4 min reading time

AI in L&D Is Inevitable—But AI-Savviness Is a Choice

Aisling MacNamara, Director, Learning & Enablement at LearnUpon

As a learning leader, the question for you is no longer whether AI will show up in your workflow—it’s whether you’re ready for the consequences of it already being there. 

You’ve likely seen AI features popping up across your tech stack. But for many of us, something still feels unresolved. While there’s no doubt that AI is being used more frequently, confident, strategic AI adoption has yet to catch up. As a result, we’re seeing a growing gap between simply adopting tools and becoming truly intentional about how they’re applied.

This gap has placed many learning leaders into one of two groups: AI Dabblers or AI Distinguishers.

• The AI Dabbler uses AI reactively. They find themselves experimenting with tools in isolation to solve immediate, high-pressure tasks just to keep their head above water.
• The AI Distinguisher uses AI with strategic intent. They integrate it into a deliberate workflow that prioritizes business outcomes, not just speed.

Ultimately, being ‘AI Savvy’ now depends on which path you choose to take: do you want to be an AI dabbler, or an AI distinguisher?

AI Strategy vs. AI Adoption: Why You Might Trip Up

Right now, it’s relatively easy to say you’re “using AI.” Most learning management platforms—including LearnUpon—offer features that serve as a powerful starting point for your strategic goals. But the real value isn’t just the tech; it’s the time you reclaim for high-impact work.

• Learning Journeys: Take the guesswork out of the user experience by automatically building paths that guide learners through every milestone, ensuring nobody gets lost in the shuffle.
• AI Content Authoring: Turn raw SME notes, documents, and even videos into a structured, ready-to-go course in minutes, rather than days of manual drafting.
• Rapid Assessment: Skip the headache of writing quiz questions by instantly generating knowledge checks and impact reports, allowing you to spend your time analyzing results instead of formatting them.
• Seamless Localization: Open up your programs to the world by easily translating entire curriculums into multiple languages—making global learning actually feel “local.”
• Smart Summaries: Let AI handle the “admin” side of writing by drafting course descriptions and notifications for you, keeping your learners informed without it becoming an afternoon-long task.

There’s no doubt that these features are great news for overworked and under-resourced learning teams, but having them in place is only the first step. While they can offer immediate relief from the manual grind, using them in isolation is simply exposure—not competence.

In 2026, your edge as an ‘AI Distinguisher’ lies in how you layer your human expertise over the tech. While an AI Dabbler uses these tools to tick a task off the list faster, an AI Distinguisher uses them to “buy back” time for the work that actually matters. By letting AI handle the routine stuff, you’re finally free to focus on the big picture, sharpening the real-world impact of every program you touch.

Why You Might Feel Stuck in the “In-Between”

The problem with bigger picture thinking isn’t usually a lack of vision on your part—it’s a lack of bandwidth. When you’re buried under a mountain of manual tasks, it’s nearly impossible to find the space to step back and focus on strategy.

That constant tug-of-war between daily demands and long-term priorities? You’re definitely not alone.

The risk is that when AI gets introduced reactively—just to cope with shrinking timelines—it can start to create friction across three areas you still need to own:

1. Ownership: Who is ultimately responsible for your AI-generated output?
2. Quality Control: What is your specific protocol for human review?
3. Accountability: Where is your human judgment non-negotiable?

Without the clear boundaries that AI Distinguishers put in place, AI stays stuck at the edges of your work: helpful for small tasks, but never truly transformative for the business.

4 Rules for Becoming an AI-Savvy “Performance Architect”

Breaking out of this reactive AI cycle isn’t about learning to code; it’s about a behavioral shift.

Take content authoring and course creation, for example. You can move from being a “content producer” to a strategic lead by simply changing how you partner with the technology.

1. Define Your Scope (The Strategy): Know exactly where AI fits into your workflow and—more importantly—where it doesn’t. Use it to build your structure, but never let it define your “why.”
2. Draft, Don’t Decide (The Creation): Treat AI output as your “almost there,” never your finished product. While the tool can do the bulk of the work, you remain the final authority, adding the context, brand voice, and soul that machines miss.
3. Iterate, Don’t Just Automate (The Mindset): Move away from the “magic button” expectation. Being ‘AI savvy’ is about refining and testing the output rather than just accepting it. It’s the understanding that the first draft is a canvas for you to improve upon.
4. Standardize Your Review (The Process): Don’t let speed at the start lead to a bottleneck at the end. Build a structured human-in-the-loop cycle to ensure you are consistently reviewing for quality and impact, not just correcting “AI-isms.”

Your Human-Powered, AI-Assisted Workflow

When you reach true AI-savviness, the technology stops being a series of experiments and starts being your infrastructure. It becomes dependable because your division of labor is crystal clear:

• AI supports your mechanics: It handles the high-volume, low-context work—like building initial structures, translating at scale, and organizing messy data. It’s your engine, not your driver.
• You shape the meaning: You provide the “why.” You’re responsible for the strategy, the cultural resonance, and ensuring the content actually solves a human problem in the real world.

The most effective leaders don’t use AI to bypass the design process; they use it to handle the heavy lifting. By offloading the repetitive stuff, you aren’t just saving time—you’re reclaiming the mental energy to do the strategic, creative work that a machine simply can’t touch.

Your Future in Learning: Moving Beyond Experimentation

As we move through 2026, the gap between AI Dabblers and AI Distinguishers will be clearer than ever. Simply dabbling in AI tools won’t be enough for you to prove value to the C-suite.

AI adoption is inevitable, but a strategic, confident use of AI in learning is a choice.

If you choose to be an AI Distinguisher now, you won’t just keep up with the industry—you’ll be the one shaping what “good learning” looks like for the next decade.