There’s something different about how people are learning right now.
It’s faster, less structured, and happening in real time. AI has removed the buffer that used to exist between learning something new and applying it. Employees are no longer waiting until they feel confident to use something; they’re using it while they’re still figuring out how it works and what it’s for. And that shift is not only changing how learning feels, but leading to more errors and setbacks.
The Shift From Private Learning to Public Experimentation
Before AI, learning was more contained.
Think about a typical onboarding process – it starts with training courses, introductions, expectation setting, and more documentation that you can possibly read. You have downtime to review and practice, and generally 60 or 90 days before you’re expected to have an impact. You had space to digest and reflect on what you’d learned before putting it into practice.
That space allowed people to learn without pressure and make mistakes without consequence.
And that space is shrinking.
What we’re finding is that employees are now learning the tool as they use it on finished products, all while the tool itself continues to change and evolve frequently.
As Catie Maillard, Global VP of People, put it: “We used to give people space to learn and experiment in a sandbox before expecting results. That space doesn’t really exist anymore – we’re expecting experiments to be production-ready.”
Why This Feels Riskier Than It Looks
On paper, this is a good thing.
Faster learning leads to faster application, which should lead to faster, more innovative growth. But in practice, it introduces a different kind of tension that most organizations haven’t addressed. That tension comes from a feeling that quality or output expectations are shifting without clear communication between employers and employees.
Between frequent updates, hallucinations, and what we’re seeing as a “confidence paradox”, errors are becoming more common, and outputs are becoming more average – lacking personality, or a defensible moat. Employees are stuck: they’re told to become more efficient by using AI tools while keeping the same level of quality.
That responsibility feels different.
And not everyone is comfortable carrying it.
What This Looks Like for Employees
Employees are adapting in real time.
They’re using AI to figure things out as they go, often relying on it to fill knowledge gaps they don’t fully understand. They’re testing ideas in the middle of their actual work, not in controlled environments designed for learning. That creates a situation in which learning and performance occur simultaneously.
And that’s where tension builds.
Because not everyone feels confident learning in front of others.
As Catie noted in another part of the conversation: “People are doing the work while they’re still trying to understand the work.”
Why This Isn’t Just a Learning & Development Problem
It’s easy to categorize this as an L&D issue.
But it goes beyond training programs and content delivery. This is about how employees experience growth inside your organization and whether that growth feels supported or exposed. It’s about whether people feel confident navigating something without clear rules or stable expectations, and whether we can set realistic expectations for employees in the Age of AI.
As Catie emphasized: “If we don’t acknowledge how different this feels for employees, and what our goals are with these transformations, we’re going to miss what they actually need to succeed.”
That’s not just training.
That’s the environment.
What HR Leaders Can Start Doing
Instead of trying to formalize everything, focus on normalizing the experience.
Start conversations – either virtually or during team meetings – that make learning visible across teams. Ask what employees are still figuring out, where they feel uncertain, and how they’re using AI to bridge gaps. These discussions create space for shared learning instead of isolated experimentation.
These actions do something important.
They make experimental learning feel supported and normalized instead of exposed.
Why This Matters Going Forward
The organizations that succeed here won’t be the ones with the most structured programs.
They’ll be the ones who adapt to help employees feel comfortable learning on the go. That comfort leads to more experimentation, more confidence, and better long-term outcomes. Without it, employees stay cautious and limit how far they push new tools.
Because that’s what AI requires.
Not perfection.
But the ability to keep moving while figuring things out.
Where This Goes Next
If your employees are learning in real time and navigating uncertainty without clear guardrails, that’s not a small shift.
That’s a fundamental change in how work gets done and how people grow within your organization. Most teams are still reacting to this instead of shaping it intentionally. That’s where HR has an opportunity to lead.
If you’re working through what this should look like, strategic guidance becomes especially valuable.
This is the work Donna Medeiros leads every day. She brings over 30 years of experience helping organizations navigate complex shifts in data, AI, and workforce transformation, including advising senior leaders in CDAO roles at Gartner. Her approach is grounded in how people actually learn and operate at work, not just how systems are designed.
What makes this conversation valuable is that it is not about adding more training or introducing another framework. It is about understanding how your employees are experiencing this shift right now and identifying what will actually support them in your environment. That includes how learning shows up in daily work, where employees feel exposed, and how to create the right balance between autonomy and guidance.
For many HR leaders, this is one of the first opportunities to step back and make sense of what they are seeing across teams.
If you’re noticing these patterns but don’t yet have a clear way to respond, it is worth having the conversation.
Connect with Donna Medeiros, VP of AI and Data Advisory, to talk through how to support your workforce through this shift: https://meetings.hubspot.com/donna-medeiros/ai_advisor_session
Frequently Asked Questions
AI is shifting workplace learning from structured, private environments to real-time, on-the-job experimentation. Employees are learning on the job, often without formal training or clear benchmarks.
Learning in public means employees are applying new tools and skills in visible environments before they feel fully confident. Their learning process is happening in real time, often tied directly to performance.
AI removes the buffer between learning and execution. Employees are expected to produce results while still figuring things out, which increases pressure and uncertainty.
HR leaders must manage inconsistent learning experiences, reduced employee confidence, and unclear standards for performance and development in an AI-driven workplace.
Organizations can normalize the learning process, encourage open conversations about uncertainty, and create environments that support experimentation rather than judge it.
Traditional training still plays a role, but it is no longer sufficient on its own. Employees also need support systems that help them learn continuously while working.
Critical thinking, adaptability, and the ability to learn independently are becoming more important than static technical skills.
