Most learning programs deliver identical content to everyone based on job titles or departments. Then L&D leaders wonder why engagement drops and skill application never materialises in actual work.
People have different strengths and development needs, but we train them identically.
Real personalisation means understanding that Sarah in accounting needs activities that challenge her analytical skills, while Mike in the same department requires simpler exercises to build foundational behaviours first. It means recognising that Jennifer has mastered delegation but needs practice with difficult conversations, while Tom excels at communication but struggles with prioritisation.
But most companies haven’t implemented this level of personalisation in their training, creating a significant opportunity for those ready to move beyond one-size-fits-all training.
Why Most “Personalisation” Efforts Fall Short
Some organisations recognise the need for more targeted training. They create different tracks for different roles, maybe implement adaptive assessments, or build learning paths that adjust based on quiz scores.
This approach treats personalisation like a branching flowchart: if manager, then leadership content; if poor performance, then remedial materials.
The problem is that skill development doesn’t work in flowcharts.
Consider two senior managers with similar experience levels. One has strong technical abilities but struggles with team dynamics. The other excels at building relationships but needs help with strategic thinking. Current “personalisation” systems would give them identical content because they share the same job title. It’s more segmentation than true personalisation.
AI-driven personalisation can recognise that they need completely different skill-building training activities.
Personalise On-the-Job Activities, not Classroom Training
Personalising classroom sessions is unscalable. The opportunity lies in on-the-job training activities. Learning retention after classroom training is notoriously poor, thanks to the Forgetting Curve.
There’s a massive opportunity to reinforce formal training with personal and practical on-the-job activities. It’s the activities that can be personalised for each learner, who can complete them in the flow of daily work to develop the habits and behaviours targeted in the overall training program.
What True AI Personalisation Looks Like
AI has opened new opportunities to transform personalisation from static segmentation into dynamic adaptation for each learner. Instead of relying on demographic data, AI systems can track actual performance patterns and skill applications through specific activities:
- Which activities someone completes successfully versus those where they struggle
- How quickly they master different types of skills
- When they’re most likely to apply new behaviours on the job
- What sequence of skill-building produces the best results for each person
This creates a competency profile that becomes more accurate over time. The system can observe that you consistently excel at analytical tasks but need more practice with interpersonal situations. It can be noticed that you apply new skills more successfully when they are built incrementally rather than jumping to advanced concepts.
The AI doesn’t just collect this data—it acts on it.
Your next activity targets your specific skill gaps at the right difficulty level, connects to competencies you’ve already developed, and focuses on behaviours you can practice immediately in your current role. The person sitting next to you gets different activities targeting their unique development needs.
The Business Impact Beyond Completion Rates
The real value comes from skill application. When people receive activities that match their current competency level and target their specific development needs, they’re more likely to practice new behaviours immediately.
More importantly, personalisation creates engagement that sustains over time. Generic training often produces initial enthusiasm that quickly fades. Personalised experiences maintain interest because the activities continuously target each person’s most relevant skill gaps.
Building Truly Adaptive Learning Systems
The shift requires both technological capability and mindset change. Too many companies approach AI as a content generator, but haven’t yet seen the opportunity for personalisation.
Here’s how to implement systems that actually personalise:
- Start with behaviour-based data collection. Use surveys from employees and their managers to identify which skills people apply successfully, where they struggle, and how quickly they master different behaviours.
- Build an activity library. Here’s where generative AI is your friend: create activities with multiple approaches to developing the same skills. This will allow an AI-decisioning model to match activity difficulty and focus to individual skill levels and needs.
- Enable continuous learning from performance patterns. The most sophisticated systems don’t just deliver different activities—they learn from every interaction to improve recommendations for everyone.
This collective learning creates a compound effect where the system becomes more effective over time.
Measuring Personalised Success
Demonstrating the value of AI personalisation requires measurement that goes beyond traditional training metrics. Organisations need to track skill development and application in actual work situations.
In addition to learner self-assessments on their own improvement, step it up and ask managers to do before and after assessments of their direct reports. Managers’ observations of behaviour change provide crucial validation of skill development and behaviour change.
Most importantly, the measurement needs to connect skill development to specific business objectives. Whether the goal is improved safety performance, better customer service, or enhanced leadership capabilities, personalised learning should demonstrate measurable improvement in the targeted competency areas.
The Future Belongs to Individual Development Experiences
Organisations that master AI personalisation will gain significant competitive advantages in talent development. They’ll see higher engagement in learning initiatives, better skill application in real work situations, and stronger connections between training investments and business outcomes.
True personalisation means treating each employee as an individual with unique competencies, skill gaps, and development needs. It means building systems that continuously adapt based on actual performance data rather than demographic assumptions.
The technology now exists to deliver this level of individualisation. The question for talent leaders is whether they’ll embrace the complexity required to implement it effectively, or whether they’ll remain satisfied with basic segmentation masquerading as personalisation.
For organisations ready to make the investment, AI personalisation represents the next evolution in corporate learning—one that can finally deliver on training that adapts to each person’s specific development needs.
