AI is changing the way eLearning teams work at remarkable speed.
AI can now complete in minutes tasks that once took hours.
AI can generate content faster. Teams can explore designs more quickly. AI can assist with coding. It can automate quality checks. Project workflows can become more efficient.
AI in learning and development is also changing the skills teams need to succeed.
But while AI is changing how we work, it does not change why we work.
The purpose remains the same. Teams still need to create meaningful learning experiences, solve real problems, improve outcomes, and help people learn better.
What is changing is the role people play in making that happen.
For L&D teams, that means adapting roles, skills, and ways of working.
And that is why eLearning teams increasingly need what we can call a Joker Mindset.
What Is the Joker Mindset?
In a deck of cards, the joker creates value by adapting to different roles. Depending on the game, it can adapt, fill a gap, or change what is possible.
The same quality is becoming increasingly valuable in an AI-enabled workplace.
The Joker Mindset means staying curious, flexible, and ready to evolve. It also means moving beyond traditional role boundaries, learning new skills, and trying new ways of working. Human judgment remains essential.
It means adapting to new roles, learning new skills, trying new ways of working, and knowing when human judgment matters most.
AI does not make expertise irrelevant. It changes where that expertise creates the greatest value.
For eLearning teams, that shift is already visible.
From Execution to Orchestration
AI-assisted workflows are already changing how many eLearning roles operate. Many roles are moving away from repetitive execution toward higher-value thinking, decision-making, and experience design.
Designers are no longer limited to creating every visual from scratch. AI can help generate concepts, variations, layouts, and creative directions. The designer’s value now lies more in art direction, visual judgment, storytelling, and accessibility. Designers also ensure that the final experience serves both the learner and the brand.
Developers can use AI to accelerate coding, troubleshoot issues, prototype interactions, and automate routine development tasks. Their role moves further toward architecting scalable, intuitive, and technically sound learning experiences.
Instructional Designers can spend less time producing first drafts. They can focus more on understanding learners, shaping learning journeys, and designing experiences that support behavior change.
Quality Analysts can use automation and AI to identify defects, inconsistencies, and potential accessibility issues faster. Human expertise remains critical in determining whether the experience is genuinely usable, inclusive, accurate, and effective.
Project Managers can spend less time tracking tasks manually. They can focus more on removing roadblocks, improving collaboration, anticipating risks, and supporting better decisions.
Functional Managers must manage what their teams know today. They must also prepare teams to learn what they will need tomorrow.
Across all these roles, the pattern is similar.
AI takes on more of the repeatable work. People move toward work that requires context, judgment, creativity, empathy, and accountability.
The Real Opportunity Is Not Just Productivity
The conversation around AI often focuses on speed.
How much faster can teams create content?
How much can teams reduce development time?
How many tasks can AI automate?
Those questions matter. But they are only part of the opportunity.
The bigger question is:
What can people do with the time AI gives back to them?
eLearning teams can use that time to understand learners more deeply and improve learning experiences. They can also strengthen accessibility, test new formats, and solve problems that technology alone cannot address.
AI should not simply help teams produce more.
Used thoughtfully, it should help teams think better.
The Human Skills That Become More Valuable
As AI capabilities grow, several distinctly human skills become even more important. Skill development now includes both technical and human capabilities.
Curiosity helps people explore what new technology can make possible.
Critical thinking helps them question AI-generated outputs rather than accepting them at face value.
Creativity helps teams move beyond predictable answers and create differentiated learning experiences.
Collaboration becomes essential as traditional boundaries between design, technology, content, data, and learning continue to blur.
Empathy ensures that efficiency never comes at the expense of the learner.
Adaptability allows individuals and teams to keep evolving as tools, workflows, and expectations change.
Perhaps the most important capability of all is the willingness to keep learning.
That should feel particularly familiar to an industry built around learning itself.
What Leaders Can Do
Building an AI-ready eLearning team is not simply about giving people access to new tools.
It requires an environment that encourages experimentation and new ways of working. Teams should feel free to question workflows, build adjacent skills, and try new approaches.
Leaders can start by identifying repetitive tasks that AI can support. They should also be clear about where human judgment must remain central.
They can also use this process to identify skill gaps across the team.
This should become part of every learning and development strategy.
Leaders can encourage teams to share experiments, successes, and failures instead of keeping AI knowledge within individual roles.
Skills development can also move beyond traditional job descriptions. This also creates new opportunities for professional development.
A designer may benefit from understanding prompting and data. An instructional designer may need stronger analytical skills. A developer may need deeper knowledge of learner experience and accessibility.
This broader approach can also support employee development.
AI tools for instructional design help teams test ideas and improve learning experiences faster.
Leaders also need to ask a broader question. Are leaders preparing teams only for today’s work, or also for what comes next?
The goal is not to turn everyone into an AI specialist.
It is to build teams that can adapt when the rules of the game change.
What This Means for eLearning
At Impelsys, we see this shift as more than an AI adoption story.
This shift is also changing corporate learning and development.
This transformation is changing how teams design, create, deliver, and improve learning experiences.
Modern learning and development solutions must combine technology with human expertise.
AI can accelerate development, enable smarter workflows, strengthen accessibility, support personalization, and help organizations unlock greater value from their learning content. But technology alone does not determine the quality of the outcome.
The goal is still to improve learning outcomes.
That still depends on human judgment. Teams need to understand the learner, ask the right questions, and make thoughtful design decisions. They also need to know when empathy, creativity, or critical thinking matters more than efficiency.
The opportunity, therefore, is not simply to automate more work.
The goal is to use AI to amplify human capability. It should give people more space to focus on the work where they create the greatest value.
For eLearning organizations, the bigger transformation may go beyond new tools. It may change what people are capable of doing with them.
Play the Joker
The future of eLearning will not belong to the teams that resist AI.
Nor will it necessarily belong to the teams that use the most AI.
It will belong to teams that know where technology adds value and where people add value. The strongest teams will know how to make both work together.
In an industry built on learning, the greatest competitive advantage may no longer be knowing everything about your role.
It may be staying flexible enough to keep redefining it.
Stay curious.
Stay adaptable.
Keep learning.
Play the joker.
Which part of your role has AI changed the most this year?
Authored by Jeevan J. Malvankar
