Your executive team wants artificial intelligence (AI) adoption yesterday. Your budget for training platforms is approved. Your learning modules are ready to launch. But here’s the uncomfortable truth: none of it will stick, and the problem isn’t your training.
Every Chief Learning Officer (CLO) knows this frustration. Employees complete their AI literacy courses, pass their assessments, and then return to their desks, where the old way of working is still faster, easier, and frankly, what their managers expect.
Organizations spend almost $100 billion annually on learning and development, yet most initiatives fail to produce lasting behavioral change. And ATD data shows that, in 2025, organizations are increasing their learning budgets because they recognize that employees can’t keep pace with AI without better support systems.
Yet these investments are often made without the foundational insights into what employees need most to ensure that reskilling and upskilling efforts have the desired impact.
The issue isn’t that your employees can’t, or don’t want to, learn. It’s more complex than that. In an era where organizations and roles of all kinds are being dramatically impacted by AI it’s critical to understand what’s required to ensure that your AI upskilling efforts will stick.
Before you invest another dollar in AI training programs, learning platforms, or upskilling initiatives, you need to audit the five foundational workplace learning systems that determine whether any new skill actually gets adopted and sustained.
My research identifies five factors that determine whether AI upskilling succeeds or fails. Organizations that address these factors can see a 3x improvement in performance.
1. The Learning Climate: Is Curiosity Punished or Rewarded?
Your learning climate refers to the work environment and the extent to which it supports both learning and taking risks.
It’s important for employees to feel safe when experimenting with new approaches. Failure is inevitable. Yet, too often, employees fear failure and its potential ramifications for their jobs and opportunities for advancement.
What good looks like:
Managers openly discuss their own learning curves with new technologies. Failed experiments are expected. Team meetings regularly include “what I learned this week” discussions.
Common failure pattern:
Leadership talks about innovation but punishes any deviation from established processes. Employees complete AI training but stick to the old way because “that’s how we’ve always done it” is the safer choice.
How to fix it:
Before rolling out AI training, determine whether your current culture actually supports learning. Ask employees questions like: “When was the last time you tried something new at work and it didn’t work? What happened?” If they answer: “I got reprimanded” or “nothing—I don’t try new things,” work on the culture is required.
2. Organizational Systems: Are Your processes Built for Learning or Speed?
Even motivated employees can’t apply new AI skills when systems and processes are designed for efficiency over adaptation. If your approval workflows, reporting structures, and operational processes assume everyone will work the same way they did last year, learning won’t transfer.
What good looks like:
Job processes have built-in flexibility for employees to test new approaches. Cross-functional collaboration is structurally supported, not just encouraged. Systems capture and share what works, creating organizational memory.
Common failure pattern:
Your company trains people on AI data analysis tools, but the monthly reporting template hasn’t changed in five years and doesn’t accommodate new insights. Employees either force new learnings into old formats or abandon them entirely.
How to fix it:
Map your core business processes and identify where they conflict with desired new behaviors. Ask questions like: “If an employee wanted to use AI to improve this process, what would keep them from doing so?” Then identify and systematically remove barriers.
3. Job design: Do Employees Have Both the Time and Space to Use New Skills?
A common reason for training failing to translate into the work environment is because jobs haven’t been redesigned to accommodate new capabilities. In this case, if you are teaching employees new AI skills but not making adjustments to workload, priorities, or expectations, you’re asking them to add new competencies on top of already full plates.
What good looks like:
Job descriptions explicitly include time for skill application and experimentation. Performance metrics balance productivity with learning. Employees can point to specific aspects of their role where they are expected to use new capabilities.
Common failure pattern:
An employee completes prompt engineering training but still has the same deliverable deadlines and the same workload. They don’t have time to use new AI tools or integrate them into their daily work.
How to fix it:
Before training, identify what work will be done differently and how it will differ after training is complete. Adjust expectations accordingly. Understand that productivity may dip as employees become comfortable with new tools and processes.
4. Managerial Support: Are Managers Prepared to Reinforce Learning?
Even perfect training fails when managers don’t support the application of what employees learn when they’re back on the job. Supervisors are the critical link between classroom learning and workplace performance. If they haven’t been trained on what good performance looks like when new AI tools are adopted, or worse, if they’re uncomfortable with the technology themselves, they will unconsciously discourage adoption.
What good looks like:
Managers should receive training before their teams do and should be prepared to regularly observe, coach, and provide feedback on specific AI-enabled behaviors. They should be prepared and confident in modeling the use of new tools.
Common failure pattern:
Excited about what they have learned and the opportunity to experiment with AI to do their jobs, employees are quickly frustrated when they find their supervisors and managers aren’t quite sold on the new tools. They quickly revert to the old ways of doing things.
How to fix it:
Train managers first. Provide them with checklists that can help them understand what successful AI adoption should look like in specific job tasks. Coach managers on how to build AI skill development into performance evaluation processes.
5. Incentives and Recognition: What Behavior Actually Gets Rewarded?
Here is the brutal reality: people do what gets rewarded, not what gets trained. If your incentive systems still reward speed over quality, volume over insight, or individual heroics over collaborative problem-solving, no amount of training will change behavior. Employees will optimize for what advances their careers, and if that’s not the new skill, the new skill gets abandoned.
What good looks like:
Recognition systems explicitly reward the application of new capabilities. Performance bonuses consider how work gets done, not just outputs. Promotions go to people who demonstrate new skills, not just tenure.
Common failure pattern:
Your organization invests heavily in AI upskilling to improve decision quality and analytical rigor, but quarterly bonuses are still based purely on output volume. It doesn’t take long for employees to see that using AI for deep analysis only slows them down without providing any reward.
How to fix it:
Audit rewards, including formal bonuses, informal recognition, promotion criteria, and peer acknowledgment. Make sure that incentives are aligned with the post-training behaviors employees have learned. Adjust metrics, as necessary, before training begins.
The Business Case You Need to Make
Training programs shouldn’t be the starting point when you’re upskilling employees to build competencies in the use of AI tools. To be effective, training should follow a thorough analysis of the learning and work environment to address any systemic barriers that could prevent learning from sticking.
The business case CLO and L&D leaders need to make isn’t the case for training; it’s the case for systemic change designed to create an environment that will support training and sustain its impacts when employees are back on the job.
When you engineer learning systems first, training investments actually deliver returns. When you train first and hope the environment catches up, you’ve simply burned budget while frustrating your workforce.
Next Steps
You don’t need a massive transformation project to start building the foundation for effective AI upskilling.
Begin with diagnosis.
Pull together cross-functional representatives from key departments where AI skills are critical. Facilitate a workshop using the five elements provided here as a framework. Document specifically where systems conflict with desired behaviors. Then present your findings to leadership as a roadmap for preparing the foundation to ensure that your training investment achieves desired results.
The future of work isn’t just about having AI skills. It’s about building organizations where new skills can take root and flourish. Fix the system first, then train. That’s how upskilling will actually work.
Priyanka Dave, MBA, PhD, is a global thought leader in workforce development, upskilling, and behavioral science, helping organizations build agile, future-ready workforces in the age of AI. With more than a decade of international experience across technology, biotechnology, aerospace, and higher education, she designs evidence-based learning and capability systems that drive measurable transformation.
Dr. Dave’s research on feedback, motivation, and workplace learning has been featured in journals such as the Journal of Organizational Behavior Management and Development and Learning in Organizations. An award-winning author and speaker, Dr. Dave brings a distinctive blend of science, strategy, and systems thinking to shaping the future of human capability.