Skills & Learning: the performance of the Learning ecosystem
No transformation without training
Whether chosen or imposed, transformations call for change from everyone in the organisation. Whether the aim is to make sense of your environment, absorb technological change, reinvent the business with artificial intelligence, understand regulatory developments or operationalise and roll out the organisation’s strategy, transformations succeed when they rely on training policies that are robust, efficient and engaging.
Training policies are one of the main conditions for innovation, agility and resilience. They call for proactive skills strategies, built on an engaging and transformative Learning ecosystem.
Towards an augmented Learning ecosystem
As AI redefines business models, roles, production methods and the ways we learn, Learning has to support the transformation of other functions while absorbing the changes that reshape its own:
- identifying, almost in real time, the skills at risk or becoming obsolete;
- proposing reskilling and upskilling action plans and tackling deskilling;
- embedding learning in the flow of everyday work;
- generating contextualised, personalised content, tutorials and support;
- personalising learning journeys;
- steering learning through data;
- moving from managing training a posteriori to predictive management.
Anticipating the evolution of roles and skills
Identifying technological, economic, regulatory, environmental and organisational changes makes it possible to gauge their effects on activities and roles.
This analysis then helps to pinpoint strategic skills, to quantify the gaps between available resources and future needs, and to build suitable responses: training, upskilling, reskilling, recruitment, mobility, career change or certification.
Measuring training performance
Measurement makes it possible to verify the real effects of each programme: skills acquired, transfer to the workplace, changes in practice, impact on business or HR objectives, and the consistency between results achieved and resources committed.
It turns evaluation into a management tool to compare programmes, adjust learning journeys, prioritise investment and continuously improve the quality of training.
How does Alixio support the performance of the Learning ecosystem?
Alixio has defined a three-dimensional Learning model:
Strategy
Experience
Data & Steering
It draws on nine levers for action that make it possible to measure the maturity of the training ecosystem, develop its performance and ensure it contributes effectively to the company’s strategic projects.
The 9 levers of Learning performance
Learning Ambition
The Learning Ambition is the medium-term policy framework (three years minimum) that sets out the company’s vision of Learning, the mission it is given and the priorities that define its action.
It covers, among other things:
- alignment between the company’s strategy, its AI ambitions and its skills challenges;
- clear, strong Learning priorities, such as supporting the redefinition of each role’s added value in the face of AI, embedding AI in the Learning production chain and developing a culture of experimentation;
- a medium-term trajectory, with adaptive milestones rather than a fixed plan;
- the resources, AI skills, IT architecture and partnerships required.
Shared Learning Governance
Putting a shared governance charter in place makes it possible to organise, regulate and formalise the relationships between the Learning team and its internal clients.
It lists the stakeholders, allocates roles, defines the governing bodies and sets out the rules of production. It is tripartite:
- Learning provides the adoption methodology;
- the IT department provides the technology;
- the business functions provide the use cases.
In particular, it formalises the rules for co-design, co-validation and co-deployment, as well as the rules for using AI in production.
HR Integration
Integrating Learning is no longer limited to the planned touchpoints of the employee journey.
AI enables a continuous, contextualised presence of learning in everyday work: real-time recommendations, automated nudges, contextual help and micro-training triggered by actual use.
This rests on a map of learning activity that distinguishes:
- the structural activations tied to the stages of the journey: recruitment, onboarding, mobility, knowledge transfer;
- the continuous activations: learning in the flow of work, predictive recommendations and suggestions based on behaviour or on the skills gaps identified.
Learning Marketing
Learning Marketing aims to build the appeal of training.
AI shifts a supply-led approach (‘here is our catalogue’) towards a marketing of latent demand: spotting needs and offering the right training, at the right time, in the right format.
This approach, which lies at the heart of an organisation’s learning culture, addresses:
- the structure and clarity of the offer;
- individualisation through personas and behavioural segmentation;
- predictive recommendations;
- the distribution of the offer and contextualised push notifications;
- a multichannel communication plan, with continuous optimisation.
Training Experience
In the contest for attention, training has to become an experience. A formal, distinctive teaching signature is its foundation, carried and championed by every trainer.
It defines the various components of the act of training: the trainer’s stance, design, delivery, assessment and the design of the modules.
And as knowledge becomes available to the many, it incorporates a framework for using AI in design and delivery:
- what AI produces;
- what people validate;
- the rules for quality and traceability;
- teaching responsibility for the content generated.
Learner Experience and Engagement
The mirror image of the training experience (what Learning offers), the learner experience sets out what each person should go through.
It is the source of impact and of lasting memory.
It is the condition for an engaging experience.
It rests in particular on:
- seamless journeys, personalised by AI;
- a blend of timings and formats;
- AI-enhanced content recommendations;
- gamification;
- continuity between physical and digital spaces;
- augmented social learning;
- contextualised nudges and micro-activations.
Data Performance
The Performance dashboard is designed to assess the operational performance of Learning.
It is built on three levels of indicators:
- the Core indicators: trainees, trainee-hours, take-up, learning mix, satisfaction, NPS;
- the Expert indicators: time to deploy, appeal of the offer, fill rate, digital engagement, cost structure;
- the AI indicators: relevance of recommendations, semantic analysis of evaluations, content-quality scoring and speed of adoption.
It is the living tool that makes it possible to detect weak signals and suggest corrective actions, both reactively and proactively.
Data Impact
The Impact dashboard measures the gains delivered by training, both for learners and for the company.
It includes, among other things:
- the time taken to build skills;
- the rate of application in practice;
- internal mobility and turnover rates;
- changes in business performance indicators;
- cultural impact;
- coverage of strategic skills;
- the prediction of impact before a training programme is fully rolled out.
AI makes it possible to shorten the time to proof, provide leading indicators and model the links between training and business performance.
Learning Economic Model and Funding
Learning rests on an economic model that has to reconcile robustness and agility.
And in an ever-tighter budgetary context, it must rely on a particularly robust funding strategy in order to take account of:
- the place and weight of Learning within the company’s budget;
- the cost structure, reviewed in the light of AI;
- internal rebilling strategies;
- external funding;
- employment and training schemes;
- the procurement strategy;
- the choices around outsourcing and in-housing.
While AI reduces certain engineering and production costs, it also introduces new areas of spending: tools, licences, in-house skills and data governance.
It also opens access to new funding linked to innovation and digital transformation. These are all new parameters to factor in.
Our approach: from diagnosis to improvement plan
Whatever the scope of the engagement, Alixio’s approach is generally built on 4 formal phases:
Self-assessment
Assess the maturity of the Learning ecosystem.
Interviews
Gather the views of the main stakeholders.
Analysis of the existing ecosystem
Analyse existing resources, programmes and data.
Improvement plan
Set out the recommendations needed and propose the improvement actions.
Transform the performance of your Learning ecosystem
Our Skills & Learning support makes it possible to identify the skills needed for tomorrow, ensure they are acquired and create the conditions for transformation.
Q&A – Learning ecosystem performance
What is the Learning Ambition?
The Learning Ambition is a medium-term policy framework that sets out the company’s vision of Learning, its mission and its areas of action. In particular, it makes it possible to align Learning with the company’s strategy and its AI ambitions.
What are the levers of Learning performance?
The model draws on nine levers: Learning Ambition, Shared Governance, HR Integration, Learning Marketing, Training Experience, Learner Experience and Engagement, Data Performance, Data Impact and Learning Economic Model and Funding.
What is the role of AI in Learning?
AI represents a twofold challenge for Learning. When access to knowledge becomes instant and unlimited, it is judgement and situational intelligence that become the new critical skills. This is a genuine revolution to lead, while also… making sense of and supporting the one that affects every function among their clients, both internal and external.
How do you measure the impact of training?
The Impact dashboard measures the gains associated with training: time to build skills, application in practice, mobility, business performance, cultural impact and coverage of strategic skills. The AI indicators also make it possible to model expected effects before the full roll-out.
Why define a Learning funding strategy?
A formal funding strategy aims to build a clear, shareable budget architecture, optimise existing management, tap into under-used funding and capture the funding linked to AI and digital transformation.
