What is Learning in the Flow of Work?

Employees face a paradox: they must continuously improve their skills while maintaining their daily performance. Faced with this reality, a new paradigm is emerging: Learning in the Flow of Work, conceptualized by Josh Bersin. This approach is revolutionizing corporate training by integrating learning directly into the daily workflow of employees.

How can this concept be transformed into operational reality? How can informal learning be effectively structured to make it a real performance driver? How to measure its impact on skills development in concrete terms? That's what we're going to see together.

Understanding Learning in the Flow of Work

Learning in the Flow of Work, a concept theorized by Josh Bersin in 2018, responds to a fundamental observation: learning is more effective when it is integrated naturally into daily professional life.

This approach radically transforms the classical training paradigm. Rather than extracting employees from their work environment to train them, it integrates learning directly into their daily tasks.

The objective? Enabling employees to access relevant knowledge exactly when they need it, in their specific professional context.

The limits of informal, unstructured learning

Informal learning, although natural and omnipresent in business, has significant limitations when it is not structured:

• Lack of knowledge validation : Without an established framework, it is impossible to guarantee the quality and relevance of the information acquired.
• Lack of traceability : The skills developed often remain invisible to the organization, creating a “blind spot” in talent management.
• Risk of dispersion : Employees can waste valuable time looking for the right information or, worse, acquire inadequate practices.

The ingredients for learning that works

For the magic of Learning in the flow of work to work, learners should:

1. Spend a lot less time looking for information
2. Use the right strategies to learn
3. Have the opportunity to practice in a virtual environment before moving into a real situation,
4. Get a lot more feedback

By respecting these 4 principles, learning in the workflow will be a real success.
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How to implement Learning in the Flow of Work?

The effective implementation of Learning in the Flow of Work requires a methodical and structured approach, based on three fundamental axes

What are the prerequisites and the methodology required?

Contrary to what one might believe (or hear), mapping learning moments or perfectly aligning educational content with all learners' learning needs are not essential prerequisites to start implementing learning in the flow of work. Indeed, these 2 points even benefit from being built iteratively as the process progresses, as uses bring out the needs.

On the other hand, to learn in the workflow, it is essential to have a smooth integration of learning tools into the existing technological ecosystem and to have an initial knowledge base or training from which to build learning experiences.

A three-step approach is required for better integration:

1. Make the learner aware of identifying their own needs and give them the means to access the help they need when they need it at the place
2. The deployment of tools that allow contextual access to resources. One LMS/LCMS Classic is not enough: you need tailor-made content that starts from the specific problem that a learner is facing at a specific time.
3. The establishment of a continuous feedback system to refine educational interventions.

For example, if a learner wonders where company data is stored or encounters specific difficulties in their position (such as “I don't know how to resolve this objection from my prospect”), we are not going to offer them a generic 10-minute training module but formats that are more adapted to their current needs.

AI: the technology that facilitates Learning in the Flow of Work

Artificial intelligence plays a central role in this transformation, in particular through:

• AI assistants that can anticipate learning needs
• Contextual content recommendation systems
• Tools for monitoring progress in real time

It's important to remember that without AI, learning in the workflow would be nearly impossible and would require a lot of resources.

Following the previous example, an educational AI can respond precisely to the learner where the data he needs to know the location of is located, then generate a micro-training exercise where he must explain this location to an interlocutor (a colleague, an external partner, a DPO, etc.)
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Measuring the effectiveness of Learning in the Flow of Work

Measuring the effectiveness of Learning in the Flow of Work requires a multidimensional approach, going beyond traditional training indicators.

What's at stake? Capturing the reality of learning that integrates organically into daily performance.

What are the key performance indicators?

To properly measure its effectiveness, it will be necessary to analyze:

• Access time to critical information : A measure of how quickly employees find relevant resources
• The rate of immediate application : Assessment of the direct application of acquired knowledge
• The impact on productivity : Analysis of the reduction of downtime related to information searches

Assessment of memory anchoring

The effectiveness of this approach is particularly evident in the retention of knowledge. Studies show a significant increase in memory retention when learning is integrated into the immediate professional context.

Organizational impact

• Reduction in the time to develop skills,
• Improving the autonomy of employees,
• Optimization of Knowledge Management.

Didask's role in Learning in the Flow of Work

Artificial intelligence is radically transforming the dynamics of Learning in the Flow of Work, creating a synergy between technology and pedagogy.

The AI assistant: a contextual educational coach

Our AI assistant will be able to:

• Based on the questions asked by the learners, propose an activity that meets their immediate needs (for example, helping them write their email for a project partner), generally a quick simulation.
• Offer more successful immersive training, where AI coaches learners around the completion of a complex task.
• If and only if the AI detects that a prerequisite is missing, the assistant will be able to provide precise recommendations for the training module.

Cognitive personalization at scale

AI allows for the fine adaptation of learning paths, taking into account:

• A pace of acquisition specific to each learner
• Individual learning patterns
• Specific constraints of the position held

From theory to practice

Integrating our assistant into the workflow turns every learning moment into an opportunity for concrete development:

• Instant identification of relevant resources,
• Immediate and personalized feedback.

The future of professional learning

Faced with the challenges of digital transformation and the constant evolution of skills, Learning in the Flow of Work is emerging as an essential strategic response.

The integration of artificial intelligence into this process marks a decisive turning point. It makes it possible to reconcile two apparently contradictory imperatives: the extensive individualization of learning and large-scale deployment. Technology is thus becoming the catalyst for a profound transformation of training practices.

Companies that know how to capitalize on this approach will have a decisive competitive advantage. It is no longer simply a question of training, but of creating an ecosystem of continuous learning, where each employee becomes an actor in their professional development.
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