The Three Big Trends Driving Change for L&D Organizations
The three greatest drivers of change for L&D groups will be artificial intelligence, skills-based talent, and new federated operating models.
Artificial Intelligence
AI has the likelihood of transforming L&D groups in several ways:
- New efficiencies in creating traditional L&D solutions (ILT, vILT, WBT, eLearning) allow teams to consolidate, expand, or stay agile in volatile areas.[i]
- SME-generated content becomes viable through prompt-driven development, requiring L&D to shift to curation, validation, and quality governance. [ii]
- Advanced experiential learning at scale: AI enables simulation, adaptive, personalized, and on-the-job learning solutions to be delivered more broadly.[iii]
- Continuous learning assessment and real-time coaching powered by AI analytics
- Agentic AI automation of operational tasks: scheduling, assessment, evaluation, reporting, and more.[iv]
- Human skills amplification: As AI automates cognitive tasks, emotional intelligence, critical thinking, change leadership, and ethical judgment become the new core curriculum—not just soft skills. Note: AI is advancing faster than most planning cycles assume; “uniquely human” is a moving boundary.[v]
Challenges AI Introduces for L&D:
The “AI Literacy Gap” Challenge: While AI removes production constraints, it creates a new barrier – L&D professionals themselves need AI fluency. NIIT research shows this is a larger barrier than AI technology itself.[vi]
The 70% Scaling Problem: Deloitte reports 70% of organizations struggle to move Gen AI from pilot to enterprise scale. It’s a change management crisis that L&D must navigate while simultaneously learning the technology themselves.[vii]
The Transition Paradox: L&D must simultaneously master AI tools, build skills infrastructure, redesign operating models, AND continue delivering current programs. Under-resourced L&D teams risk becoming perpetually reactive.[viii]
The Executive-Worker Trust Gap Accenture research reveals a 16-point gap between what executives believe they’re communicating about AI experimentation and what workers perceive. 53% of workers don’t know who is accountable when AI makes a mistake – a trust deficit that undermines transformation.[ix]
Skills-based Talent
Skills-based talent creates a new talent infrastructure built on comprehensive, current enterprise skills data linked to individuals, learning solutions, work processes, job architecture, and workforce planning. This enables more personalized, adaptive talent solutions and shifts HR focus from headcount management to capability orchestration. Organizations with strong skills infrastructure are 37% better at measuring business outcomes tied to learning .[x]
Skills-based talent may push L&D priorities, IF L&D owns or influences the skills infrastructure:[xi]
- Less generic curriculum design, more competency framework architecture and learning pathway orchestration.
- Skills taxonomy stewardship and governance (often shared with a cross-functional skills hub).
- Talent marketplace content curation and learning pathway integration.
- More content curation, cross-channel skills assessment, and skills-based workforce modeling.
- Greater involvement with career path-oriented development and internal mobility—though talent marketplaces that bypass L&D-curated content may reduce L&D influence.
Challenges Skills-based Talent Introduces:
The Governance Challenge: Organizations struggle with who owns skills transformation—L&D, Talent Acquisition, Workforce Planning, or a separate Skills Hub. Turf battles and unclear accountability are common.[xii]
The “Inference vs. Validation” Tension: AI can infer skills from HR data and behavior, but validation at scale remains a challenge. Solving this gap builds trust in skills data and drives adoption.[xiii]
Multi-Speed Skills Evolution: Technical skills (half-life ~2.5 years) decay faster than foundational or power skills. Traditional annual planning processes can’t accommodate the required refresh cycles. [xiv]
The Measurement Burden: Strong skills infrastructure creates expectations of continuous ROI proof. L&D must become metric-obsessed and data-fluent or risk being defunded. [xv]
The Compliance Anchor: Compliance training remains job-based, mandate-driven, and mostly immune to skills-based transformation—creating a split operating model.[xvi]
AI + Skills Together
The convergence of AI and skills-based approaches creates powerful synergies but also new complexities:
- All skills-based tech is built on AI [xvii]
- Distributed skills data management is intermediated by AI [xviii]
- AI-enabled personalization requires validated, current skills data to deliver value [xix]
- Cross-channel development requires AI-enabled skills assessment and cross-validation [xx]
However, AI-powered systems are only as good as their data, recommendations often lack algorithmic transparency (the “Black Box” problem), and AI trained on historical data can perpetuate developmental opportunity bias – requiring ethical oversight L&D must develop.
New Operating Models
The impact of AI + Skills will push L&D groups to evolve along several lines:
- Changes in learning portfolio ownership: For L&D, potentially less functional and career milestone training, more leadership and workforce capability development
- Changes in product line focus: Likely less traditional delivery (ILT, vILT, eLearning), more experiential, on-the-job and learning-in-the-flow-of-work (LIFOW)
- Demand for training early-career skills, once learned on the job, that are now being handled by AI
- Tighter integration with business units on talent marketplaces, workforce planning, and human-centered work design – though L&D may more often contribute than lead these initiatives
- New infrastructure management responsibilities: Skills data and systems, skills validation, content moderation, AI governance, etc.
The impact of these drivers is likely to push L&D in several directions at once, risking fragmentation. Simultaneously, these drivers raise the question of who will own the broader, multi-channel “talent development” mandate across the entire talent value chain – L&D, Talent, or a hybrid.
Three Divergent Futures for L&D
NIIT Research suggests L&D faces three possible futures based on how these forces converge:[xxi]
- Transformers (12%) – Strategic Orchestrators position L&D as connective tissue linking AI tools, skills intelligence, and business strategy. CLOs report to CEOs, co-own skills governance, and budgets grow as impact is proven.
- Builders (52%) – Execution Specialists excel at AI-powered content but risk remaining tactical. Measured on efficiency, not outcomes.
- Traditional (36%) – Efficiency plays risk being bypassed or consolidated as skills infrastructure moves to HR Analytics and development migrates to business units.
The Emerging Structural Shifts
Shift #1: A Growing L&D Platform Core
Despite agentic AI automating operational tasks, a platform core is growing to support all learning missions. It owns learning tech, skills architecture and analytics, shared measurement infrastructure, content and design standards, and AI governance. It may also include COEs oriented around the four product lines: Traditional (ILT/vILT/eLearning), Experiential (simulations, adaptive), Performance/OJT (coaching, apprenticeship, developmental assignments), and Learning in the Flow of Work (performance support, knowledge management, JIT help).
Shift #2: Potential Bifurcation of Learning Solution Development by Mission
As L&D missions grow closer to business partners, teams will feel a pull toward forward operating, semi-autonomous, specialized mission pods with their own domain expertise, tools, stakeholder relationships, embedded specialists, SMEs, and KPIs. Examples include Business Unit learning teams, Compliance learning, Leadership & Career Development, Transformation & Digital Adoption, Innovation & Future Skills, and Culture & Onboarding pods—some of which may report outside of central L&D. [xxii]
Shift #3: Including the Extended Ecosystem to Drive Culture Change
AI + Skills transformation is as much a culture change as an operating model change. Tighter engagement will be required with transformation offices, HRBPs, OD functions, IT enablement, data/analytics teams, compliance officers, external vendors, and embedded function academies. These partners, once arm’s-length stakeholders, must be brought into close alignment for full adoption.
Challenges the New Operating Model Introduces:
Escaping the “Order Taker” Mindset: Becoming a Strategic Orchestrator assumes L&D successfully transitions to strategic partner. Many organizations could bypass L&D entirely, with skills hubs reporting to CHROs and talent marketplaces owned by Talent Acquisition.
Cross-Functional Ownership Ambiguity: The structural question is not just integration but who leads – L&D or others. Skills hubs increasingly report to CHROs; talent marketplaces are often owned by Resource Management or Talent Acquisition. For many L&D organizations, securing a strong position within a hub-and-spoke model they don’t anchor is a more realistic—and still valuable – goal than the full Transformer path.[xxiii]
The Pod Dissolution Risk: Some pods (especially functional capability and transformation) might be fully absorbed by business units, leaving only compliance and leadership development in central L&D. What looks like evolution might be fragmentation that weakens enterprise influence.
The L&D/OD Integration Challenge: OD often owns change management, culture work, and organizational design that overlaps with multiple pods. Without deliberate collaboration, organizations risk duplicating effort or creating territorial conflicts.
The Practitioner Identity Crisis: Donald Taylor’s global sentiment survey shows practitioner pressure has returned to near-pandemic levels, with “human” surging in free-text responses. The people asked to lead AI adoption are quietly questioning whether their own skills still have value.[xxiv]
New Skills Required for L&D and Talent Professionals
Three Implementation Approaches: Which Path Will Your Organization Take?
Research from Deloitte suggests organizations typically take one of three approaches to skills-based transformation. Understanding which approach your organization is taking helps L&D position itself effectively:[xxv]
Approach 1: Talent Practice-by-Practice Transformation (“Talent-Driven”)
What it is: Start with transforming a single talent practice to be skills-based, then expand or pivot to build a central skills hub. One of the most common approaches—L&D may lead initially but then get subsumed into broader talent infrastructure. Limited business ownership; does not demonstrate ROI quickly.[xxvi]
Approach 2: Hub-First Infrastructure (“Foundation-First”)
Build a centralized skills hub with core infrastructure (talent philosophy, skills taxonomy, data systems, governance) before transforming individual talent practices. L&D is one spoke among many, with the hub typically owned by Talent. Pain point: building an enterprise taxonomy in the abstract can be very challenging.[xxvii]
Approach 3: Pilot Business Solutions + Scale (“Test and Learn”)
Select a specific business unit or high-impact use case for an end-to-end skills-based model, then scale based on learnings. Move from business unit to business unit, connecting skill taxonomies later (with governance and machine learning). Provides immediate ROI demonstration and business sponsorship. L&D may or may not be part of the pilot depending on where skills are most measurable.
The Question L&D Must Answer:
Which future are you building toward, and which implementation approach gives you the best positioning? The convergence of AI, skills-based talent, and new operating models isn’t just changing what L&D does, it’s determining whether L&D leads, follows, or disappears.
End Notes
[i] Pelster, B. and Hammerstad. J. (2026, February) The L&D Revolution: From Learning to Business Enablement. NIIT Confluence 2026 Proceedings. Clearwater FL.
Mikucki, E. (2025). AI in learning and development: The future of “smarter” training. Training Industry. https://trainingindustry.com/articles/artificial-intelligence/ai-in-learning-and-development-the-future-of-smarter-training/
Alster, K. (2026). AI in L&D has passed the tipping point: Here’s what the data shows. eLearning Industry. https://elearningindustry.com/ai-in-ld-has-passed-the-tipping-point-heres-what-the-data-shows
Omer, A. H. (2025). Maximizing ROI: Transitioning from ILT to eLearning with AI‑powered rapid solutions. eLearning Industry. https://elearningindustry.com/maximizing-roi-transitioning-from-ilt-to-elearning-with-ai-powered-rapid-solutions.
[ii] Pelster, B. and Hammerstad. J. (2026, February) The L&D Revolution: From Learning to Business Enablement. NIIT Confluence 2026 Proceedings. Clearwater FL.
Martinez, R. (2024). Employee‑generated learning content is a must. Training Industry. https://trainingindustry.com/articles/content-development/employee-generated-learning-content-is-a-must/
Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., … Resnik, P. (2025). The prompt report: A systematic survey of prompt engineering techniques (arXiv:2406.06608v6). arXiv. https://doi.org/10.48550/arXiv.2406.06608
[iii] Collins, Dr. G. Dickens, B. (2026, February) How AI will Define the Future of Learning. NIIT Confluence 2026 Proceedings. Clearwater FL.
Hamann, H. (2025, June 25). Bridging the skills gap: How AI‑powered simulations are reshaping L&D. The Regis Company. https://go.regiscompany.com/blog/bridging-the-skills-gap-how-ai-powered-simulations-are-reshaping-ld
Chahal, B. (2025). Personalized learning at scale: How AI is shaping L&D. Training Industry. https://trainingindustry.com/articles/personalization-and-learning-pathways/personalized-learning-at-scale-how-ai-is-shaping-ld/
Mollick, E., Mollick, L., Bach, N., Ciccarelli, L. J., Przystanski, B., & Ravipinto, D. (2024). AI agents and education: Simulated practice at scale (arXiv:2407.12796). arXiv. https://doi.org/10.48550/arXiv.2407.12796
[iv] Mehta, A. (2026) “The Agentic Readiness Model From Conversation to Orchestration: The Blueprint for the Autonomous Enterprise” Mehtadology Ltd. www.mehtadology.com
Gartner. (2025). Agentic AI benchmarks across the enterprise and HR. https://www.gartner.com/en/documents/6561002
Deloitte. (2025). Tech trends 2026: Agentic AI strategy. https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/agentic-ai-strategy.html
[v] Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey NIIT Managed Training Services & St. Charles Consulting Group.https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/
Krivkovich, A., & Madgavkar, A. (2026, January 6). Human skills will matter more than ever in the age of AI. McKinsey Global Institute. https://www.mckinsey.com/mgi/media-center/human-skills-will-matter-more-than-ever-in-the-age-of-ai
[vi] Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey NIIT Managed Training Services & St. Charles Consulting Group.https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/
[vii] Deloitte AI Institute. (2024, October). State of generative AI in the enterprise: Q4 2024 — Generating a new future. Deloitte. https://www.deloitte.com/us/en/insights/topics/digital-transformation/generative-ai-and-the-future-enterprise.html
[viii] Gartner (2024) State of the L&D Function Benchmarking Report, 2024–2025. Gartner. https://www.gartner.com/en/documents/5767515.
[ix] Close, K., Durg, K. Sakr, M., Wrobleski, S. Yosef, L. (2025, September) Learning, Reinvented: Accelerating Collaboration between Human and AI. Accenture https://www.accenture.com/us-en/insights/consulting/learning-reinvented-accelerating-human-ai-collaboration
[x] Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey. NIIT Managed Training Services & St. Charles Consulting https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/
[xi] Friedman, C. (2025). Enterprise skills unlocked: A blueprint for building skills‑based talent management. St. Charles Consulting Group. https://www.amazon.com/Enterprise-Skills-Unlocked-Skills-based-Management-ebook/dp/B0FC9SYBB4
[xii] Johnson, A., Friedman, C. (2023, September) Skills Based Organizations: Building Adaptive Workforces for the Future. NIIT Managed Training Services. https://www.niit.com/en/learning-outsourcing/insights/resources/skills-based-organizations-report/
Deloitte. (2025). Global human capital trends 2025: Turning tensions into triumphs. https://www.deloitte.com/global/en/insights/topics/human-capital-trends.html
Mercer. (2025). 2024–2025 skills snapshot survey report. https://www.mercer.com/insights/talent-and-transformation/skill-based-talent-management/
[xiii] Johnson, A., Friedman, C. (2023, September) Skills Based Organizations: Building Adaptive Workforces for the Future. NIIT Managed Training Services. https://www.niit.com/en/learning-outsourcing/insights/resources/skills-based-organizations-report/
Aptitude Research. (2025). Validated skills: The impact on talent acquisition transformation. https://www.aptituderessearch.com/wp-content/uploads/2025/05/Apt_SkillsHirevue_0325_Report_Final.pdf
van der Meulen, N., Tona, O., & Leidner, D. E. (2024). Resolving workforce skills gaps with AI‑powered insights. MIT Center for Information Systems Research. https://cisr.mit.edu/publication/2024_0401_DigitalTalentTransformation_VanderMeulenTonaLeidner
[xiv] World Economic Forum. (2025, January 8). The Future of Jobs Report 2025. World Economic Forum. https://www.weforum.org/publications/the-future-of-jobs-report-2025/,
- Bersin, B. Pelster, J. Schwartz, B. van der Vyver; (2017) “Career and Learning: Real-Time, All the Time” 2017 Deloitte Global Human Capital Trends: Rewriting the Rules for the Digital Age DU Press. p. 29. https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2017.html
[xv] Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey. NIIT Managed Training Services & St. Charles Consulting https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/
[xvi] Association for Talent Development. (2025). State of the industry: Talent development benchmarks and trends. https://www.td.org/research/state-of-the-industry
Chartered Institute of Personnel and Development. (2023). Learning at work 2023: Survey report. https://www.cipd.org/globalassets/media/knowledge/knowledge-hub/reports/2023-pdfs/2023-learning-at-work-survey-report-8378.pdf
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Bersin, J. (2022). The definitive guide to corporate learning: Growth in the flow of work. Josh Bersin Company. https://joshbersin.com/research/definitive-guide-corporate-learning/
[xvii] Friedman, C. (2025). Enterprise skills unlocked: A blueprint for building skills‑based talent management. St. Charles Consulting Group. https://www.amazon.com/Enterprise-Skills-Unlocked-Skills-based-Management-ebook/dp/B0FC9SYBB4
Gartner. (2024). How to adopt a skills‑based approach for talent management (ID G00815274). Gartner, Inc. https://www.gartner.com/en/documents/5618392
Eastwood, B. (2024, June 10). How companies can use AI to find and close skills gaps. MIT Sloan School of Management. https://mitsloan.mit.edu/ideas-made-to-matter/how-companies-can-use-ai-to-find-and-close-skills-gaps
Algasova, I. (2025, August 13). A practical guide to AI‑powered skills‑based talent management. Association for Talent Development. https://www.td.org/content/atd-blog/a-practical-guide-to-ai-powered-skills-based-talent-management
[xviii] Gartner. (2023). Innovation insight for AI‑enabled skills management (ID G00785487). Gartner, Inc. https://eightfold.ai/wp-content/uploads/2023-Gartner-Innovation-Insight-AI-Enabled-Skills.pdf
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[xix] Bersin, J., & The Josh Bersin Company. (2026). “The Definitive Guide to Corporate Learning: From Static Training to Dynamic Enablement”. The Josh Bersin Company. https://joshbersin.com/learning2026/
Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey. NIIT Managed Training Services & St. Charles Consulting https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/
[xx] Friedman, C. (2026) Taxonomies are the Easy Part: Why Skills Validation is the Real Challenge. St. Charles Consulting Group. https://www.linkedin.com/posts/craig-w-friedman-8950841_taxonomies-are-the-easy-part-why-skills-activity-7427417470975713280-tb0F?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAAwN_EBn-1NqnSKHnALrRIRmyAr-__EDF4
Gartner. (2023). Innovation insight for AI‑enabled skills management (ID G00785487). Gartner, Inc. https://eightfold.ai/wp-content/uploads/2023-Gartner-Innovation-Insight-AI-Enabled-Skills.pdf
[xxi] Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey. NIIT Managed Training Services & St. Charles Consulting https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/
[xxii] Lipton, A. F., Durham, L. Eighteen, J. Borzee, C., Friedman, C. Fiala, M. (2026, February) Rebuilding L&D for an AI-driven World: 2026 Global Learning Transformation Benchmark Survey. NIIT Managed Training Services & St. Charles Consulting https://www.niit.com/en/learning-outsourcing/learning-transformation-survey-report/ STAI index
[xxiii] St. Charles Consulting client experience.
[xxiv] Taylor, D. H. (2026, February). “L&D Global Sentiment Survey 2026: Into the Unknown”. Learning and Performance Institute. https://donaldhtaylor.co.uk/research_base/global-sentiment-survey-2026/
[xxv] Cantrell, S., Griffiths, M., Hiipakka, J., & Cleary, B. (2022, September 7). The skills-based organization: A new operating model for work and the workforce. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/talent/organizational-skill-based-hiring.html
[xxvi] Paraphrased from Deloitte Insights’ analysis of Cargill’s skills‑based transformation. Deloitte. (2024, June 14). Skills‑based organizations. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/talent/organizational-skill-based-hiring.html
[xxvii] Deloitte. (n.d.). Skills‑based organisations: Looking back to move ahead. Deloitte Netherlands. https://www.deloitte.com/nl/en/services/consulting/perspectives/skillsbased-organisations.html