AI first: L&D function- From training calendars to self evolving skill networks

AI first: L&D function- From training calendars to self evolving skill networks

RV Iyer RV Iyer

RV Iyer

Founder @startup - stealth mode

Published Apr 24, 2025
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Learning & development (L&D) is a calendar-based support function, disconnected from daily business realities

Learning & Development (L&D) has long existed as an event-driven, HR-controlled, calendar-led function. It managed workshops, training programs, leadership sessions & compliance courses; often measured by attendance, satisfaction scores & budget utilisation rather than real business outcomes

In AI-first organisations, this old model dissolves

  • In AI-first organisations, L&D ceases to be an event-driven, HR-managed service

  • It transforms into an invisible, self-regulating, AI-managed capability system, seamlessly guiding individual growth & enterprise capability at the speed of market dynamics

  • AI transforms learning from a scheduled, instructor-led service to an invisible, hyper-personalised, competency-driven, always-on capability engine

  • Learning stops being a department; it becomes an intelligent, self-correcting system embedded within work, decisions & organisational design itself

The attempt here is to explore how L&D evolves from a nice-to-have support function to a core operational layer in AI-first organisations, permanently redefining how organisations build, deploy & sustain capability

Note:

  • This is Part 18 of a multi-part series where I simplify my research to make it accessible for non-IT professionals, a significant segment of the global workforce that often has a smaller voice in digital and social media, especially in conversations around AI

  • You can access the other parts of this series via my profile on Linkedin

The traditional L&D function

  • A calendar planner & administrator of physical & virtual training events

  • A provider of compliance, leadership & soft skills programs, often decoupled from real work

  • Dependent on self-reporting, post-training feedback forms & HR dashboards for measuring impact

  • Focused on generalised programs, training hours & annual up-skilling themes

  • Viewed as a service provider for managers, not a business-critical capability driver

But AI-first systems invert the above assumptions & dismantle this legacy structure

AI-first transformation

Stage 1: AI as a personalised learning enabler

  • AI systems analyse employee skill gaps, performance data & business priorities to recommend personalised learning journeys

  • Learning content becomes on-demand, modular & contextually surfaced within workflows

  • AI-powered learning management systems (LMS) suggest micro-learning, peer-driven content & live problem-solving opportunities

  • AI curates external courses, articles, podcasts & simulations based on individual roles, career goals & project demands

Impact:

L&D shifts from ‘one-size-fits-all’ workshops to individually tailored, AI-curated, continuous skill development paths

Stage 2: AI as a capability architect

  • AI operationalises live competency frameworks; dynamically matching people to projects, roles & tasks based on evolving skills

  • It designs role-specific, outcome-linked learning programs that evolve daily as business needs shift

  • Learning is fully integrated into project systems, performance reviews & operational dashboards

  • AI monitors skill acquisition rates, knowledge gaps & capability risk exposures in real time

  • It automates mentoring matches, peer-learning networks & expert-driven problem-solving sessions

Impact:

Learning stops being a department-led activity & becomes a core operational input; constantly adjusting supply of capability to match business demand

Stage 3: AI as a self-learning, enterprise-wide brain

  • L&D is no longer a separate function; AI systems track, predict & close skill gaps automatically

  • AI absorbs market signals, competitive shifts, customer feedback & internal performance data to update competency models & re-skilling priorities

  • Teams become self-policing, self-learning clusters, continuously guided by AI on what to learn next, with whom & for what business outcomes

  • AI automatically identifies obsolete skills, future roles & emerging talent gaps, adjusting workforce configurations accordingly

  • Human learning teams become AI ethics custodians, experience designers & learning system stewards; no longer content administrators

Impact:

Learning transforms into an invisible, embedded capability network, governed by AI systems that understand work, capability demand & market context better than human managers

The rise of competency-driven, AI-optimised learning

  • Competency models stop being static PDFs or HR policies; they become live, AI-managed systems linked to business performance

  • AI constantly updates these models based on market trends, customer data, competitive moves & internal metrics

  • Learning journeys are no longer linear or generic; they’re situational, role-specific, peer-influenced & business-integrated

  • AI monitors the enterprise’s skill supply chain, identifying capability bottlenecks, succession risks & high-impact up-skilling areas in real time

Impact:

Learning becomes a strategic operating lever, & not a support activity, directly tied to value creation, growth & organisational resilience

The end of classroom-led & HR-owned L&D

The old L&D model; built on calendars, training hours & reactive program design, disappears:

  • AI eliminates attendance-based learning metrics & self-reported assessments

  • Learning is integrated into real-time operations, decisions & product cycles

  • Obsolete training modules, irrelevant content & time-bound workshops become operationally meaningless

  • Capability management shifts from annual planning to continuous, AI-managed optimisation

What remains:

A small L&D governance layer focused on; AI system integrity & bias calibration, learning experience curation & marketplace partnerships, crisis learning interventions & ethical decision frameworks

Learning as a self-evolving, AI-directed system

  • Learning becomes continuous, hyper-personalised & deeply operationally embedded

  • AI manages capability supply chains as actively as finance manages cash flow

  • L&D shifts from being a support function to an intelligent infrastructure layer; enabling organisations to scale, pivot & innovate faster than human-led systems ever could

  • AI-first organisations will no longer teach employees what’s nice to know; they will equip them in real time for what the business actually needs next

Impact:

A world where people continuously learn, adapt & evolve; not because a manager or HR asks them to, but because the system around them actively shapes & accelerates their growth at the pace of market realities

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