AI and Technology in Professional Training: What Changes in 2026
March 4, 2025 · Education · 9 min read
Technology is changing professional learning, but the useful question is not whether every organisation should adopt the newest platform. The useful question is which technology improves a specific learning or performance problem, for which employees, under which conditions.
In 2026, artificial intelligence has become central to this discussion. AI can help organisations create content, personalise practice, identify skill gaps and provide faster feedback. At the same time, it introduces new risks around privacy, bias, accuracy, over-reliance and assessment integrity.
A strong technology-enabled learning strategy therefore combines speed with governance, human judgement and workplace application.
Why technology matters more now
The World Economic Forum’s Future of Jobs Report 2025 found employers expecting major skill disruption through 2030, with AI and big data, cybersecurity, technological literacy, creative thinking and adaptability among the areas rising in importance.
Source: World Economic Forum, Future of Jobs Report 2025
This creates two related learning challenges:
- employees need new skills faster;
- learning teams need more agile ways to identify, build and apply those skills.
Technology can help with both, but only when it is connected to real work.
AI is now both a learning topic and a learning tool
Employees increasingly need AI literacy for their roles, while L&D teams are also using AI inside learning systems.
LinkedIn’s 2025 Workplace Learning Report found that 71% of surveyed L&D professionals were exploring, experimenting with or integrating AI into their work. The report also emphasised skills-based career paths, internal mobility and faster upskilling.
Source: LinkedIn Workplace Learning Report 2025
Common uses include:
- drafting learning content;
- creating practice scenarios;
- generating quizzes or reflection prompts;
- supporting coaching conversations;
- recommending learning resources;
- summarising knowledge;
- identifying skill gaps;
- providing just-in-time assistance during work.
Do not confuse faster content production with better learning
AI can make it easier to produce more slides, courses, quizzes and videos. That is not automatically an improvement.
A learning team can create more low-value content faster if it does not start with a clear performance need.
Before using AI to generate training, ask:
- What should the learner be able to do differently?
- Is training actually the right intervention?
- What practice will be required?
- Which information must be accurate and verified?
- How will workplace application be measured?
AI-assisted personalisation can be useful when the data is meaningful
Learning platforms can adapt recommendations based on role, skill data, assessment results or learning behaviour.
Personalisation becomes useful when it helps answer practical questions such as:
- Which skill gap is most relevant to this role?
- Which prerequisite is missing?
- Which practice task should come next?
- Which internal opportunity could use this skill?
Personalisation based on weak or inferred data can also create irrelevant recommendations, so employees should be able to understand and challenge important profile assumptions.
Skills data can connect learning to workforce planning
Modern L&D is increasingly connected to a skills system rather than only a course catalogue.
A skills-based approach can link:
- job roles;
- required capabilities;
- current employee skills;
- learning resources;
- projects and assignments;
- internal mobility;
- future workforce needs.
LinkedIn’s 2025 learning research highlights skills gap data, skills-based career paths and internal mobility as practices associated with more mature career-development systems.
The main challenge is data quality. A skills taxonomy that is outdated or too generic can make the whole system less useful.
AI coaching can increase access to practice, but it is not the same as human coaching
AI tools can simulate conversations, provide structured prompts and offer immediate feedback. That can be useful for repeated low-risk practice.
Examples include:
- sales conversation rehearsal;
- manager feedback practice;
- interview preparation;
- presentation practice;
- language practice;
- scenario-based decision exercises.
For high-stakes development, employees may still need human coaching to interpret context, relationships, organisational politics and nuanced judgement.
Simulation remains powerful when real-world practice is risky or expensive
Simulation technology is especially useful where mistakes in the real environment would be unsafe, costly or difficult to arrange.
Examples can include:
- aviation;
- healthcare procedures;
- industrial safety;
- equipment operation;
- emergency response;
- complex customer or leadership scenarios.
The value comes from realistic practice and feedback, not from novelty alone.
VR and AR should solve a real practice problem
Virtual and augmented reality can create immersive practice environments, but they are not automatically superior to simpler methods.
Before investing, ask:
- Does spatial or physical practice matter?
- Is the real environment dangerous, rare or expensive?
- Will immersive practice improve fidelity?
- Can the organisation maintain the hardware and content?
- Is there a simpler method that would achieve the same objective?
For many knowledge tasks, a well-designed scenario or supervised practice may be more efficient than immersive hardware.
Mobile learning is useful when work is distributed
Mobile learning can support employees who are travelling, remote, field-based or working away from a desk.
Useful mobile formats include:
- short reference guides;
- checklists;
- brief videos;
- practice questions;
- job aids;
- spaced reminders;
- performance support at the point of need.
The goal should be access and usability, not simply converting a desktop course to a smaller screen.
Microlearning is a format, not a learning theory
Short learning units can be useful for:
- refreshing knowledge;
- reinforcing key points;
- supporting a larger programme;
- delivering just-in-time instructions.
They are less suitable when the learner needs extended reasoning, sustained practice or complex integration of multiple skills.
Do not claim that shortening content automatically produces a fixed percentage increase in retention or learning speed.
Learning in the flow of work can reduce the gap between course and application
Performance support places useful guidance close to the moment of need.
Examples include:
- embedded software guidance;
- decision checklists;
- knowledge assistants;
- searchable procedures;
- AI-supported help systems;
- manager prompts before important conversations.
This can be especially useful for tasks that employees perform infrequently and may not remember from formal training.
Learning analytics should answer a decision question
Technology makes it easy to collect completion, click and viewing data. Much of it has little value by itself.
Useful analytics should help answer questions such as:
- Which capability is still weak?
- Where are learners getting stuck?
- Who needs additional practice?
- Which programme is associated with workplace application?
- Which role is experiencing a critical skill gap?
Completion rate is an activity measure, not proof of performance improvement.
AI-generated learning content requires verification
Generative AI can create plausible but incorrect information. The risk is especially serious in legal, health, safety, finance, compliance and technical learning.
A responsible process should define:
- which content requires subject-matter review;
- which sources are acceptable;
- how factual claims are checked;
- how updates are managed;
- when AI use should be disclosed;
- who remains accountable for the final material.
AI governance belongs inside L&D governance
NIST’s AI Risk Management Framework is designed to help organisations manage risks throughout the AI lifecycle. It emphasises governance, mapping context, measuring risk and managing risk rather than treating trustworthiness as a one-time technical test.
Source: NIST AI Risk Management Framework
For L&D, relevant questions include:
- What employee data is the AI system using?
- What decisions does it influence?
- Can a learner challenge or correct an AI-generated profile?
- Does the system create unequal outcomes?
- Is human review required for high-impact decisions?
- How are errors monitored?
Diversity and inclusion matter in AI-enabled learning systems
UNESCO’s Recommendation on the Ethics of Artificial Intelligence places human rights, fairness, inclusion, transparency and human oversight at the centre of responsible AI governance.
Source: UNESCO Recommendation on the Ethics of Artificial Intelligence
Learning teams should test whether content and systems work fairly across relevant languages, locations, accessibility needs and employee groups.
Protect employee privacy
AI-enabled learning systems may process:
- assessment scores;
- career interests;
- skills profiles;
- manager feedback;
- learning behaviour;
- work samples;
- conversation transcripts.
Do not collect data simply because the platform can. Define the purpose, access rules, retention period and lawful basis appropriate to the relevant jurisdiction.
Assessment integrity changes when AI is available
If employees can use generative AI during work, banning AI from every learning assessment may create an unrealistic test. But if the objective is to verify individual knowledge or regulated competence, unrestricted AI assistance may invalidate the assessment.
Assessment design should therefore clarify:
- what capability is being tested;
- whether AI use is allowed;
- what must be completed independently;
- how reasoning or practical performance will be verified.
Do not automate consequential development decisions without oversight
An AI recommendation about learning content is lower risk than an automated decision that affects promotion, employment or access to opportunity.
High-impact decisions need stronger governance, transparency and human accountability.
Technology should support training transfer
The most useful learning technology helps employees apply skills at work.
Examples include:
- practice before a real task;
- manager prompts after training;
- spaced reinforcement;
- job aids;
- coaching support;
- workflow guidance;
- follow-up assessments based on actual work.
See MATSH’s Training and Job Performance guide for the transfer framework.
A practical technology-selection framework
Before buying a learning technology, score it against:
- Learning problem: What problem does it solve?
- User group: Who will use it?
- Practice quality: Does it improve real practice?
- Workflow fit: Can employees use it at the right moment?
- Evidence: What outcome is supported?
- Integration: Does it work with existing systems?
- Accessibility: Can relevant employees use it?
- Privacy: What data does it require?
- Governance: Who is accountable for AI output and decisions?
- Total cost: Licensing, implementation, content, support and maintenance.
Run pilots before enterprise-scale rollout
For emerging technology, a controlled pilot can test:
- usage;
- learning quality;
- employee trust;
- technical reliability;
- manager experience;
- privacy and governance issues;
- workplace application.
Define success criteria before the pilot begins.
What the future of professional learning is likely to look like
The direction is not simply “more online learning”. The stronger shift is toward:
- skills-based learning;
- AI-assisted practice;
- career-linked development;
- learning embedded into work;
- faster content iteration;
- more individualised support;
- better connection between learning and internal mobility;
- stronger AI governance.
The organisations that benefit most will be those that combine technology with good diagnosis, relevant practice and human management.
MATSH and technology-enabled learning
MATSH uses digital and live learning formats according to the capability being developed. Technology can expand access and practice, but programme design still needs clear outcomes, relevant evidence and a path from learning into work.
Frequently asked questions
Will AI replace corporate trainers?
AI can automate or accelerate parts of content creation, practice and support. Human trainers remain important for facilitation, judgement, complex feedback, context and high-stakes learning.
Is VR better than classroom training?
Not universally. VR is most useful when immersive or physical practice adds value. The right method depends on the learning objective.
What is the biggest risk of AI in workplace learning?
There is no single risk. Important risks include inaccurate content, privacy, bias, over-reliance, opaque recommendations and inappropriate automation of consequential decisions.
How should L&D teams use AI responsibly?
Use defined governance, verify factual content, protect employee data, keep humans accountable and evaluate whether the technology improves learning or workplace performance.
Does personalised learning always improve performance?
No. Personalisation can make learning more relevant, but performance still depends on content quality, practice, manager support, workplace opportunity and the accuracy of the data used for personalisation.
Sources
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