more than 300,000 employees the option to use advanced AI tools for performance reviews is a bold transition. The bank onboarded 200,000 users to its large language model platform ‘LLM Suite’ within eight months of launching, positioning the technology as a secure, internal productivity hub for day-to-day work.
When considering integrating AI tools, organisations tend to focus on infrastructure, security and vendor selection, and while these are important factors, they aren’t where the transformation truly lives or dies. The harder work is managing the psychological and cultural shift when you roll out powerful new tools to your entire workforce, especially for sensitive tasks like performance reviews.
If you treat rollouts as a tech deployment instead of a change management opportunity, you’re setting yourself up for an expensive failure
Resistance to change, fear of job loss, and lack of trust are the real barriers to the success of AI. If you treat rollouts as a tech deployment instead of a change management opportunity, you’re setting yourself up for an expensive failure. Employees won’t adopt new technology just because it’s available, for effective adoption they must understand why it matters, feel safe to experiment, and see peers succeed with it.
JPMorgan’s large scale roll out offers three critical lessons for L&D leaders navigating an AI transformation:
Lesson 1: AI as a development tool, not a threat
The narrative you set around AI will determine whether employees lean in or pull back. If they see AI as a replacement, their resistance will spike. Whereas if they see it as a tool to expand their capabilities, adoption rates will climb. JPMorgan made a deliberate choice to frame AI as a productivity assistant, not a decision-maker. The firm allows employees to use AI to help draft performance reviews but explicitly prohibits it from making decisions around compensation or bonuses, keeping humans fully accountable.
This distinction signals that AI is here to support judgement, not replace it. The message shifts from “AI is taking over” to “AI is enhancing what you can do.” When employees grasp that AI is an investment in their professional development, not a threat to their job security, you’ll see the best engagement.
Leaders should celebrate early adopters and use their stories to show how building AI proficiency can directly support career growth and future opportunities. By highlighting quick wins and sharing a diverse range of success stories, organisations can make AI adoption feel relevant and achievable for everyone. At the same time, it’s important to acknowledge that some uncertainty is natural and understand that AI-related anxiety is a normal part of learning something new. When experimentation is encouraged and made clear that no one is expected to be an expert overnight, AI becomes less intimidating.
Lesson 2: Ownership drives engagement
JPMorgan took an iterative approach, rolling out LLM Suite across multiple functions, with software developers using it to review code and investment bankers preparing presentations. By including employees in the rollout, organisations can introduce feedback loops that create a sense of ownership, where employees feel like partners in the implementation, not passive recipients.
Anonymous surveys also create space for honest concerns to surface, while open forums encourage shared problem-solving and one-on-one conversations provide more personalised support where it’s needed. By combining weekly pulse surveys with deeper monthly focus groups and regular quarterly reviews, organisations can build a continuous rhythm of listening; one that captures what’s working, identifies what needs adjustment, and keeps employees meaningfully involved at every stage.
When employees see that their feedback leads to tangible changes, they believe the organisation is genuinely making AI work for them, not just rolling it out to check a box.
Lesson 3: Lead with transparency and psychological safety
Trust is the foundation of AI adoption, and trust requires transparency. Leaders must model AI use openly, sharing their own experiences and admitting where AI didn’t work. This kind of transparency builds psychological safety and the belief that experimentation is encouraged, and mistakes are learning opportunities, not career risks.
Addressing job displacement fears with honesty and empathy is also key. Jamie Dimon, JPMorgan’s CEO, has been direct about the impact of AI, stating that it will “change every job,” eliminating some roles and creating others. By sharing concrete examples of how AI can be used as a collaborator rather than a replacement and acknowledging that some roles will evolve dramatically helps to show commitment to reskilling and supporting employees through an AI transition.
Additionally, co-creating AI guidelines through ethics committees that include voices from various levels and departments, rather than imposing rules from the top down, will not only reflect transparency to further build trust, it also fosters greater psychological safety. When employees have a hand in shaping how AI is used, they’re more likely to trust the process and hold themselves accountable.
AI transformation starts and ends with people
Successful AI adoption requires alignment with broader organisational goals like agility, innovation and employee engagement. This means not just measuring adoption rates, but also sentiment, confidence, and perceived value among employees.
AI will continue to evolve, and it is a marathon, not a sprint. Leaders must sustain attention, resources, and momentum throughout the transition, treating AI rollouts as ongoing cultural shifts, not a one-time event.
AI is here to stay, and the question isn’t whether to adopt it, but how to bring your people along for the journey. L&D practitioners who treat AI transformation as a people movement, not just a tech project, will unlock the full potential of these tools.
Nick Petschek is EMEA MD at Kotter
