As workforces grow more mobile across the Gulf states, learning systems struggle to keep up. People carry skills from site to site, but proof of capability often gets lost along the way. For L&D professionals supporting Gulf-facing or cross-border teams, Vardhan Kapoor and Shubham Choudhary share some handy practical solutions
Picture the scene: a technician completes safety training; an operator renews a licence a supervisor signs off competence. Then the worker changes site, employer or project, and suddenly none of it seems to count. For L&D teams supporting Gulf-facing or cross-border workforces, this is becoming a defining challenge. Not because training quality is poor, but because capability doesn’t travel well.
“Many organisations are discovering a quiet failure mode: skills exist, but proof doesn’t”
As economies across the Gulf countries of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE accelerate AI adoption, infrastructure buildouts and industrial diversification, the pressure on frontline capability has never been higher.
Yet many organisations are discovering a quiet failure mode: skills exist, but proof doesn’t. That gap is widening, but L&D teams can redesign learning systems, so that capability actually transfers, not just accumulates.
The overlooked problem is learning that only works ‘in place’. Traditional corporate learning assumes stability, such as:
- a fixed employer
- a single HR system
- a linear career path
- training that stays inside one organisation
That assumption no longer holds. Across the Gulf Cooperation Council (GCC) countries above, work is increasingly characterised by:
- project-based employment
- layered subcontracting
- labour mobility across sites and entities
- migrant-heavy workforces
- short ramp-up windows with high compliance stakes
In this environment, learning outcomes are only valuable if they can be unbundled and reconstructed with relevant, present-day applications to survive movement. Yet in practice, proof of learning is often:
- stored in internal LMS platforms
- issued as PDFs without verification
- labelled inconsistently across vendors
- disconnected from role eligibility, real-life applications or site access
- difficult to audit months later
The result is not just inefficiency. It’s a risk. Workers are retrained unnecessarily. Projects stall while documents are chased. Errors compound across contractors. And capable people are sidelined by missing or mistrusted records.
Why this matters more as AI adoption accelerates
AI is changing the nature of work, but not in the way most headlines suggest. In frontline, industrial, logistics, construction and facilities roles, AI adoption is increasing process complexity, not eliminating it. Systems become more automated, approvals faster, tolerances tighter. That raises the bar for workforce readiness.
However, someone still needs to:
- operate safely around automated systems
- implement stricter, relevant-to-the-present procedures
- identify and respond correctly when systems fail
- document actions and handovers precisely
This is why organisations are increasingly relying on workers with backgrounds in oil and gas, utilities, aviation and other high-discipline sectors. These workers already understand controlled operations, safety hierarchies and audit culture.
The learning challenge is not to turn them into AI experts. It is to translate and prove what they already know, in ways new systems and employers can trust.
From retraining to reclassification: an L&D mindset shift
A common response to workforce change is retraining. But retraining assumes a skills deficit. In many cases, the real problem is a classification deficit. Workers already possess relevant capabilities, but those capabilities are:
- not mapped to current role taxonomies
- not expressed in machine-readable ways
- not verifiable outside the original employer
- not recognised by downstream systems
For L&D, this suggests a shift in focus: From delivering more training to making capability legible across industries. That means investing as much thought into how learning is recorded, verified, and reused as into how it is delivered.
What portable capability requires (and what it doesn’t)
Portable capability does not require:
- a single global LMS
- radical reskilling programmes
- expensive credentials for every role
- replacing human judgment
It does require a few foundational design choices.
1. Define capability in operational terms
Instead of vague learning outcomes, focus on:
- what a worker is allowed to do
- under what conditions
- on which sites or systems
- with what renewal logic
This reframes learning as eligibility, not just education.
2. Separate evidence from storage
Learning evidence should outlive the platform that issued it. That means:
- consistent naming of credentials
- clear issue and expiry dates
- traceable issuers
- time-stamped verification events
When evidence is portable, workers don’t have to ‘start over’ every time they move.
3. Design for subcontracting reality
In complex supply chains, L&D must assume:
- training is delivered by multiple parties
- verification may happen elsewhere
- audits occur long after onboarding
Designing for this reality reduces friction and blame-shifting later.
4. Build correction, not punishment, into the system
Documentation errors are inevitable, especially in migrant-heavy environments. What matters is whether systems allow:
- fast correction
- human review
- transparent explanations
- worker access to their own records
This is as much an ethical issue as an operational one.
Where AI fits and where it doesn’t
AI can help with pattern detection, document validation and workflow automation. But for L&D leaders, the critical question is governance, not capability.
Before AI touches learning records or eligibility decisions, organisations should be able to answer:
- what decisions can AI influence?
- what evidence supports those decisions?
- who reviews edge cases?
- how can a worker challenge an outcome?
Existing regulatory signals from employment law to emerging AI governance frameworks are consistent on one point: automation does not remove accountability. Learning systems must therefore remain explainable, auditable and humane.
The opportunity for L&D leadership
The most effective L&D teams in the next phase of workforce transformation will not be those that deliver the most content.
They will be the ones who:
- make capability transferable
- reduce retraining waste
- protect workers from administrative exclusion
- support mobility without sacrificing safety
- translate experience into trusted proof
In short, they will treat learning not as an event, but as infrastructure that is tangible and traceable. Because in fast-moving, high-stakes environments, capability only matters if it can move in a way that is intact, trusted and ready to deploy.
Vardhan Kapoor and Shubham Choudhary are Co-Founders of FirstWork
