Paradigm Shift in Corporate Learning

Chapter 2

Workplace Learning: Work and Learning Grow Together

"The industrial revolution gave us the 'assembly line theory' of education. According to it, learning takes place in special locations at times planned down to the minute well in advance, usually as frontal teaching with predefined curricula, the same for everyone. It is easy to make fun of this model. Most experts believe that despite its past achievements, it is bankrupt today."

In Future Learning, formal and informal learning are combined in a holistic concept into Workplace Learning – Next Generation.

2.1 On the Way to Workplace Learning – Next Generation

"The World Economic Forum assumes that 44% of employees will need to build fundamentally new skills in the next five years. In its current 'Future of Jobs 2023' report, it concludes that analytical and creative thinking, as well as artificial intelligence (AI) and the handling of big data, will be the most in-demand skills by 2027. Leadership qualities and social influence, along with curiosity and lifelong learning, will also become more important. Stockpiling knowledge and qualifications plays a subordinate role."

This trend is confirmed by other studies and the Haufe Akademie's Trend Radar (2024). According to this study, the key factors of future skills are:

1. Digital learning and technology-supported knowledge building
2. Knowledge management and competence development
3. Agile methodology and adaptive learning
4. Holistic leadership and organizational development

New forms and methods of working—New Work—are ubiquitous: in professional life, in old and new professions, in companies and organizations. Networking, complexity, digitization, artificial intelligence, agility, or self-organization are the key buzzwords. Behind all these forms are expanded requirements to act self-organized and creatively, to develop new competencies and the values that underpin them.

Employees can only acquire knowledge, competencies, and values in a way that impacts action. Cramming and memorization help little; a forward-looking form of learning—Future Learning—is required. This new learning world must mirror practice if employees are to be prepared for the future challenges of New Work.

Future Learning and New Work represent the future of learning and working (cf. Foelsing, Schmitz 2021a).

The development of corporate learning over the past three decades is characterized by five phases of digitization or digital transformation, i.e., the fundamental change in the business model of corporate education:

We have summarized our vision of future learning as follows:

Future Learning can no longer be separated from one's own development of values and competencies. Training and continuing education offerings are sought as needed for support and supplementation and integrated quickly, but they are not the center of learning. Actual ability to act is created through competencies. Values transformed into personal feelings form the real competence cores, providing motivation and orientation. Without feelings—without emotional change—there will be no effective learning in the future. Knowledge today is quickly available and increasingly curated, i.e., carefully and specifically selected for learners with the help of AI.

Learning in the workflow, due to high complexity, increasingly requires tackling challenges together with colleagues, learning guides, managers, or with support from experts. This exchange increasingly takes place online, giving learners broader networks. These processes are increasingly accompanied by virtual assistants who provide well-founded feedback and targeted solution hints. Future Learning therefore requires networking among human learning and cooperation partners and human-computer learning assistants.

Workplace Learning is the central strategy of the future because the nature of work continues to change fundamentally. Digital transformation and, in particular, artificial intelligence have taken the vision of Workplace Learning to a fundamentally new dimension.

In Workplace Learning – Next Generation, learning is integrated into the workflow in a self-organized way through dialogue with generative AI so that the required knowledge is provided on demand, curated to solve challenges in practice.

Consequently, learning will no longer occur when a course is available but when a challenge in the workflow must be solved. The previously practiced stockpiling of learning according to curricula in formal teaching/learning arrangements is replaced by targeted, dialogic learning on demand in the workflow.

Supply-driven, formal learning thus loses massive importance and is increasingly limited to foundational education and legally mandated measures, e.g., in safety.

This means that the roles of everyone involved change fundamentally. Learners organize their learning processes themselves; managers shift from supervisors to development partners of their employees; learning and development professionals become learning architects who primarily design the enabling space—the digital "learning house"—for self-organized learning; and trainers become learning guides who coach these processes.

For HR development and external education providers, this opens up new, attractive opportunities—provided they fundamentally change their business models.

A necessary prerequisite for Future Learning is acceptance among employees and managers.

2.2. The Learning Goal Pyramid Is Turned Upside Down

Why do we promote corporate learning? There is a simple reason: employees should be enabled to master current and future challenges in the workflow in a self-organized way. The problem is that future challenges are often not yet known today. This also applies to the methods and tools that will be needed.

With the previously practiced stockpiling of learning, this task cannot be accomplished. There is only one solution: we must enable employees to tackle their challenges independently. For this, they need the necessary attitude—values—and the ability to act, i.e., competencies.

We are currently experiencing a paradigm shift, massively reinforced by artificial intelligence, that turns the learning goal pyramid upside down. Curricula with fixed knowledge- and qualification-oriented learning objectives and content are gradually being replaced by "flipped curricula" (cf. Seufert 2024).

In this model, individual values and competencies (soft skills) form the goals of corporate learning.

Knowledge and qualifications remain necessary but are no longer the goal—rather, they are prerequisites. This means the required knowledge can be provided in curated form via AI.

Knowledge, qualification, and competence—as well as skills—are often used synonymously in everyday language. Skills, knowledge in the narrower sense, or qualifications will be necessary prerequisites but no longer the goal of employee development. Ultimately, what counts is the ability to overcome challenges in practice independently with the right attitude and to act effectively. This has fundamental consequences for corporate learning.

Values act as an internal compass. They steer decisions in action and do not develop through mandates but through reflected experiences and the consequences experienced.

Workplace learning is not a standardized process kept in reserve but part of the workflow. Values and competencies arise through doing—accompanied by reflection and dialogue, not through knowledge transfer or lab exercises.

2.3. Workplace Learning – Learning Design Is Turned Upside Down

Traditionally, corporate learning first "imparts" the necessary knowledge on a topic in a seminar or via e-learning modules. Exercises follow to "reinforce" this knowledge, along with tests or exam tasks. The development of values and competencies is sometimes initiated through transfer or practical tasks but often left to chance. This also explains the extremely low, mostly single-digit application rates of acquired knowledge.

The most important learning venue is the workflow because values and competencies can only be built independently while mastering real challenges.

In Workplace Learning, the sequence of learning design is turned upside down. Values and competencies are developed primarily through challenging practical tasks and projects in an integrated process with social learning and learning guidance. This enables personalized learning processes that can be supplemented by training measures and supported by continuing education.

This leads to the following sequence for learning design:

1. It starts with the question: in which practical challenges or practical projects can the desired soft skills be developed? Based on their skills diagnostics, employees agree on personalized learning paths in the workflow with their manager.
2. The second step clarifies which flanking support employees' self-organized learning processes need. Social learning with learning partners and colleagues plays a central role. Learning guides and experts—especially the AI learning partner—provide support and advice in these processes.
3. Only in the third step are these learning processes supplemented by training as needed. Method training, e.g., on Scrum, is suitable when practical tasks are carried out according to agile principles.
4. Supporting continuing education measures can build foundational knowledge and qualifications or spark the self-organized learning process.

Workplace Learning takes place in practice from the outset; work and learning grow together. Concepts to foster transfer become largely unnecessary. Responsibility for learning shifts to employees, supported by HR development and their manager. Training and continuing education serve supporting or complementary functions.

2.4. The Road to Future Learning

The Gartner study "2026 Top Priorities for CHRO" (Gartner 2025b) highlights the most important challenges and priorities in corporate learning for 2026:

Priority 1: Use AI to fundamentally transform HR

• Without a new HR business model, AI remains ineffective.
• HR needs an "innovation team" to systematically drive the reorientation of HR and the introduction of AI.
• HR roles must be fundamentally redesigned.
• Standard processes are increasingly taken over by AI-supported systems.

Priority 2: Shape human–machine collaboration

• Invest in differentiating competencies that competitors can hardly copy.
• Optimize the targeted deployment of employees in practice to achieve immediate impact.
• Enable purposeful collaboration between people and AI.

Priority 3: Mobilize leaders

• Make initiating and supporting change a core task for leaders.
• Adapt expectations for leaders and the values and competency models to the changed challenges.
• Enable targeted skills development for leaders.

Priority 4: Develop culture to strengthen performance

• Link corporate values to everyday work by mapping them in a value model with clearly described levels of expression.
• Describe the expected attitude with which employees act in practice.
• Initiate targeted development of values among all teams and employees in the workflow.
• Initiate targeted culture development at all levels.

This means a qualitative change in how we learn and leads to a fundamentally different business model for corporate learning—Future Learning in the form of Workplace Learning – Next Generation. Compared to traditional learning concepts, it has the following core characteristics:

Future Learning is defined by values and competence goals.

• Planning personalized learning processes is carried out independently by employees.
• Learning processes are determined by challenges in practice and in practical projects, which are handled collaboratively.

Learning happens in a triad:

The roles in the learning process change fundamentally:

• Learners are at the center and become architects of their personalized learning path.
• HR Development becomes a strategic learning architect that designs the enabling space (Learning Experience Platform – LXP) for self-organized learning (Learning & Development).
• Managers become development partners of their employees and enable their self-organized learning.
• Trainers take on the role of learning guides who enable and coach self-organized learning in the organization.

2.5. Enabling Space – Learning Experience Platform (LXP)

Values- and competence-based management no longer focuses on detailed planning of teaching/learning processes (planning fixation) but on enabling the self-organized development of knowledge, qualifications, values, and competencies in personalized learning processes (realization fixation).

2.6. AI in Corporate Learning

According to a current Boston Consulting Group study (2025), about 50% of employees and almost 85% of managers regularly use generative AI (GenAI) tools. Another study by Christoph Meier, SCIL (2025b), shows that the use of GenAI has also reached HR development. However, usage is concentrated on a few areas, especially developing learning materials and assessments and analyzing data (Handa et al. 2024). Few companies have yet seized the opportunity to use AI to drive a fundamental change in corporate learning (cf. Taylor/Vinauskaite 2025).

It is becoming clear that we have reached a "Rubicon" and are increasingly using AI as a multifunctional or even autonomous agent.

While AI has so far mainly optimized traditional teaching methods or individual processes, in the future it will shape entire process chains—from recruiting to targeted skills development—so that learning becomes increasingly networked and systemic. In the future, employees will steer AI agents that independently develop, accompany, and adapt learning paths. As a result, the learning culture will continue to change; learning will become personalized, adaptive, and dynamic.

2.7. Skills Diagnostics

It is therefore not enough to record what knowledge someone has, what qualifications they have completed, and which tasks they have performed. The decisive factors are soft skills, i.e., the attitude primarily based on internalized values, and the ability to act in order to solve as-yet-unknown challenges independently. That is why professional skills diagnostics are needed.

With AI support, competency diagnostics can now be used by every employee whenever they want to plan their own learning. To do this, a system is needed that adapts optimally to the needs of the organization.

This skills diagnostic system is usually designed as a rating system in which employees assess specific behavioral anchors, and the results are aggregated into a value or competency profile. For recruiting—where external assessments are not yet available—a ranking system is generally used that enables personal assessments of values and competencies without entering "desired" values, producing an objective picture.

An individual report can then be derived from this with the help of AI.

Employees can then, if necessary in dialogue with AI, set their learning goals based on these recommendations and optimize their personalized learning path.

This skills diagnostics approach enables:

• independent derivation of individual values and competence goals and the necessary hard skills for each role,
• self-organized design of personalized learning paths with suitable practical tasks and projects, social and professional learning support, and, if needed, training and continuing education.

This makes competency diagnostics scalable and enables an economical and effective implementation of the Future Learning vision.

Teams and the entire organization also exhibit specific competencies that develop in team-based or organizational actions. These can likewise be captured, analyzed, and evaluated to derive team or organizational development measures.

AI-based skills diagnostics:

• uses AI-based reporting to create a transparent foundation for every employee to independently diagnose their values and competencies,
• enables comparisons of individual or team profiles with others or with target profiles,
• forms the basis for value and competence goals at individual, team, and organizational levels,
• enables targeted, personalized value and competence development,
• creates the foundation for organization-wide skills management at all levels,
• makes transparent which competencies are most important for performance,
• highlights the spread of assessments at organizational and team levels by visually emphasizing the standard deviation (68% of responses).

2.8. Dialogic Learning with an AI Learning Partner

Corporate learning has often taken place in isolated formats: seminars, e-learnings, or workshops are centrally planned and delivered—often disconnected from actual needs and with little measurable transfer to practice. Yet in an increasingly dynamic working world, employees need knowledge precisely when challenges in the workflow must be mastered.

This is where generative AI comes into play. AI can act as a personal learning partner that supports employees through dialogic learning directly in their workflow. Instead of relying on static learning formats, your employees develop their competencies in direct exchange with AI—situationally, on demand, and in line with real practice.

In dialogic learning, the learning process begins not with stockpiled knowledge but with the specific problem in the workflow.

Employees gradually build the knowledge they need to tackle their problems through targeted communication with AI and apply it immediately in practice. If uncertainties arise, they can probe immediately and ask AI for further explanations.

To use AI optimally as an assistant that provides the inputs to shape learning independently, prompts play a key role. Dialogic learning requires starting learning processes with an initial question such as "What are the main causes of this problem?" Follow-up questions then build on each other to develop a solution. Learners co-develop definitions with AI, formulate further questions, and ask for examples or experiences. They clarify with AI how what they have learned could be applied in different contexts, explore relationships with other concepts, and reflect on their learning experiences.

This dynamic learning fundamentally differs from conventional methods.

Employees develop knowledge tailored exactly to their needs and follow personalized learning paths that reflect their prior abilities and career goals. Learning is needs-driven, fostering intrinsic motivation and creating immediate value in the workflow. Integration into daily work eliminates separate training times. Continuous use of AI strengthens digital skills and fosters a culture of self-organization in which employees learn autonomously and efficiently.

This leads to a significantly higher return on investment (ROI) in corporate learning and greater performance across the organization.

Dialogic learning is already feasible today through generative AI. The decisive factor, however, is embedding this approach in a holistic learning strategy.

2.9. Case Study – AI Learning Partner VIA

Organizations sit on a treasure that grows every day: project experience reports, decision templates, mission statements, recommendations for action, presentations, concepts, lessons learned—and especially the implicit knowledge of their employees. Yet in practice, this treasure often remains hidden. Knowledge exists but is not findable.

Learning platforms are usually geared to course catalogs, rigid learning paths, and formal training logic. In Workplace Learning, learning must organize itself out of work—individually, situationally, with targeted development of values and competencies, aligned with an organization's strategy and values.

VIA enables self-organized learning directly in the workflow, links it to value and competence goals, and makes knowledge in the company usable: through integrated skills diagnostics, human and AI-supported learning guidance, and values-based competence development.

Learning thus becomes not a separate process alongside work but a strategic part of work itself—combined with a learning culture in which employees take responsibility, have orientation, and can grow competencies in a targeted way.

VIA continuously accompanies employees and managers in everyday work:

• Dialogic coaching: As a personal AI coach, VIA is available 24/7 and personalized to each learner. Continuous support enables targeted development of values and competencies from knowledge.
• Skills diagnostics and development: Capturing values and competencies, analyzing and evaluating the results, and deriving recommendations for individual learning goals and personalized learning paths.
• Long-term memory: VIA documents learning steps, knows the learner's history, and incorporates it into its suggestions.
• Scenario techniques: Challenging situations, e.g., difficult conversations, can be realistically rehearsed in advance.

VIA becomes a personal development partner with expertise.

It draws on standardized content or on customers' own content.

As coach, curator, and career navigator, VIA curates knowledge from internal and external sources, provides it contextually in dialogue with learners, and generates appropriate learning and reflection impulses—from micro-training and podcasts to simulations. For the company, VIA supplies valuable data on competencies, skill gaps, motivation, and learning culture through continuous skills mapping—and makes learning in the workflow visible, measurable, and controllable. All GDPR requirements are met.

VIA is based on a networked multi-agent framework in which specialized AI agents collaborate, including:

• Skills diagnostics tools that diagnose, plan, and document work and learning,
• Planning tools for communication, documentation, scheduling, or project planning,
• Memory for safeguarding separate learner histories,
• Databases for structured data and content metadata,
• Content sources relevant to the target groups.

This modular architecture connects strategy, culture, work, and learning into a learning system in which VIA forms the interface between people, knowledge, and the organization.

As a result, roles also change: employees steer their own learning processes, managers act as development partners, HR developers design the digital enabling space, and trainers become learning guides.

Learning in the flow of work becomes reality.

2.10. Social Blended Learning

Employees in most organizations have been socialized in a learning world shaped by external organization. It therefore makes sense to meet them where they are and gradually and with guidance lead them toward self-organized learning through a social blended learning arrangement.

Targeted development of values and competencies within social blended learning arrangements has proven effective. Here, the formal design of blended learning is combined with social learning while working on a real practical project.

The learning process should start with the familiar kick-off in a seminar room or virtual room. The goal is not knowledge transfer but the consistent, binding planning of self-organized learning processes. Together with participants, a learning culture is created that is characterized by high commitment and mutual support in the form of learning partnerships, learning groups, or project journals.

Participants thus gradually build their competence for self-organized learning and working. Modern information technology provides the learning technologies that make values- and competence-oriented learning in the workplace possible in combination with e-learning, blended learning, and social learning.

Learners organize their personalized value and competence development process within the practical project or task agreed with their manager themselves—from defining goals to learning design to success control. They are supported by their learning partners (co-coaching), the learning group (peer consulting), learning guides (coaching), and their manager (mentoring). In communities of practice, participants can self-organize to exchange experiences from projects and develop them further together. They use the possibilities of the enabling space for learning design, self-organized knowledge building, online communication and collaboration, and obtaining feedback.

This learning architecture enables development processes characterized by values and competence orientation as well as self-organization, with practical projects and challenges in the workflow forming the "red thread."

In the self-organized development phases, participants combine formal and informal learning processes into a systematic value and competence development process. The use of digital learning tools, social media, and intensive, emotionally engaging personal conversations between managers and employees is crucial. This paradigm shift includes above all the following elements.

2.11. Efficiency and Cost-Effectiveness

Values- and competence-oriented learning in the workflow, supported by AI, is far more effective than formal learning, saves costs, shortens learning time, and delivers a higher ROI than classic seminars.

• High learning efficiency through targeted and individualized learning in dialogue with AI based on AI-based skills diagnostics,
• sustainable learning through the direct, self-organized application of knowledge in practice with the goal of personalized value and competence development,
• intrinsic learning motivation because learning takes place in employees' own challenging tasks in practice,
• improved performance through directly implementable solutions for one's own work processes (according to Learning Guild 2025, the error rate drops by up to 97%),
• significantly reduced costs because learning processes are self-organized with the help of AI and implemented in the workflow (according to Learning Guild (2025), for example, education costs fall by over 50%, onboarding time shortens by over 40%, and the error rate drops by 97%),
• targeted development of organizational and learning culture by enabling the development of values and thus the attitude of all employees,
• greater attractiveness as an employer thanks to value orientation and attractive, innovative development opportunities,
• lower turnover and improved employee retention (according to Learning Guild 2025 by 35%),
• precise talent selection and career counseling with the help of skills diagnostics.

Overall, the ROI of learning increases significantly.

Workplace Learning also helps to significantly increase employer attractiveness, because applicants are drawn to these learning opportunities. Skills diagnostics supports optimizing recruitment and career counseling. Employee retention improves; employees are retained and motivated to perform. Consistent skills management is therefore retention management.