For learning and development (L&D) professionals, the past year has brought an avalanche of conversation about how generative artificial intelligence (AI) will reshape learning and work. And yet, many teams are still in exploration mode — curious, but cautious. According to a report by MIT and BCG, while 70% of companies have adopted AI, only 15% are using it for organizational learning. Meanwhile organizations that combine organizational learning and AI-specific learning (dubbed “Augmented Learners” in the report) are 1.4x more likely to realize additional business value and annualized revenue benefits from AI.

That gap matters. Because in L&D, we’re not just experimenting with tools, we’re shaping how the workforce learns to work differently. Our role is pivotal in translating AI capability into human capability.

I’ve worked across instructional design, learning strategy, performance consulting and organizational development (OD) for over a decade, and I’ve seen this pattern before: A new technology arrives and L&D gets looped in late. But with AI, we can’t afford to wait. This isn’t just another platform to evaluate. It’s a shift in how knowledge is created, accessed and applied — and it will impact how we design learning, support performance and enable capability at scale.

If you have yet to get started, there are several focused, tactical ways L&D teams can apply AI now to begin to driving impact, saving time and freeing teams up to focus on what matters most: enabling people to thrive in the flow of work.

1. Accelerate Course Development by Making AI Part of the SME Team

One of the most impactful ways I’ve used AI in my day-to-day work is in dramatically reducing course development timelines without compromising quality.

A recent example: We needed to create a net-new learning experience on an emerging internal topic. Historically, this kind of project would take more than eight weeks: multiple subject matter expert (SME) meetings, rounds of design, iterations after reviews. And by the time we released the content, we’d already be behind the need. Instead, we used AI strategically to cut that timeline to just three weeks.

Here’s how:

• I trained a custom GPT on internal materials (notes and documentation) from the SME along with notes I took during a preliminary SME interview, turning it into a “first-draft SME.”
• I worked with the GPT first to generate a detailed course outline and, later, my storyboard and even the written course content.
• This AI-SME reviewed initial drafts before they reached the real SME, catching terminology inconsistencies and logic gaps.

By the time the actual SME reviewed the course, she had minimal feedback, which I think we all know is pretty rare in L&D. We bypassed multiple rounds of revision, and our SME got her time back.

Tactical takeaway:

If you regularly work with SMEs who are time-strapped (and who isn’t?), try training a custom GPT on a topic-specific corpus: internal documents, prior decks and key terms. Use it to generate first drafts, anticipate SME feedback and free your experts to focus on nuance, not basic corrections.

Supporting research:

As David De Cremer and Garry Kasparov put it in Harvard Business Review, organizations will become more efficient and accurate when we combine artificial and authentic intelligence (AI + Humans) to create what they refer to as “Augmented Intelligence.” That is reflected in how we’re approaching AI in L&D — using it to accelerate development, streamline reviews and empower our people, not displace them.

2. Use AI to Personalize Learning at Scale, Without Reinventing Your Tech Stack

Personalization has been the L&D holy grail for a long time. But customizing learning for every role, team or development need has felt impossible without massive budgets or headcount.

AI is shifting that. We’re using it to:

• Generate role-specific summaries of organization-wide learning content.
• Create tailored learning journeys by combining AI-curated playlists with manager-selected development goals.
• Translate technical or policy-heavy training into plain language for different audiences.

For example, I used AI to tailor an onboarding path for three different roles — individual contributors, people leaders and executives — by adjusting tone, depth and use cases. The source content stayed the same and AI handled the contextual rewrite. What would have taken days took hours instead.

Tactical takeaway:

Use AI to adapt content by persona. Prompt tools like ChatGPT to “rewrite this for a front-line retail manager with no prior exposure to this concept,” or “simplify this for a new hire on day one.” The result is more engaging, role-relevant content that drives actual behavior change.

Supporting research:
Personalization is no longer a “nice to have,” it’s a workplace expectation, especially for younger generations. As SHRM CHRO Jim Link puts it:

“Younger generations have grown up in a world of personalized experiences, from streaming services to online shopping. It’s no surprise they now expect the same level of customization in their careers. Human resources (HR) leaders who fail to meet these expectations will struggle with engagement and retention… Organizations getting this right will lead the future of work, fostering a more engaged, productive and satisfied workforce.”

3. Streamline Curation and Surface Hidden Knowledge

We’re swimming in content, but most of it isn’t usable, searchable or aligned to performance needs.

One of the simplest, highest-ROI uses of AI in our work has been using it as a curation and synthesis engine:

• AI reviews internal decks, PDFs, transcripts and LMS assets.
• It summarizes or clusters similar content, highlighting overlaps and gaps.
• It generates role-specific “learning guides” or microlearning sequences from existing material.

Instead of building new content from scratch, we fed existing documents into AI, and it created a modular, learner-centered guide, organized by key milestones, not just topics.

Tactical takeaway:

Use AI to inventory and summarize what you already have. Prompt it to “Review these 10 documents and generate a 5-day onboarding journey for a junior engineer.” Then validate the flow with a stakeholder and publish in your learning management system (LMS) or learner experience platform (LXP).

Supporting research:
According to the BCG report, organizations can use AI to expand their ability to capture knowledge and use it to extract hidden or tacit information that is typically difficult to codify. For example, NASA trained AI used on the Mars rover on past data to help it to understand the concept of “interesting,” something that was difficult for operators to define.

4. Embed AI in Workflow Learning and On-the-Job Support

Training sessions are still valuable, but real performance change happens in the flow of work. That’s where AI can make learning visible, accessible and context-aware.

We’re starting to integrate AI into internal tools so employees can:

• Ask natural language questions (“How do I submit an expense report?”).
• Get instant, role-specific answers pulled from policy docs or SOPs.
• Receive suggestions or nudges based on workflow triggers.

Instead of navigating three systems or waiting for help, learners get support at the moment of need, and that’s transformative.

Tactical takeaway:
You don’t need to build an AI assistant from scratch. Use existing tools (e.g., Microsoft Copilot, Notion, Slack AI, Atlassian Rovo) and connect them to curated knowledge bases. Start with high-volume “how do I…” questions from your support tickets or help desk.

Supporting research:
Josh Bersin’s research has consistently highlighted that that “learning in the flow of work” is a top differentiator for high-performing orgs, and AI makes it technically feasible — if we design with intent.

Final Thought: It’s Time to Treat AI as a Capability, Not Just a Tool

AI isn’t just something L&D teams need to teach others about, it’s something we need to master ourselves. If we want to be strategic partners in building future-ready organizations, we need to lead by doing:

• Build internal fluency.
• Embed AI into our workflows.
• Partner with business leaders to solve real problems using real data.

The MIT SMR + BCG report makes it clear: AI transformation is not a tooling conversation. It’s an organizational capability-building challenge. And L&D is already in the business of capability building.

So, here’s your next move: Pick one use case. Experiment with purpose. Start small and start now.

And when you do? Don’t just track what changed. Share what you learned.

Because the L&D teams who figure this out early won’t just teach AI, they’ll shape how the workforce learns to thrive with it.