As roles become more complex, talent shortages intensify and transversal skills gain importance, the way organisations design training will be completely reshaped from 2026 onwards.
Traditional approaches—annual reviews, self-assessments and managerial impressions—remain useful, but clearly show their limits when it comes to anticipating real needs, allocating budgets effectively, and supporting employees in sustainable development paths.
Predictive analysis opens up a new way forward: it helps detect skill gaps before they start affecting performance, and highlights the most relevant development levers for each individual.
The goal is simple: to deliver better training, with less waste, and in a genuinely strategic way.
Understanding predictive analysis applied to learning & development
Predictive analysis uses reliable data—psychometric, behavioural, performance-based and contextual—to anticipate the skills an employee will need to strengthen to succeed in the future.
Unlike traditional approaches that focus on observation, this method anticipates, assesses importance, ranks priorities, and adapts training needs accordingly.
Its value is driven by several factors:
- Skills evolve faster than job descriptions.
- Organisations must optimise increasingly scrutinised L&D budgets.
- HR teams need objective, trackable decision-making.
- Managers rarely have enough time to assess needs in depth and design coherent individual plans.
The essential data behind a reliable predictive diagnosis. The quality of predictive analysis depends entirely on the quality of the data. A strong approach draws on several sources.
1. Psychometric data
These include personality, cognitive abilities, motivations and workplace behaviours. They form the scientific backbone of the diagnosis, helping anticipate attitudes, interpersonal style and how people handle stress or change.
2. Role-specific and job requirement data
Every role comes with its own critical skill set.
For instance: strategic thinking for a manager, analytical rigour for a data analyst, or persuasion for a B2B sales representative.
Predictive analysis compares the employee’s actual profile with the role’s success model.
3. Internal data: performance, feedback and 360° reviews
These inputs enrich the diagnosis by integrating past results, team feedback, managerial observations and career trajectories. They offer a dynamic, long-term view of skill development.
4. Semantic analysis of the employee journey
Language analysis (NLP) can reveal hidden skills, weak signals in day-to-day tasks, risk situations or repeated behavioural patterns. It’s a key lever for identifying under-used talent or anticipating future risks within the organisation.
The predictive method: step by step
Below is a complete step-by-step method, with a concrete example showing how predictive analysis accurately identifies training needs by linking individual data, job requirements and strategic priorities.
Step 1: Define the critical skills for the future
Before any analysis, the organisation must clarify its priorities for the year:
- Where do we want to go?
- What skills will be essential to deliver the strategy?
- Which roles are going to evolve?
- Which skills must teams strengthen rapidly?
For example, a B2B sales team aiming to accelerate growth and master consultative selling might identify four critical skills: assertiveness, persuasion, results orientation and adaptability. These become the foundation of the predictive model used to determine training needs.
Step 2: Assess employees using a shared, objective baseline
Predictions are only reliable if employees are evaluated with objective tools such as psychometric assessments, structured observation grids or performance metrics.
This shared baseline allows fair comparisons, gap measurement and consistent development plans.
Step 3: Detect gaps through predictive scoring
The model calculates the real probability of success in a given role, weighting each skill according to its impact on performance.
For example, for a B2B salesperson, the analysis may highlight low assertiveness and limited results orientation, but strong communication and interpersonal skills.
These gaps are critical because they affect the ability to close deals, handle objections and maintain overall performance.
The model therefore recommends targeted training such as closing techniques, negotiation or strengthening assertive behaviour.
Step 4: Prioritise the skills to develop
Training cannot cover everything at once.
To ensure a concrete and measurable return on investment (ROI), development actions must be prioritised based on:
- Impact on performance: which skills will deliver the quickest win?
- Effort required: which skills are fastest and easiest to develop?
For instance, assertiveness may be a rapid-impact lever and therefore a priority, whereas strategic skills—slower to build—require a longer-term development plan.
Step 5: Build a truly individualised development plan
Once gaps are identified and ranked, a personalised plan is created, fully aligned with the employee’s actual level and focused on priority improvement areas.
Recommended methods include targeted micro-learning, individual coaching, gamified activities, role-plays for real-life skill development, and blended learning to reinforce progress. Continuous monitoring helps adjust the plan as the employee grows and new needs emerge.
What predictive analysis should never become
To ensure an ethical, transparent and human-centred approach, predictive analysis must always be seen as a decision-support tool—not a substitute for human judgement.
It does not provide absolute truth: data and situations evolve.
It must be used strictly to support skills development and career management, never for sanction or exclusion.
When used correctly, predictive analysis helps invest in skills that truly matter, avoid generic training plans, and strengthen long-term team performance.
