Enterprise learning, rewired: Absorb LMS brings AI and human-centered strategy together

What happens when AI stops assisting—and starts helping you lead?

AI and efficiency go together. Faster processes, less friction, and more automation are extraordinary advantages for organizations adopting AI. But efficiency is a floor, not a ceiling.

As AI becomes the norm, the opportunities worth chasing are at the intersection of what AI can do and what it can’t, like intuition, judgment, the ability to read a room or ask the question nobody else thought to ask. Enterprise learning is entering its agentic era, where LMS platforms stop being libraries and start becoming learning and leading infrastructure—all in the name of expanding human capability and perfromance.

Agentic AI is making the partnership of AI and human skills more powerful. It understands intent, takes action across platforms, and connects dots that used to take weeks of coordination to see. Its greatest value is that it creates more room for human thinking and the perspectives, creativity, and judgment that only people can bring.

All of this is changing what leadership looks like in HR, L&D, and product. The conversations can be uncomfortable because there's a lot of fear around AI. But call out the elephant. Bring your team to talk about it. Discover where AI could reduce their noise so they can focus on what matters. Ask them what they'd do with more time, more clarity, more room to lead.  

Absorb LMS is building toward that future with a learning ecosystem designed to deepen learning experiences. In this article, we share 17 insights from Absorb's leadership team on what the future of AI and learning looks like with people in mind.

Each insight is paired with an "Ask your team" prompt with real questions to spark conversations about skills strategy, AI adoption, and what it's going to take to lead through what comes next.

Meet the minds behind Absorb’s AI vision

Absorb LMS is shaping the future of enterprise learning. These leaders are driving the shift from reactive automation to strategic orchestration. Here’s who they are, and how they think about AI.

Craig Basford, EVP, Product
Craig sees agentic AI as the “best” tier of learning design, where learners don’t just consume content, they co-create it with intelligent agents.

Cheryl Yuran, Chief People Officer
Cheryl brings a human-centered lens to AI strategy. She champions psychological safety, peer mentoring, and inclusive learning environments, especially as AI becomes more autonomous.

Leslie Kelley, Chief Growth Officer
Leslie sees AI as a business-wide enabler. She’s focused on tying learning to measurable outcomes (retention, revenue, and readiness) and believes agentic AI must serve the entire ecosystem, not just internal teams.

Obaidur Rashid (OB), VP, AI Strategy and Innovation
OB is Absorb’s agentic AI visionary. He’s building tools that understand compound intent, automate workflows, and connect learning to performance through data interoperability.

Matthew Reeves, CEO & Co‑Founder, Together (an Absorb company)
Matthew pairs mentorship science with AI autonomy to strengthen human connection at scale. He believes AI should amplify what great mentors do best—accelerate development, build confidence, and turn networks into an organization’s most defensible advantage.

1. AI's biggest opportunity isn’t automation but the acceleration of human development

“The irony of AI is that it raises the premium on being human. But most organizations are treating AI like a tech upgrade, optimizing for tools and ROI instead of developing the people who make it all work.” — Matthew Reeves  

Much of the conversation around AI at work centers around what AI can do. In that lives the question if it leaves less for humans to do. But the more powerful question is to ask what can AI help people learn to do better?

Those leading AI are deploying AI to execute tasks faster and they're using it to build sharper thinkers, more adaptive leaders, and more confident decision-makers. AI-powered learning tools are able to meet employees where their skill gaps are, resulting in a workforce that’s doing more than leaning on AI. It’s a workforce that’s experiencing skill growth because of it.  

The skills that create the most durable value like critical thinking, contextual judgment, curiosity, the ability to question an AI's output aren't soft skills. They're critical capabilities that need to be practiced, stretched, and developed over time.  

It is limiting to use AI only to substitute for human thinking, rather than to strengthen it. When the learning infrastructure mirrors the mindset — AI as a tool for execution rather than growth — you get a workforce that moves faster but develops slower. That’s not good long-term.  

AI should not be the answer, but the coach.

Ask your team:

• Are we using AI to help people complete tasks, or to help them build the capabilities behind the tasks?

• What would it look like to use AI-powered tools to accelerate leadership development, not just content delivery?

• How are we measuring skill growth (not just AI adoption) as a measure of learning success?  

2. In the future, your LMS will work as hard as your L&D team does

“Tomorrow’s LMS won’t be a bottleneck—it’ll be a conductor.” — Craig Basford

The best learning cultures are built by people who understand what learners need and design experiences that meet them there. But for many L&D teams, a lot of energy gets consumed by the operational grind like managing content libraries, tracking completions, chasing enrollments, and pulling reports. Administrative work eats the time and energy of the strategic work.

AI can change this. Tomorrow's LMS actively works alongside your team to surface the right learning to the right person at the right moment. It flags who's falling behind before a manager has to ask. It personalizes pathways without requiring someone to manually build each one. It handles the repetitive so your team can focus on what humans do best: understanding your people, designing meaningful experiences, and connecting learning to business outcomes.

It's not a platform to be managed, but a capable partner to direct. The L&D role won't shrink but it will shift from administration to orchestration, from reactive to proactive.

The organizations that will win will use AI to free their best people to do their best thinking.

Ask your team:

• What administrative tasks are consuming time that could be spent on learner impact?

• If your LMS could handle the operational load, what would your L&D team focus on instead?

3. When AI does the doing, everyone steps up to the directing

“Leadership becomes more important—because everyone leads their AI agents to outcomes.” — Obaidur Rashid

Agentic AI scales autonomy. When every employee has an AI agent to direct, leadership becomes distributed, and with it will come the democratization of ownership, autonomy, and accountability across every level of an organization.

The same shift is happening in learning itself. Just as agentic AI empowers employees to lead without a traditional leadership title, AI-powered LMS will empower people with limited instructional design experience to build meaningful learning content for the first time. Subject matter experts, team leads, frontline managers can step in and create. The barrier to building isn't gone, but it's lower than it's ever been. That's the democratization of learning in action.

As organizations move from centralized control to empowered enablement, training programs need to evolve alongside them — from teaching task execution to building intent-driven leadership, where employees learn to guide both people and AI agents toward meaningful outcomes.

Ask your team:

• How are we preparing employees today to lead AI agents in the future?

• What training do we need to build delegation as a competency?

• How can we embed agentic thinking and democratized learning into onboarding and leadership development?

4. AI will turn learning into a performance multiplier

“AI doesn’t just personalize learning—it aligns it with business outcomes.” — Leslie Kelley

AI-powered LMS platforms can now connect learning data with performance metrics, enabling L&D teams to prove impact in real time. In the near future, AI-powered LMS platforms will predict performance, prescribe development paths, and automate skill-building tied directly to business goals. Learning will evolve from a support function into a strategic engine for productivity, retention, and revenue growth.

For leaders, this means preparing to shift from content creation to performance orchestration. The question becomes: How do we design learning that drives measurable outcomes?

Ask your team:

• What business outcomes are we trying to influence through learning?

• How can we use AI to surface skill gaps and close them faster?

• What metrics will help us prove learning ROI to the C-suite?

5. Learning will be measured by impact, not completion

“Completion was never the goal. Performance always was. Now AI can close the gap between the two — and L&D will never look the same.” — Obaidur Rashid

Your LMS data should seamlessly connect with performance systems to prove ROI.

It’s a big shift for L&D leaders from activity-based metrics to outcome-based accountability. Completion rates and seat times just won’t cut it. The future of learning measurement directly links learning to performance, productivity, and business goals.

For this to happen, we’ll need truly seamless data interoperability, the ability to connect the LMS with systems like CRM, HRIS, and productivity tools to surface real-world impact. It also means evolving dashboards to show skill acquisition, behavior change, and business contribution.

Ask your team:

• What systems do we need to integrate to measure learning impact?

• Are we tracking outcomes beyond completion rates?

• How can we build a dashboard that shows learning’s contribution to business KPIs?

6. Tomorrow, learning will be delegated, not delivered

“Instead of searching for a course, you’ll ask an AI tutor to create a 10-minute video.” — Craig Basford

Agentic AI understands compound intent and delivers hyper-personalized content, a huge leap from content libraries to content generation. Instead of curating static modules, teams will enable dynamic, on-demand learning, including micro-learning right in the flow of work, tailored to individual needs, roles, and goals.

The door then opens to scalable personalization, where AI tutors respond to real-time queries, generate in-the-moment microlearning, and adapt formats to learner preferences. It also means rethinking instructional design to support AI-assisted creation.

Ask your team:

• What learning experiences could be generated on demand?

• How can we shift from static content to dynamic creation?

• What frameworks do we need to ensure quality and consistency in AI-generated learning?

7. It’s possible for AI to create safer spaces for learning

“AI agents give learners a safe space to ask questions and rehearse without judgment.” — Cheryl Yuran

Psychological safety meets personalization, especially valuable for introverts.

For L&D leaders, this is a powerful opportunity to scale inclusive learning environments. AI tutors can offer private, judgment-free zones where learners feel safe to make mistakes, ask questions, and practice skills, without fear of embarrassment or bias.

This may be especially impactful for neurodiverse learners, introverts, and those in high-stakes roles. It also supports a culture of vulnerability and growth, where learning is seen as a journey, not a performance.

Ask your team:

• How do we ensure AI tools foster psychological safety?

• What role does vulnerability play in our learning culture?

• How can we use AI to support inclusive learning for diverse learner profiles?

8. Learning must enable everyone

“AI-powered learning needs to be enabled across your ecosystem.” — Leslie Kelley

Enablement for customers, partners, and suppliers, not just employees.

For L&D leaders, this means evolving from internal training to ecosystem-wide enablement. In today’s distributed business landscape, learning must support every stakeholder who contributes to growth, service, and innovation. AI-powered LMS platforms make it possible to scale structured learning across audiences with different needs, roles, and contexts.

This shift will unlock new value: better customer onboarding, more capable partners, and aligned suppliers—all contributing to business outcomes.

Ask your team:

• Who outside our org needs structured learning?

• How can we scale training across our business ecosystem?

• What content formats and access models work best for external audiences?
  

9. Vulnerability will still matter (maybe even more than ever)

“The foundational work isn’t about getting everyone ‘AI‑ready.’ It’s about creating a culture where people feel ready to try.” — Cheryl Yuran

Technology can’t replace trust. HR must model psychological safety.

For L&D leaders, this is a reminder that human connection is irreplaceable. While AI can personalize, automate, and scale learning, it can’t foster trust, empathy, or psychological safety. These are the foundations of effective learning cultures, especially in hybrid and remote environments.

Modeling vulnerability—through storytelling, feedback loops, and inclusive leadership—creates space for authentic growth. AI in every form (from traditional to agentic) should support this, not overshadow it.

Ask your team:

• Are our leaders modeling vulnerability?

• How do we balance AI efficiency with human connection?

• What rituals or practices reinforce psychological safety in learning?

10. AI adoption is split, but don’t wait too long to explore...

“The organizations that will lead with AI aren’t the ones that started first, they’re the ones that start with intention.” — Leslie Kelley

It's time to move from exploration to execution and from curiosity to capability. AI adoption is uneven, but if you wait, you’ll fall behind. The key is to start small, learn fast, and scale strategically.

A crawl-walk-run approach helps build confidence and competence. Begin with pilot programs, automate low-risk workflows, and gradually expand to personalized learning and agentic enablement.

Ask your team:

• Where are we on the AI adoption curve?

• What’s our crawl-walk-run strategy?

• What use cases could deliver quick wins and build momentum?

11. AI-native employees will expect more

“Once someone’s worked with AI, there’s no pulling them back into traditional workflows.” — Cheryl Yuran  

With evolving AI, personalization and autonomy are now table stakes. AI-native employees—those who use AI tools daily—expect learning to be instant, personalized, and frictionless. Traditional models like static courses and rigid pathways are losing their value, fast.

To stay relevant, L&D must evolve toward adaptive learning ecosystems that mirror the autonomy and responsiveness employees experience elsewhere. It's not too early to rethink legacy systems and consider how agentic, learner-led models might positively impact your teams.

Ask your team:

• Are our learning models meeting AI-native expectations?

• What legacy systems need to evolve?

• How can we design learning that feels intuitive, personalized, and empowering?

12. AI strategy should be decentralized—but guided

"AI adoption without guardrails is risk, not innovation. The organizations getting this right aren't choosing between experimentation and responsibility. They're building the frameworks that make both possible at scale." — Leslie Kelley

Now’s the time to explore ethical frameworks for your organization’s AI adoption.

For L&D teams, this means balancing freedom with responsibility. AI experimentation should be encouraged—but within clear ethical, legal, and operational boundaries. A decentralized approach allows teams to innovate locally, while guardrails ensure alignment with organizational values and compliance.

Use an approach that fosters ownership, creativity, and agility, while protecting against misuse and fragmentation.

Ask your team:

• Are we giving teams room to experiment with AI?

• What guardrails do we need to ensure responsible use?

• How can we share learnings across teams to accelerate adoption?

13. Soon, the workforce strategy will be rebuilt

“What used to mean hiring smart people now means reorganizing how and where employees are deployed.” — Leslie Kelley

Agentic AI helps leaders rethink roles and skills. It’s a fundamental shift in workforce design. AI is a catalyst for reimagining how work gets done. Roles will evolve, workflows will be restructured, and skills will need constant refresh.

Strategic workforce planning must now include AI fluency, delegation skills, and cross-functional agility. Learning becomes the engine that powers this transformation.

Ask your team:

• How are we redesigning roles for an AI-enabled workforce?

• What skills will matter most in the next 12 months?

• How can we use learning to support workforce agility and resilience?

14. Social learning will spark adoption

“Technology doesn't drive adoption. People do. Your biggest AI advocates are already in your Slack channels, figuring it out. Give them space to experiment and share. Your job is to amplify them.” —Matthew Reeves

Peer-to-peer sharing unlocks momentum.

Just like everything else, when it comes to AI, culture drives adoption. Formal training is important—but informal, peer-led learning is often what accelerates change. When employees (and especially leadership!) showcase creative uses of AI (as well as failures, learnings, and wins), it builds trust, curiosity, and momentum across the organization.

Creating internal communities of practice, spotlighting success stories, and encouraging cross-functional sharing can turn AI from a tool into a movement.

Ask your team:

• How are we showcasing internal AI use cases?

• What communities could help drive adoption?

• How can we reward and amplify peer-led innovation?

15. The next gen UI will be based on natural language

“Today’s UI is a maze built on the assumption we can’t understand user intent.” — Obaidur Rashid

Agentic AI introduces conversational interfaces.

User experience must be reimagined. Traditional interfaces rely on clicks, filters, and navigation trees. But with agentic AI, users can express compound intent—and get what they need through natural language.

This shift will make learning more intuitive, accessible, and inclusive, especially for non-technical users. It will also simplify workflows, reduce friction, and boost engagement.

Ask your team:

• Are our tools intuitive enough for natural language use?

• What workflows could be simplified with conversational AI?

• How can we design learning experiences that feel like a conversation?

16. No matter what we call it, inclusive training still matters, and AI can help

“Inclusive design is a foundation, not a program, and AI gives us better tools to get it right.” — Cheryl Yuran

Agentic AI must support inclusive environments.

Gitnux DEI in eLearning Report, 2025 says only 35% of eLearning content developers have received formal training on designing for diversity, highlighting a gap in inclusive AI-powered learning.

Even as terminology is shifting, it’s more important than ever to design for equity and belonging. Done right, AI can help reduce bias in content delivery, personalize learning for diverse needs, and support cross-generational and cross-cultural communication.

But it requires intentional design, intentionally embedding inclusive principles into AI training data, content frameworks, and feedback loops.

Ask your team:

• How are we designing AI-powered learning to reduce bias?

• Are we enabling cross-generational and cross-cultural communication?

• What inclusive design principles guide our learning strategy?

17. Learning should be moving towards business strategy territory

“Learning is no longer owned by one department. Agentic AI will enable strategic enablement across departments.” — Leslie Kelley

Performance is a CEO-level priority.  

For L&D leaders, this is a call to step boldly into the boardroom. Learning isn’t just a support function, it’s a strategic driver of revenue, retention, readiness and performance management. Agentic AI will make it easier than ever to align learning with business goals, measure impact, and enable every department.

This means embedding learning into sales enablement, customer success, product innovation, and leadership development, not just compliance and onboarding.

Ask your team:

• Is learning embedded in our business strategy?

• How are we tying learning to revenue, retention, and readiness?

• What cross-functional partnerships could amplify learning’s impact?

AI's impact on learning is already taking shape

AI won’t replace L&D. But L&D leaders who use AI will replace those who don’t. Every learner, manager, and administrator deserves a personalized team of agents that understands their role, their context, and their goals. Absorb is building that with Aura.  

Absorb Aura will be a team of 17+ learning and admin agents accessible from one central learning hub, designed to turn every step forward into measurable capability and real performance gains. Agents that route questions, filter access, and move tasks to completion, compounding skills, speed, and impact with every interaction.

This is where thought leadership and grand vision meets execution. Tomorrow's learning is human at its core, intelligent by design, and built to create impact that lasts. The organizations that start building toward it today will be the ones that define what performance looks like tomorrow.