On the Skills & Growth Wave with AI

Executive Summary

L&D is shifting from “courses consumed” to capabilities deployed. AI now stitches together skills data, work history, performance, and business priorities to recommend the next best learning, the next best project, or the next best role. The result: internal marketplaces that surface gigs, rotations, and roles—and instantly show the learning needed to qualify. HR stops hand-curating catalogs and starts supplying ready talent to growth initiatives. Managers fill needs faster. Employees see real paths inside the company. Finance gets hard numbers on time-to-ready and retention.

Our free article unpacks how leading teams run this in practice: the core stack (skills graph, inference, recommendation, marketplace, analytics), the measurable benefits (retention, reskilling at scale, time-to-fill), and what truly differentiates providers (fresh skills ontologies, inference quality, loop from learning→mobility→performance, integrations, and explainability). We also map the near future—capabilities over skills, task-level matching, and genAI that builds micro-learnings from your own content.

If you’re designing your 2026 people strategy, don’t buy “AI for L&D” as a feature. Build a talent supply chain.
Read the full, free IEC Rebel’s Digest article—and participate in our Study #1: AI in Talent Intelligence & Development to benchmark your stack and influence the IEC Dynamic Map Quadrants.

Article 90 AI in Learning & Development

#3/8 – An IEC Rebel’s Digest article for the HR/WFM Global Study 2026

The short of it

If you still think L&D is about “who attended which course,” AI is about to make you look very 2016.

We’re moving from learning as content to learning as allocation of capability. And the engine behind that shift is AI sitting on top of skills data, work data, and people data — and then pushing people toward the next best learning, the next best project, or the next best role.

This is the corner of AI in HR that will actually change day-to-day work, because it sits where three realities collide:

1. Businesses don’t have the talent they need.
2. Employees don’t see growth paths and leave.
3. HR has the data — but it’s scattered, unlabeled, and stale.

AI in L&D (including internal mobility and talent marketplaces) is the answer to that triangle.

Let’s unpack how HR is using it, what the good platforms actually do, how providers differentiate, and where this goes next.

1. From LMS to Talent OS

Traditional L&D tooling was built like a library: catalogue, enroll, complete, report.

AI-driven L&D is built like an operating system: infer, recommend, route, match, close the loop.

The key mental shift: the unit of value is not the course — it’s the skill signal. AI reads signals (profile, role, performance, projects, manager input, even external CVs) and predicts what this person should learn or do next to increase business readiness.

That’s why this space increasingly blends with internal mobility and opportunity marketplaces: sometimes the best “learning” is not a course, it’s a stretch assignment for 3 weeks in Supply Chain.

So, L&D, Talent Management, and Internal Mobility are converging under the umbrella we’re calling here AI in Talent Intelligence & Development.

2. How HR is actually using it today

Most HR orgs start in three places — all very pragmatic:

1. Personalized learning recommendations
   • AI maps job families → skills → content and serves “next best learning.”
   • It auto-curates from multiple sources (your LMS, LinkedIn Learning, Coursera, internal wikis).
   • It reduces the need for L&D to manually build catalogs for every job.
2. Skill and role profiling
   • AI infers skills from job titles, CVs, internal HRIS, project histories.
   • It fills gaps in your skills graph that you’d never get to manually.
   • This is what unlocks mobility: once you know who can do what, you can move people.
3. Internal marketplace matching
   • Managers post gigs, projects, rotations. 
   • AI matches employees based on skills and potential and interests.
   • HR uses this to increase retention and to deliver development “in the flow of work.”

On top of that, more advanced orgs use AI for:

• Capability gap analysis for strategic initiatives (“We need 50 people with genAI-product skills in 9 months.”)
• Succession and career paths that aren’t static but adapt to what the business actually needs.
• Learning impact analytics tied to performance or project outcomes, not just completions.

3. The internal mobility piece: the hidden power play

Why is internal mobility always mentioned together with AI in L&D? Because AI finally fixes the two structural blockers of mobility:

1. We didn’t know what people could actually do: AI can infer missing skills, weight recency, and give you a 70–80% accurate view without 10,000 people filling in forms.
2. We didn’t know what opportunities existed: A marketplace powered by AI can surface projects, roles, gigs, mentorships, communities — not just permanent jobs.

So employees stop asking, “What’s my next step here?” and instead get a ranked list of “Here is what you could do next, and here is what you’d need to learn to be picked.”

That link — opportunity → learning needed to qualify — is where AI turns L&D from a cost center into a talent-supply function.

4. Key features of AI-powered L&D & marketplaces

Let’s get concrete. The stronger platforms in this space tend to offer variations of:

1. Skills graph / ontology engine
   • AI-assisted building and maintenance of your enterprise skills graph.
   • Automatic normalization of messy skill names (“Excel basic,” “MS Excel,” “Spreadsheeting”).
   • Mapping skills to roles, levels, and even geographies.
2. Inference and enrichment
   • Read HRIS, ATS, performance, even project tools to infer skills.
   • Estimate proficiency based on role tenure, recency, signals from assessments.
3. Recommendation engine
   • “Next best learning” tailored to role, career aspiration, and business priorities.
   • Can prioritize internal content over vendor content.
   • Can take into account language, location, compliance requirements.
4. Opportunity / internal talent marketplace
   • Project, gig, role, mentorship publishing.
   • Matching via skills + availability + interest.
   • Sometimes bidding or manager approval flows.
5. Career-pathing with gaps
   • “You are here → this role is 4 skills away.”
   • Links each gap to specific learn/do assets.
   • Makes development plans real, not aspirational.
6. Analytics and talent intelligence
   • Heatmaps on skill readiness per business area.
   • Time-to-ready for critical roles.
   • Which content actually moves the needle.
7. Governance, security, enterprise readiness
   • Role-based access, regional data controls, model transparency.
   • Very relevant for global orgs with EU employees.

5. How it will change the work (for HR, L&D, managers, employees)

For HR / L&D:

• Less time curating catalogs, more time designing capability programs tied to strategy.
• Move from “we offer courses” to “we supply ready talent for Product, Ops, Market Entry.”
• Better conversations with Finance because you can show skill-to-business impact.

For managers:

• Easier to find internal people fast instead of going to the external market.
• Ability to post small, low-friction gigs to test people.
• Visibility on who is “almost ready” if they complete 1–2 learnings.

For employees:

• Clarity on possible career moves inside the company (retention lever).
• Learning that is actually relevant, not generic dumps.
• More portfolio-like careers: short projects across functions.

For the organization overall:

• Internal mobility speeds up.
• Time-to-fill goes down.
• You build a culture of transparent opportunities, which helps engagement.

6. Benefits (the ones you can sell internally)

1. Retention: People leave because they don’t see a path. A marketplace + AI recommendations = immediate visibility.
2. Reskilling at scale:AI reduces the human labor of building role-based curricula for 50 roles in 15 countries.
3. Better use of internal supply: You discover hidden talent pockets (e.g., ex-developers in customer success).
4. Faster response to strategy shifts: If the business pivots to AI products, you can map who’s closest to the needed skills and push learning to them.
5. Data for workforce planning: L&D stops being anecdotal — you can tell the CHRO, “We can have 60% of our people AI-literate in 6 months, here’s the path.”

7. So… how do providers actually differentiate?

This market is getting crowded. On the surface, everyone says “AI,” “skills,” “recommendations.” The real differentiation tends to happen on five axes:

1. Depth and freshness of the skills graph
   • Some vendors maintain huge, continuously updated global skills ontologies trained on real labor market data.
   • Others rely mostly on your internal data and get stale quickly.
   • The better ones detect emerging skills (think: LLM ops, AI product PM) and suggest them before you even define them.
2. Quality of inference
   • Good platforms don’t just say “you’re a PM, so you have stakeholder management.”
   • They weight recency, context (industry, product type), and corroborating signals (certs, projects).
   • Some even let managers validate or override to improve the model.
3. Tightness of the opportunity loop
   • The winning approach ties learning → mobility → performance in one loop.
   • Weaker platforms just recommend content and stop.
   • Stronger ones show: “Take this course to qualify for this project that aligns with your career path.”
4. Enterprise integrations & trust
   • Differentiation on how easily they ingest HRIS, ATS, content libraries, project systems (Jira, ServiceNow, Workday, SAP).
   • Also on regional compliance (EU, data residency, works councils).
   • For global orgs, this is often the real buying criterion.
5. AI strategy: open, closed, or hybrid
   • Some vendors run mostly on proprietary models fine-tuned on HR/talent data.
   • Others orchestrate multiple LLMs (for classification, recommendations, summarization).
   • The more mature ones give you explainability (“we recommended this because…”) which is critical in Europe and for change management.

You can basically ask vendors:

“Show me how your AI updates skills and opportunities week over week without manual work — and show me how a manager can trust the recommendation.”

If they can’t do that, they’re still in the marketing phase.

8. Future directions (the fun part)

Here’s where this is heading over the next 12–24 months:

1. From skills to capabilities: Skills are atomic; capabilities are what the business cares about. AI will help roll up skills → capabilities → roles automatically.
2. Task-level matching: Instead of “a project in Finance,” you’ll see micro-matching to tasks (“run this analysis,” “translate this deck”), powered by AI-generated task descriptions.
3. GenAI as learning co-pilot: Not just recommending courses, but building micro-learnings from your own internal content, policies, SOPs. Personalized, on-demand.
4. Performance-linked development nudges: AI will read performance reviews and instantly generate development plans, coupled with internal opportunities to apply them.
5. Scenario-based workforce readiness: “If we open 3 new regions, how many people can we redeploy with <40 hours of training?” AI will answer that.
6. More control for works councils / compliance: Especially in DACH/EU, we’ll see features for transparency, opt-out, and human review of AI in mobility decisions.

9. What HR should do right now

Since this is the “Rebel’s Digest,” here’s the unvarnished version:

1. Stop trying to model every skill manually.: Pick a platform that can infer 70% and let managers correct the rest.
2. Pilot in a business unit that hurts.: Somewhere with high turnover or fast-changing skill needs. Prove that internal mobility + AI recommendations can fill roles faster.
3. Connect learning to opportunity.: Don’t just roll out “personalized learning.” People learn when there’s a clear outcome. Always show the opportunity next to the learning.
4. Set data rules early.: Get IT, legal, and works council on board with how AI infers, stores, and exposes skill data.
5. Measure what matters.
   • % of employees with a recommended next step
   • number of internal moves sourced from the marketplace
   • time-to-ready for priority roles
   • retention in key populations

Those are the metrics that make CHROs and CFOs listen.

10. The bottom line

AI in L&D — especially when fused with internal mobility and marketplaces — is not a “nice new gadget.” It’s the operating layer that turns your people data into real-time talent supply.

The organizations that win will be the ones that:

• let AI do the heavy lifting on skills,
• tie learning to actual opportunities,
• and demand explainable, integrable AI from their providers.

Everyone else will still be pushing courses and wondering why their high potentials keep leaving.

Closing note & invitation

Provider comparisons—and the identification of the leading AI provider in Talent Acquisition—will be a core part of the first global IEC study on “AI in Talent Management and Development.” Participants are invited to join free of charge.

Participation: pm@theIECgroup.com

Study scope (AI-first lens):

Study #1: AI in Talent Intelligence & Development

• AI-Driven Talent Acquisition
• Performance & Talent Intelligence
• Learning & Development (incl. internal mobility/marketplaces)

Study #2: AI in Workforce Operations & Pay

• AI in Scheduling & Time Management
• AI for Payroll & Compliance
• Total Workforce Orchestration (VMS/FMS, contractors, EOR)

Study #3: Employee Experience & People Insights

• Employee Experience & Virtual Assistants
• Workforce Analytics & Planning

Over the next few weeks, we’ll zoom into each of the eight categories in IEC Rebel’s Digest—one deep dive at a time, with practical use cases, KPIs, and the vendor patterns to watch.

IEC Rebel’s Digest— The IEC Group can help you audit your global employment setup by identifying labor leasing risks, verifying licensing requirements, and ensuring your EOR partners meet every compliance standard—before regulators come knocking.

Last but not Least: If you’re facing challenges and wondering how others are managing similar issues, why not join The Leadership Collective Community? It’s a peer group and webcast platform designed for leaders to exchange insights and experiences.

JOIN THE IEC NETWORK

Introducing the IEC Knowledge Network Free Membership – Your Gateway to Seamless Access!

We are thrilled to present a new service that goes beyond the ordinary download experience. In addition to offering you the ability to download the things you love, we are delighted to introduce the IEC Knowledge Network Free Membership.

The Free Membership option grants you access to our library of articles and videos, without the need for tedious registrations for each piece of content.

The publication serves as a trusted resource to support executives in their pursuit of sustainable and successful global expansion. In addition the IEC Practitioners are available to discuss your specific challenge in more detail and to give you clear advise..

Take advantage of this valuable resource to accelerate your global expansion journey