Kate Kraft |
Not long ago, AI sat at the margins of corporate learning-and-development (L&D) initiatives, relegated to an add-on, peripheral tool while the technology developed. That’s no longer the case.
Today, forward-thinking employers are already embedding AI into learning experiences in meaningful ways. In Guild’s marketplace of thousands of learning programs, we’re seeing what real adoption looks like — and where it’s starting to drive outcomes. The question isn’t whether AI can enhance learning. It’s how to use it to accelerate readiness for business-critical roles.
Across the learning value chain, from design to coaching to assessment, AI is already influencing how fast people gain skills, how effectively managers coach, and how L&D measures progress. But here’s the tension: AI is moving faster than most organizations can safely or strategically keep up with. Traditional learning cycles — annual planning, quarterly refreshes, multi-year vendor agreements — don’t match the new learning velocity AI enables. Many HR and L&D leaders face a new strategic test: how to use AI to drive real impact, not just stack tools.
Looking at early evidence from Guild learning partners in 2025, the takeaway is clear: AI has moved from a technology conversation to a talent conversation.
Where AI is making an impact
Richer interactivity, real-time feedback, and always-on support
What we’re seeing so far:
AI unlocks interactivity that keeps learners motivated and engaged.
Personalized, in-the-moment feedback accelerates learning.
Human-driven troubleshooting is giving way to AI support systems.
Here’s what those patterns look like on the ground:
Human + AI collaboration (University of Denver)
The University of Denver is using AI avatars in its frontline manager program. Human instructors handle feedback and onboarding so learners feel supported as they use the technology, but AI avatars provide scalable practice. Learners even get direct coaching from faculty to reinforce what they practiced with avatars.
Interactive simulations (eCornell)
eCornell is using AI-driven simulations that adapt to learner responses, turning static modules into live decision-making and negotiation scenarios. The model gives specific and direct feedback on the learner’s inputs live, accelerating speed-to-proficiency.
AI-powered grading (Springboard)
Springboard trained an AI “Grader Bot” to evaluate Cybersecurity assignments, providing immediate, high-quality feedback equal to or better than human graders. After piloting in select cohorts, the system scaled across all assignments, setting a new benchmark for quality, speed, and efficiency.
Always-on support (Chegg Skills)
Chegg Skills integrated AI as an embedded assistant, offering contextual help and feedback throughout their programs. Engagement rose by 30%, with many students saying AI felt less intimidating for questions and troubleshooting than asking an instructor.
Together, these pilots show AI reshaping three pressure points in learning: how content is created, how skills are practiced, and how support is delivered — all measurable in engagement, persistence, and speed-to-competence.
So what should HR leaders do?
Early signals from AI-powered learning are promising. Learning providers report stronger engagement, higher persistence, and reduced instructor burnout. Learners describe more confidence and faster skills development.
Based on our analysis, here are five no-regrets moves. These are practical steps you can start now to keep people at the center while taking advantage of AI’s opportunities.
1. Amplify. Don’t replace.
Use AI to extend learning, not replace it. Let assistants and simulations shorten feedback loops for learners so they can learn quicker—and let instructors and coaches bring the human connection and real-world practice that will only grow more valuable. AI can scale learning, but it cannot confer trust, context, or credibility. Those remain human responsibilities.
In practice: Let AI multiply the impact of humans: students can use AI chatbots for first-line questions and low-stakes practice, keeping humans for higher-stakes, one-on-one coaching. Learners are more willing to surface “small” questions to a chat agent, which brings needs to light earlier and preserves scarce coaching time for the deeper content.
2. Safety and governance before you scale.
Many AI-enabled programs stall because of challenges with learner access to AI and their uncertainty about using it. Frontline employees may lack devices or licenses, and many don’t know if they’re allowed to paste work material into an AI tool during training. This makes careful design even more important. The latest AI browsers, like OpenAI’s Atlas, can complete online training on behalf of an employee, so be judicious in what you allow and how you design your assessments.
In practice: Create learner-facing guides that are short, easy, and accessible. List approved tools and data types, and make clear how to label AI-assisted work. Treat governance as design, not red tape. Clear rules unlock experimentation without compromising trust.
3. Measure what matters.
Engagement should be seen as a starting signal rather than the finish line. Start by clarifying what impact means in your context: reduced ramp time, better retention, higher manager satisfaction, etc. This clarity will be critical as organizations' L&D functions shift from content production to AI-enabled content orchestration.
In practice: Build simple metrics you can track across pilots before scaling. Evaluate leading and lagging indicators, like learner reported confidence, pace/progression, and operational process improvement times; then, design programs so outcome signals are captured without creating reporting drag — the added forms, manual tracking, or extra touchpoints that make measurement burdensome for learners or facilitators.
Your AI in learning playbook: A one-page guardrail checklist
Keep your playbook to one page and write it for the learner.
Specify which AI tools are approved, what kinds of data are allowed (and what’s prohibited), where to work (enterprise channels), and who approves exceptions.
State your privacy and attribution expectations (e.g., label AI-assisted work) and note the supported devices/access for frontline workers.
Specify how to get a license, coaching, or accessibility support.
The goal is confidence and clarity, so learners can practice on real tasks, safely, not a legal memo no one reads.
4. Don’t just roll out tools. Teach people how to use them well.
Whether AI-powered or not, most tools fall short when employees aren’t trained to use them confidently. Embedding AI-driven feedback into orientation or training modules can help close that gap — speeding up adoption and shortening time to value.
In practice: Work with business leaders to evaluate where employees need more deliberate practice and identify how AI can support “sandbox”-esque role-play opportunities to learn, practice, and get feedback within existing business tools, without taking up valuable manager time.
5. Connect the dots.
AI can help track momentum, flag risk, and sequence work so managers know when a learner is ready for live customer contact, a clinical rotation, or production-grade tasks. It can also identify employees to prioritize for upskilling or reskilling to meet the demands of the roles of the future.
AI can also support internal mobility efforts, by informing hiring and promotion decisions, maximizing the value of your existing workforce and setting you up to scale.
In practice: Identify areas of AI automation risk within your organization, map employees’ skill gaps onto your learning ecosystem, and then drive reskilling efforts accordingly, in partnership with business leaders.
What this means for HR and what comes next
AI’s biggest value for HR is the visibility into who’s ready for what comes next.
AI is redrawing the boundaries between learning, performance, and workforce planning. For HR leaders, the opportunity is to translate learning signals into talent decisions: who’s ready for new roles, where risk exists, and which capabilities to build or buy. The shift is from program ownership to talent intelligence, connecting learning data to mobility, readiness, and retention outcomes.
The next phase will belong to the leaders who prove it works. Guild’s data shows that when learning becomes visible, personalized, and embedded in the flow of work, it drives measurable business results. Consistency, speed, and accountability don’t have to compete. HR and L&D leaders who move from pilots to proof will show not just what AI can do — but what it’s worth.
