5 initiatives to strengthen experiental learning
Experiental learning has become one of the most powerful strategies for improving knowledge management in organizations. In a rapidly changing business environment, organizations that prioritize learning through real-life experiences are the ones that gain sustainable competitive advantages. This article explores how to implement experiential learning strategies, using practical examples, to boost efficiency and foster innovation in companies.
Why is Experience-Based Learning Important?
Learning through experience allows employees and teams to learn from their successes and failures, capturing valuable knowledge that can be shared and applied to improve outcomes. The most valuable knowledge in organizations doesn’t just come from data or information, but from the daily experiences of the workforce.
1. Knowledge Days: A Tool for Sharing Organizational Wisdom
Knowledge Days are an effective practice used by companies like Hatch to encourage the transfer of experiental learning. These events are designed for employees to share their professional experiences and lessons learned during project execution. By doing so, companies can capture and disseminate tacit knowledge, transforming it into an accessible organizational asset.
Knowledge Days are an excellent way to foster collaborative learning and ensure that the lessons learned in one project benefit other teams across the organization. This practice helps companies increase efficiency and improve decision-making based on experience.
2. Technology Forums: Collective Experiental Learning for Problem Solving
Technology forums are another powerful strategy for promoting experience-based learning. These spaces allow employees to discuss technical challenges, share solutions, and learn from similar experiences in other areas of the company.
An example of this approach is Cotecmar, a company in the naval industry that uses technology forums for its engineers and technicians to share experiences and solutions for complex projects. This type of collaborative experiental learning not only improves operational efficiency but also fosters a culture of continuous improvement.
3. Communities of Practice: Networks for Experience-Based Learning
Communities of Practice (CoPs) are networks of employees with shared professional interests who come together to exchange experiences, knowledge, and best practices. In Bancolombia, for example, CoPs have been essential in fostering learning in key areas like risk management and technological innovation.
These CoPs allow employees to learn from others’ experiences, sharing valuable lessons that help improve internal processes. This type of experiential learning not only fosters collaboration but also enables tacit knowledge to be formalized and shared across the organization.
4. Lessons Learned: Formalizing Experiental Learning to Improve Decision-Making
Lessons Learned are a key tool for capturing experience-based learning from work teams. At YPF, for example, project teams gather at the end of each initiative to analyze what went well and what could be improved, documenting these learnings in a centralized database.
This approach ensures that lessons learned are available for future projects, allowing the organization to continually improve and avoid repetitive mistakes. By capturing and sharing experience-based knowledge, companies can enhance operational efficiency and the quality of work.
5. Storytelling: Learning Through Narratives in Knowledge Management
Storytelling is a knowledge management strategy that allows employees to share meaningful experiences through narratives. In the Singapore Air Force, storytelling is used for officers to share lessons learned during critical missions, helping new officers learn from past experiences.
Storytelling is a powerful tool for transmitting organizational knowledge, enabling companies to capture tacit experiences in a more accessible and applicable way. By integrating this practice, organizations can ensure that lessons learned in critical situations are shared and applied effectively.
Conclusion
Experience-based learning is fundamental for knowledge management in modern organizations. By implementing practices like Knowledge Days, technology forums, Communities of Practice, Lessons Learned, and storytelling, companies can capture, share, and apply experience-derived knowledge, improving decision-making and fostering a culture of innovation and continuous improvement.
The success of these actions depends on the company’s ability to integrate accumulated knowledge into its processes and create a culture where continuous learning is the driving force behind growth. By implementing these strategies, companies not only optimize their efficiency but also strengthen their ability to adapt and thrive in a constantly changing business environment.
If you’d like to explore any of these initiatives further or learn more, feel free to contact me at [email protected], or check out my latest book, “Así se Gestiona el Conocimiento, Experiencias y Casos Prácticos en Empresas“.
AI-Powered Content Curation: Success Stories in Knowledge Management
In the digital age, where information overload represents one of the greatest challenges for organizations, content curation with artificial intelligence has become a strategic tool for optimizing knowledge management.
The ability to filter, organize, and distribute relevant information not only improves decision-making but also drives innovation across all areas of the company.
This article explores how large corporations are leveraging artificial intelligence (AI) to enhance content curation and highlights success stories that demonstrate the tangible results achieved.
What is content curation with artificial intelligence in Knowledge Management?
Content curation is the process by which relevant and useful information is identified, organized, and presented from large volumes of data. In the context of knowledge management, curation enables companies to maximize the value of their data and turn it into actionable knowledge, thereby increasing organizational productivity and competitiveness.
AI-Driven Content Curation: A Disruptive Approach
Artificial intelligence (AI) has revolutionized the content curation process within knowledge management. Technologies such as natural language processing (NLP) and machine learning algorithms allow for automating the collection, classification, and presentation of key information. This not only significantly reduces the time spent on manual tasks but also enhances the accuracy and relevance of content, giving companies a competitive edge in complex environments.
Success Stories: AI-Powered Content Curation
Success Story 1: Optimizing Technical Knowledge in an Energy Company
An energy sector multinational faced the challenge of managing large volumes of technical information accumulated over years. By implementing an AI-powered content curation solution, the company automated the identification of critical technical reports and operational documents. AI enabled the extraction and organization of key data, allowing employees real-time access to essential information.
Results:
- 40% reduction in time spent searching for critical information.
- Improved collaboration among technical teams.
- Accelerated innovation in operational processes.
This case illustrates how AI-driven content curation transforms knowledge management in technical industries, optimizing workflows and competitiveness.
Success Story 2: Automating Content Curation in the Banking Sector
A leading European banking institution implemented a knowledge management system to automate content curation for its financial analysis and risk management teams. Thanks to artificial intelligence, the bank automated the collection of financial reports and market news, providing customized dashboards with the most relevant information for each area.
Results:
- 30% reduction in decision-making response time.
- Improved risk analysis accuracy, aligned with financial market requirements.
Success Story 3: Knowledge Encoding and Reuse at Ternium
Ternium, part of the Techint group, implemented a technical knowledge curation method by segmenting information into “knowledge objects.” These are small content units, such as documents or brief explanations, designed for easy reuse. This strategy ensures consistency across multiple plants and reduces errors, providing detailed and reusable knowledge in a standardized format.
Success Story 4: Knowledge Management through Storytelling at Ecopetrol
At Ecopetrol, content curation is done through “Success Stories,” where the best operational practices are shared in an attractive visual style, similar to a comic. This format has proven particularly effective for communicating with entry-level employees working at oil wells, where traditional computer-based methods were not feasible.
Success Story 5: Effective Use of Knowledge Maps at YPF
At YPF, content curation is supported by detailed knowledge maps that visually align knowledge needs with organizational resources. These maps allow employees to identify critical areas and ensure curated information is accessible and integrated into organizational processes.
Success Story 6: Knowledge Socialization at Aenza
Engineering company Aenza uses curated content captured in “knowledge books” and forums to facilitate knowledge exchange among engineers. The company also provides editorial support to help engineers document their experiences, ensuring that valuable knowledge is shared and preserved.
Best Practices for Implementing content curation with artificial intelligence
- Define Clear Objectives: Identify which areas of the organization will benefit the most from content curation to maximize project impact.
- Efficient Automation: Select AI tools that suit the company’s specific needs and data volumes.
- Continuous Feedback: Engage employees and organizational leaders in the curation process to ensure high-quality, relevant content.
The Future of content curation with artificial intelligence in Knowledge Management
Integrating content curation with artificial intelligence not only optimizes knowledge management but also strengthens companies’ innovation capacity. As these technologies evolve, organizations that invest in them will be better prepared to face the challenges of the digital environment and achieve sustainable competitive advantages.
Conclusion
AI-powered content curation is essential for companies aiming to optimize their knowledge management and foster innovation. Here we present five success stories showcasing its impact on time reduction and improved operational decisions.
If you want to learn about other success stories, benchmark, or share your experience, email me at [email protected] to exchange ideas.
In closing, large corporations should consider AI implementation in content curation. This is essential to remain competitive in the digital age.
How to retain the knowledge of people who leave
At a certain point in history, our client Ecopetrol was impacted by a Colombian government decree from 2005, which eliminated special pension schemes. This led to around 30% of the workforce being eligible for retirement. So, the question was How to retain the knowledge of people who leave
“When you review the profiles of these people, you find they have over 15 years of experience and are between 40 and 50 years old. We’re talking about very young people with a lot of experience in Ecopetrol’s processes. These retirements could have a significant impact on operations if we don’t ensure the necessary knowledge transfer, if we don’t make sure that knowledge stays within the company,” says Oscar Guerra from the company.
This was one of the triggers that motivated the inclusion of Knowledge Management at Ecopetrol. In other organizations, no such measures were taken, and when top management realized the loss of critical knowledge due to retirements, unfortunately, it was already too late.
Instead of just presenting the problem, let’s look at some cases where this type of situation has been successfully resolved with concrete plans and programs.
ECOPETROL
Going back to Ecopetrol, Human Resources realized that a third of the company had to retire, so they created a plan that included a campaign to raise awareness among those nearing retirement to transfer their knowledge.
“The objective was met,” says Janeth Rojas Martínez from the Knowledge Management department, “and all those who contributed felt very honored by a soft recognition, which consisted of a dinner where they were given diplomas to acknowledge their contributions to Knowledge Management.”
INCAUCA
At Incauca, a Colombian company based in Cali, the Social Welfare department applies a process for people nearing retirement. It involves a one-year support program. Additionally, those considered of special value to the company are rehired after retirement.
PAN AMERICAN ENERGY
Guillermo Ceballos, from the oil company Pan American Energy, shares that “In Argentina, the retirement age for women is 60 and for men is 65. At Human Resources, we interview professionals every 12 or 14 months when they reach that age. We have a geophysicist working who is 70. When we interviewed him and asked how we could start planning his retirement, he replied: ‘What? I have at least 10 more years working at full capacity, and then I’ll dedicate myself to mentoring.’ This is because we have a mentoring program, and he wants to continue contributing from there. He is also a university professor, so he’s already working on what knowledge he can transfer.”
IBM
At one point, IBM needed to bring in resources and decided to turn to people who had retired from the company, people between the ages of 65 and 70 who had a lot of knowledge to share but were, as they say, just “tending their gardens.”
The retirees responded, and work systems were created to fit their age and needs, allowing them to enjoy what they had earned after years of dedication.
Some work remotely. Others go to IBM offices a few days a month. They are integrated into teams primarily composed of young people between the ages of 22 and 30, blending their tacit knowledge with the vast experience of these “warriors” who were previously reluctant to share their knowledge. They never wrote a paper, but they know a lot because they spent 30 years solving problems and providing solutions.
Interestingly, they now share their accumulated knowledge with a lot of enthusiasm, probably because they see life from a different perspective and take IBM’s call as recognition, which helps them feel useful.
For these older individuals who joined the project, the challenge is to work in teams, as most spent many years working solo in a management position or consulting role. When they were called back, they were required to closely interact with the younger employees, not only to transfer their technical knowledge but also to teach them how to navigate IBM and pass on a corporate culture that had yielded excellent results and that the company wants to update, not lose.
According to Roberto Rodríguez, who works at IBM: “The idea is for these experts to act like educators, as if they were in a classroom, but the challenge is to teach while performing tasks. Our teams usually consist of two veterans and 10 or 12 young people. It wasn’t easy to get the teams to work well together, but we did it.”
“We had to make changes on the go, at speed, and we also had to identify leaders, which is one of the most important tasks of our non-technical education department, which we call ‘Professional Development.’ These leaders, who are young individuals with natural leadership skills, help the veterans see the benefits of the new ways of working, which prioritize the collective over the individual.”
In this project, IBM aimed to, and successfully managed, something valuable and challenging: changing the habits of those with different work styles, not aligned with a culture of sharing. Throughout the process, there were a few individuals who had to be let go, but they were few. In most cases, with time, effort, and intensive training, awareness, coaching, leadership courses, review meetings, learning sessions, and evaluations, these reincorporated experts were successfully integrated.
In addition to the examples mentioned above, we’ve developed other critical knowledge retention programs with various clients across different industries. The key has always been planning well in advance to execute the programs without overwhelming the process at the last minute.
YPF
At the Argentine oil company YPF, they named their program “Footprints,” which dedicated approximately 56 hours to each person nearing retirement who had significant knowledge to offer the company.
The following graphic shows the work scheme that was used:
One of our clients, Petróleos de Venezuela (PDVSA), during an industry-wide strike, lost many qualified professionals who took their knowledge to competitors in the same industry.
Retirement plans help retain the knowledge of those who leave, but they are also important because, during the course of the plan, individual knowledge is integrated into the company’s intellectual assets.
There are more successful cases that I couldn’t include due to space, but for those interested in learning more or sharing your own experiences, feel free to contact me at [email protected] and we can discuss further.
Define the Knowledge Critical
It’s not enough to just understand that “knowing how” generates value for the business; it’s also essential to understand which knowledge adds value to the organization.
In numerical terms, 20% of organizational knowledge generates 80% of a company’s intellectual capital, so it’s important to direct knowledge management actions to focus efforts on those critical knowledge areas that are central to the business.
The Experience of TIGO Colombia
At TIGO, based in Medellín, a knowledge management model was developed to identify critical knowledge associated with each role, develop, secure, and create it, all while aligning this model with the organization’s strategic objectives aimed at generating value. This model focused on two areas:
1) Identification of Critical Knowledge: This type of knowledge is linked to the training proposal offered by the Training School under the corporate university’s oversight.
To identify critical knowledge, the process considers the identification of experts, the processes that require the knowledge, and the roles that need the knowledge.
2) Identification of Key/Critical Roles: These roles are associated with the training itinerary assigned to that critical role. At the end of the training plan, individuals can become certified to perform that role within the company.
To identify these key roles, factors such as the processes involved, the critical activities performed by the role, the knowledge required for the activity, and the identification of experts are considered.
Not all knowledge holds the same importance for the business; in fact, some knowledge does not add value.
The criteria for validating the identification of critical knowledge and roles are based on whether a role is difficult to obtain, if few people perform it, if it has a direct impact on customer service delivery, or if it performs critical process activities.
The graph below illustrates the description:
Critical Knowledge at GMI Peru
Another significant case to include in this article is that of our client, Graña y Montero. GMI identifies critical knowledge primarily from documented experiences of past projects.
Mara del Rosario Barrena, Organizational Development Manager at GMI, explains that critical knowledge in her company refers to engineering. For example, if a project has been completed, the plans must be accessible to consult the applied knowledge. This prevents “reinventing the wheel” when someone has to work on a similar project.
Graña y Montero identifies critical knowledge mainly from documented experiences of past projects. Mara del Rosario Barrena, Organizational Development Manager at GMI, explains that critical knowledge in her company refers to engineering. For example, if a project has been completed, the plans must be accessible to consult the applied knowledge. This prevents “reinventing the wheel” when someone has to work on a similar project.
One of the pillars supporting Graña y Montero is “Business Knowledge.” As shown in the following graph, a set of Knowledge Management activities are outlined: identification, capture, organization, and dissemination of what is known.
How is knowledge identified at Graña y Montero? Fundamentally, based on documented experiences from past projects. There is also knowledge closely related to the present, with knowledge generated daily, and another necessary for addressing the future, identified based on the strategy required by the organization’s engineering business.
In this case, it involves looking at where the business is headed and, through this anticipatory exercise, visualizing what will be needed in the future.
Integrating Artificial Intelligence into Knowledge Management
In the digital age, Knowledge Management has become a key factor in the success of organizations. With the increasing complexity of data, Artificial Intelligence (AI) has emerged as a fundamental tool for optimizing the capture, processing, and distribution of organizational knowledge.
In all consulting experiences, it is evident that companies integrating AI into their knowledge management processes not only store information more efficiently but also generate new insights and solutions through various forms of artificial intelligence.
“AI not only aids in capturing and utilizing explicit and tacit knowledge but also fosters a continuous learning environment and enhances strategic decision-making.”
Cognitive AI, which mimics human thinking, has transformed organizations’ ability to process large volumes of data and convert them into useful knowledge. On the other hand, machine learning and deep learning allow companies to analyze complex patterns, predict trends, and make data-driven decisions in real time.
Complementing this, natural language processing (NLP) has revolutionized the way systems manage and understand textual knowledge, facilitating the capture and organization of tacit knowledge. Meanwhile, AI-driven intelligent agents automate repetitive tasks and manage information flows faster than ever before.
We have worked on integrating these various forms of artificial intelligence with diverse clients across different industries. These practical experiences highlight how AI not only aids in capturing and utilizing explicit and tacit knowledge but also fosters a continuous learning environment and enhances strategic decision-making
Experiences Linking Knowledge Management and Artificial Intelligence
Below are some success stories where artificial intelligence has played a central role in transforming knowledge management.
Shell: An AI system was implemented to manage knowledge related to oil and gas extraction. This system analyzes historical and current drilling data to provide recommendations on production optimization and maintenance. Thanks to AI, Shell has been able to capture and utilize the expertise of its professionals more efficiently, improving decision-making.
PwC: An AI-powered platform was developed that allows employees to access relevant and personalized information based on their roles and needs. The system uses natural language processing techniques to analyze large volumes of internal and external information, enhancing knowledge search within the company. This approach not only accelerates access to information but also fosters collaboration and innovation.
Siemens: AI is employed to manage knowledge related to their industrial plants. AI helps analyze operational data to predict failures and optimize system maintenance. Additionally, Siemens uses AI to capture technical knowledge generated by engineers and share it efficiently among teams, facilitating problem-solving and improving productivity.
General Electric: The Predix platform was developed, combining artificial intelligence and data analytics to manage knowledge in the operation of industrial equipment. The platform allows engineers to access real-time data and apply machine learning algorithms to optimize machine performance, thus capturing the knowledge generated by analyzing large volumes of data.
Unilever: AI and machine learning are used to manage knowledge in the human resources area, particularly in recruitment. The system analyzes interview data and resumes to identify the best candidates more efficiently. Simultaneously, it captures knowledge about best hiring practices and continuously improves the selection process.
In these and other experiences not covered in this article, a knowledge management model linked to artificial intelligence can be represented in a reference model like the one below:
In another article, I will focus exclusively on developing this model, where Knowledge Management is placed at the center, as it deserves a detailed exploration.
If you’d like to discuss this topic, see how you can leverage what you have in your organization, or learn about specific experiences in your industry, feel free to write to me at [email protected] and we can talk.
From Document Management to Knowledge Management
In today’s competitive business world, scaling from document management to knowledge management is more than a change in terminology; it is a crucial strategic transformation for any organization aiming to maximize the value of its information.
This article delves into how to make this transition practically and effectively, drawing on best practices and insights from industry leaders.
The Key to Success: Connecting Experts with Those in Need
Grupo Energía Bogotá provides a clear vision of knowledge management. The key lies in connecting those who need critical information with those who possess the necessary knowledge. This approach not only facilitates information exchange but also promotes a “knowledge culture” where information is managed and shared in a way that truly adds value.
It is important to shift the mindset within organizations to foster a culture that not only stores information but uses it strategically. This is where knowledge management stands apart from mere document management, moving from an archival approach to one of value creation and distribution.
The Crucial Difference Between Information and Knowledge
In the industrial business context, the line between document management and knowledge management often becomes blurred. Belcorp highlights a critical point: many companies confuse information management with knowledge management, which can limit their ability to generate real value.
It is necessary to clearly differentiate these concepts. While information management focuses on storing and organizing data, knowledge management is oriented towards the strategic use of that data to create competitive advantages. The main task is to convince leaders that knowledge management is a distinct discipline that can transform how an organization operates and thrives.
Practical Strategies to Avoid Fruitless Debates
Instead of falling into semantic debates about what constitutes document management and what constitutes knowledge management, it is more productive to focus on how to integrate both disciplines to enhance business outcomes. True synergy is achieved when these strategies complement each other, generating a tangible impact on processes and value creation.
Success Case: Grupo Energía Bogotá
Grupo Energía Bogotá is a living example of how knowledge management can be successfully implemented. Here, each document has a clear purpose and is managed through specialized software that prevents content duplication and ensures that information is always up-to-date.
This approach not only improves operational efficiency but also ensures that corporate knowledge is accessible and relevant, integrating lessons learned from various sources and keeping them updated for practical application.
How Major Law Firms Transformed Their Document Management into Knowledge Management to Boost Their Success
The transition from document management to knowledge management is crucial for law firms to maximize their efficiency and competitiveness. The two cases below are outstanding examples of how adopting knowledge management has transformed their operations.
In the first case, one of the largest law firms in the US and Europe evolved its traditional document management system into a knowledge management approach. By implementing advanced tools such as intelligent search engines and pattern analysis, they reduced the time spent searching for information and improved legal decision-making. This change not only increased productivity but also significantly enhanced service quality, offering a competitive advantage in the market.
The second case, a global firm with a presence in over 16 countries, transformed its document management approach into a comprehensive knowledge management system. The firm created a centralized repository and encouraged collaboration among its lawyers through internal platforms. This allowed them to standardize services, improve operational efficiency, and offer innovative legal solutions. As a result, they reduced costs, increased productivity, and strengthened their competitive position globally.
In this second case, we recently expanded the development of collaborative platforms and the implementation of artificial intelligence technology for predictive information management.
Conclusion: Knowledge Management as a Strategic Pillar
The evolution from document management to knowledge management is not just an operational improvement but a cultural transformation that can redefine business success. By adopting practical approaches based on the experiences of leaders such as law firms, Grupo Energía Bogotá, or Belcorp, organizations can integrate knowledge management into their overall strategy, ensuring that information is not just stored but transformed into a strategic asset.
I have left out some experiences due to space constraints, but you can find some of them in my books here or you can write to me at [email protected] to discuss the opportunities available in your context with the resources you have today or with those available for free.
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