Learning Journey

Using Knowledge Management to Drive Sustainable Digital Transformation

/ Hans van 't Riet / Leave a comment

Central to achieving sustainable adoption and value creation is the strategic implementation of Knowledge Management (KM). By harnessing KM practices, organizations can ensure the continuous improvement of processes, foster a culture of learning, and drive long-term business value.

Build Communities of Practice

At the heart of effective Knowledge Management lies the creation of communities of practice (CoPs). These groups bring together individuals with shared expertise and interests to collaborate, share insights, and solve problems. For example, a Salesforce Champion network empowers employees to share best practices, exchange knowledge, and act as advocates for innovative solutions.

Communities of practice encourage:

  • Collaboration: Enabling cross-functional teams to break down silos and share critical information.
  • Innovation: Providing a platform to explore new ideas and refine existing processes.
  • Ownership: Creating champions who drive adoption and advocate for continuous improvement.

Tips for Building Communities of Practice

  1. Define Clear Objectives: Establish the purpose and goals of the community to ensure alignment with organizational and transformation priorities.
  2. Identify and Empower Leaders: Select passionate and knowledgeable individuals to act as community leaders and facilitators.
  3. Provide Enabling Platforms: Use digital tools like Microsoft Teams, Slack, or Yammer to create spaces for collaboration and information sharing.
  4. Foster Inclusivity: Encourage participation from diverse groups across the organization to ensure a variety of perspectives.
  5. Recognize Contributions: Celebrate achievements and contributions to keep members motivated and engaged.
  6. Offer Continuous Support: Provide resources, training, and time for community members to actively participate.
  7. Evaluate and Iterate: Regularly assess the community’s impact and adapt strategies to address emerging needs.

Leverage Digital Platforms for Knowledge Exchange

Digital platforms are pivotal in ensuring seamless knowledge exchange across organizations. Tools like Microsoft Teams, Slack, and SharePoint facilitate communication, documentation, and real-time collaboration. These platforms enhance KM by:

  • Centralizing Knowledge: Providing a unified repository for accessing critical information and best practices.
  • Enabling Asynchronous Collaboration: Allowing team members to contribute across geographies and time zones.
  • Automating Processes: Integrating with AI to streamline workflows, identify knowledge gaps, and recommend relevant content.

The Role of AI in Knowledge Management

Artificial Intelligence (AI) is transforming how organizations manage and utilize knowledge. By integrating AI into KM systems, organizations can:

  • Enhance Searchability: AI-driven search capabilities ensure employees can quickly locate relevant documents and insights.
  • Personalize Learning Paths: AI algorithms recommend tailored content and training resources based on individual roles and learning preferences.
  • Monitor Knowledge Utilization: Advanced analytics identify trends in knowledge use, guiding improvements in content and processes.

Enable Continuous Learning and Onboarding

Effective KM fosters a culture of continuous learning, critical for onboarding new employees and upskilling existing teams. Key strategies include:

  • Structured Training Programs: Incorporating KM platforms into onboarding processes to provide access to curated resources and learning modules.
  • On-Demand Learning: Allowing employees to access training and knowledge resources at their convenience.
  • Feedback Loops: Capturing insights from new and existing users to refine training materials and ensure relevance.

Drive Process Improvement and Value Creation

Knowledge Management directly supports process execution by ensuring that employees have access to the tools, information, and expertise needed to perform their roles effectively. By embedding KM into daily workflows, organizations can:

  • Improve Efficiency : Avoid repetitive mistakes, prevent reinvention of solutions and enable sharing of best practice.
  • Accelerate Decision-Making: Equip teams with data and insights to make informed decisions quickly.
  • Deliver Measurable Outcomes: Link KM efforts to key performance indicators such as productivity, efficiency, and customer satisfaction.

Conclusion

In a world driven by digital innovation, Knowledge Management is not merely a support function—it is a strategic enabler of sustainable transformation. By building active communities of practice, leveraging digital tools and AI, and fostering continuous learning, organizations can achieve continuous process improvement and long-term value creation. Embracing KM as a cornerstone of digital transformation ensures that knowledge—an organization’s most valuable asset—is accessible, actionable, and impactful.

The AI-Savvy Leader: Key Concepts and Actions for Leaders

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David De Cremer’s The AI-Savvy Leader: Nine Ways to Take Back Control and Make AI Work has been widely acknowledged by leaders as an essential resource for understanding why AI implementations often fail and, more importantly, how to ensure their success. Among the books I’ve read recently, this stands out as one of the most practical and insightful guides for leaders.

The book provides invaluable insights for navigating the challenges of integrating AI into organizations. Below is a summary, crafted with the assistance of AI (ChatGPT), highlighting the most critical concepts and actionable steps for leaders to take.

1: Learning – Get to Know AI, and Learn to Use It as a Leader

Leaders don’t need to be AI experts, but a foundational understanding of AI’s capabilities and limitations is crucial. This knowledge enables leaders to make informed decisions and oversee AI projects effectively.

Actions for Leaders:

  • Invest time in learning AI fundamentals through workshops or seminars.
  • Regularly consult AI experts to remain informed on advancements.
  • Encourage AI literacy across leadership teams to drive strategic decisions.

2: Purpose – Use Your Purpose to Ask the Right Kind of Questions

AI initiatives should align with the organization’s mission and values. Purpose-driven projects ensure AI is used meaningfully to serve strategic goals.

Actions for Leaders:

  • Define a clear organizational purpose that guides AI projects.
  • Evaluate AI initiatives to ensure alignment with long-term goals.
  • Ask questions like, “How does this AI solution contribute to our mission?”

3: Inclusion – Work in Inclusive Ways to Drive Human-AI Collaborations

Inclusive environments foster better human-AI collaboration by leveraging diverse perspectives. This diversity leads to more innovative and adaptable AI solutions.

Actions for Leaders:

  • Build cross-functional teams to design and implement AI projects.
  • Solicit input from employees, customers, and partners to improve AI solutions.
  • Promote inclusivity to reduce biases in AI systems.

4: Communication – Build a Flat Communication Culture to Drive AI Adoption

Transparent communication accelerates AI adoption and fosters trust. Leaders must ensure open dialogue across all organizational levels.

Actions for Leaders:

  • Establish regular forums to discuss AI initiatives and progress.
  • Use clear, non-technical language when communicating about AI.
  • Encourage feedback loops to address concerns and refine strategies.

5: Vision – Be Visionary in How to Use AI

A strong vision defines how AI will transform the organization and creates a shared sense of purpose. Vision-driven leadership inspires confidence and commitment to change.

Actions for Leaders:

  • Craft and articulate a clear vision for AI’s role in the next decade.
  • Link AI efforts to tangible outcomes, such as improved customer experiences.
  • Share success stories to build enthusiasm and confidence in AI initiatives.

6: Balance – Adopt AI with All Stakeholders in Mind

Balancing stakeholder interests ensures ethical and sustainable AI adoption. This approach builds trust and minimizes resistance to change.

Actions for Leaders:

  • Conduct impact assessments for all AI initiatives.
  • Engage stakeholders—employees, customers, and partners—in discussions about AI.
  • Implement governance frameworks to address ethical challenges.

7: Empathy – Use a Human-Centered Approach to AI Adoption

Adopting a human-centered approach prioritizes user needs, experiences, and trust. This ensures AI enhances rather than disrupts human well-being.

Actions for Leaders:

  • Gather insights from users to understand how AI impacts them.
  • Ensure transparency in how AI systems make decisions.
  • Provide mechanisms for feedback and continuous improvement of AI tools.

8: Mission – Augment (Don’t Automate) to Create Jobs

AI should augment human capabilities rather than replace them. This approach fosters innovation and creates new opportunities.

Actions for Leaders:

  • Identify processes where AI can enhance productivity without job loss.
  • Invest in employee reskilling programs for AI-augmented roles.
  • Communicate how AI can create opportunities for growth and innovation.

9: Emotional Intelligence – Accept That Soft Skills Are the New Hard Skills, and Practice Them

Soft skills like empathy, adaptability, and communication are vital in managing the human side of AI adoption. Emotional intelligence ensures AI serves people effectively.

Actions for Leaders:

  • Develop emotional intelligence within the leadership team.
  • Lead by example, showing empathy and adaptability during AI integration.
  • Address employee concerns with transparency and understanding.

Conclusion: Becoming an AI-Savvy Leader

Leaders play a pivotal role in ensuring AI serves their organization’s purpose while enhancing human capabilities. By following the actionable steps outlined in each chapter of The AI-Savvy Leader, leaders can drive meaningful and ethical AI adoption that aligns with their organization’s values and long-term vision.

See Do Teach Method – A Powerful Approach to Learning and Capability Building

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The See Do Teach method is a transformative approach to skill acquisition, team development, and leadership building. Rooted in experiential learning, it creates a dynamic cycle of observation, practice, and instruction that ensures not only the mastery of tasks but also the empowerment of individuals to become educators themselves. Here’s why it works so effectively and how it can be applied.

Why the See Do Teach Method Works

The See Do Teach method is built on the principle of active engagement, which is proven to improve retention and understanding. Each stage builds on the last, creating a progressive learning pathway that embeds skills deeply: 1. Observation Enhances Understanding: Seeing a task performed by an expert provides learners with a clear example of success, demystifying the process and showcasing best practices. 2. Practice Solidifies Skills: Doing the task immediately after observing allows learners to apply their newfound knowledge in a safe environment, with room for feedback and improvement. 3. Teaching Deepens Expertise: Explaining and demonstrating a skill to others reinforces the teacher’s mastery and ensures that knowledge is disseminated effectively across teams.

Breaking Down the Steps

Step 1: See

Observation is the foundation of the See Do Teach method. In this stage, learners watch a skilled individual perform a task, noting critical steps, techniques, and nuances.

Example: In order to train people inside the organization to become transformation managers, I worked with one of the big 4 strategic consultancies to show actual projects in the organization to our candidates.

Step 2: Do

After observing, learners move on to hands-on practice. Here, they replicate the task under guidance, applying what they’ve seen while gaining firsthand experience.

Example: After observing the expert consultants on one or two projects, the roles changed, with the internal teams executing the projects and the expert consultants reviewing and giving advice.

Step 3: Teach

The final stage involves teaching the newly learned skill to others. This step requires learners to organize their understanding and communicate it effectively, cementing their knowledge.

Example: After executing a couple of projects themselves, the internal teams became teachers to the next cohort of candidates (and the external consultants phased out) in their See-Do cycle.

The Flywheel Effect

The See Do Teach method operates as a flywheel—a self-reinforcing cycle that gains momentum over time. As learners become teachers, they perpetuate the process, creating a culture of continuous learning and growth. Over time, this approach not only spreads knowledge but also cultivates leadership qualities and drives organizational excellence.

Example in Practice: A company adopts the See Do Teach method to train employees on a new software system. Initially, a few experts demonstrate its usage (See). Next, these employees practice and refine their skills (Do). Finally, they teach the system to others (Teach). Within weeks, the organization’s proficiency with the software grows exponentially, reducing reliance on external trainers and fostering a collaborative learning environment.

Conclusion

The See Do Teach method is a simple yet profound approach to learning that combines observation, hands-on practice, and teaching. By embedding this cycle into your organization or personal development strategies, you can create a robust framework for skill acquisition, team growth, and leadership development. Over time, the method becomes a powerful flywheel, driving sustainable success and empowering individuals to achieve their full potential.

Co-Intelligence – Maximizing AI Collaboration for Success

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I kicked off the year by reading Co-Intelligence by Ethan Mollick to gain fresh insights on working with AI, and it exceeded my expectations. The book demystifies complex AI concepts, provides practical guidance for integrating AI into professional settings, and highlights its immense potential as a collaborative partner. It offers a clear understanding of where AI stands today and several scenarios of where it might lead us in the future.

With practical examples Ethan explains how to maximize results; treat AI as a partner in your creative, strategic, and operational endeavors. Experiment, refine, and adapt your interactions to find the balance where AI amplifies your strengths and minimizes inefficiencies. With a combination of structured prompts, human feedback, and iterative collaboration, you can unlock AI’s full potential while ensuring your unique perspective and expertise remain central.

Leveraging ChatGPT as a partner to synthesize the book into actionable strategies, I got the below feedback, which make a lot of sense, especially when you have read the examples that underpin these in the book, and this list provides a good reminder when engaging with AI on how you can get the most out of it.

1. Treat AI as a Collaborative Partner

  • Mindset Shift: View AI not as a tool but as a “co-worker” or “teammate” that can augment your capabilities.
  • Define Roles:
    • Assign specific tasks to the AI, like brainstorming, drafting, or fact-checking, while you focus on oversight and decision-making.
    • Example: Ask the AI to critique your idea as a “skeptical consultant” or to generate creative suggestions as a “visionary collaborator.”
  • Iterate and Refine:
    • Avoid one-off queries. Build a back-and-forth interaction by refining prompts and providing feedback (e.g., “Can you make this more concise?” or “Explore this concept further”).
    • Experiment with AI’s ability to “think in roles,” such as acting as a researcher, editor, or project manager.

2. Use Precise and Contextual Prompts

  • Be Clear and Specific:
    • The quality of your results depends on the quality of your instructions. Write clear, detailed prompts.
    • Instead of asking, “Help me write a report,” specify: “Write a 500-word report summarizing the latest trends in renewable energy with an emphasis on solar power.”
  • Provide Context:
    • Let AI understand the purpose of its task by offering context. For instance, “Summarize this article for a professional audience in the technology sector.”
  • Use Follow-Up Instructions:
    • After getting a response, refine it with follow-ups like: “Simplify this,” “Add examples,” or “Make it more engaging.”

3. Experiment with Creative and Practical Applications

  • Creative Problem Solving:
    • Use AI for brainstorming ideas, creating innovative solutions, or generating multiple alternatives to a problem.
    • Example: “Generate five out-of-the-box marketing ideas for a tech startup targeting Gen Z.”
  • Practical Applications:
    • Automate repetitive tasks, such as data entry, drafting emails, or creating reports.
    • Example: “Draft an email responding to a customer inquiry about our refund policy.”

4. Evaluate and Verify AI-Generated Outputs

  • Critical Thinking:
    • Treat AI’s responses as suggestions, not facts. Always review and validate content, especially for accuracy and bias.
  • Ask for Sources:
    • When dealing with factual or research-based information, prompt the AI to provide sources: “Cite the studies you used to generate this answer.”
  • Compare Outputs:
    • Ask AI for multiple perspectives or versions of the same task to ensure comprehensiveness.
    • Example: “Rewrite this email with a more formal tone.”

5. Personalize AI for Your Workflow

  • Train AI on Your Style:
    • Share examples of your work so AI can align its tone, voice, or formatting.
    • Example: “Here is an email I wrote. Use this tone to draft a response to a client inquiry.”
  • Use Templates:
    • Develop prompt templates for recurring tasks. For instance:
      • “Summarize [X topic] in [Y number of words] with [Z target audience] in mind.”
      • “Generate a list of 10 questions for an interview on [X topic].”

6. Explore AI’s Specialized Capabilities

  • Data Analysis:
    • Use AI to analyze trends, identify patterns, or make data-driven decisions.
    • Example: “Analyze this dataset and identify the top three trends in customer behavior.”
  • Content Creation:
    • Create blog posts, reports, or presentations with AI assistance.
    • Example: “Draft a blog post on the benefits of adopting AI in small businesses.”
  • Role-Based Interactions:
    • Assign the AI specific roles for tasks:
      • “Act as a copywriter and create an ad slogan.”
      • “Act as a hiring manager and draft interview questions.”

7. Balance Innovation with Human Judgment

  • Recognize AI’s Limits:
    • Understand where AI struggles, such as nuanced judgment, emotional intelligence, or context-heavy decisions.
    • Use AI for what it does best—speed, efficiency, and scalability—while maintaining human oversight for subjective or critical matters.
  • Use Iterative Collaboration:
    • Collaborate dynamically by combining AI suggestions with human intuition. For example, take AI-generated ideas and refine them manually for greater creativity or precision.

8. Keep Ethical Considerations in Mind

  • Avoid Overreliance:
    • Ensure your own skills and knowledge remain sharp. Use AI to complement your abilities, not replace them entirely.
  • Identify Bias:
    • Be aware of potential biases in AI-generated content and take steps to address or mitigate them.
  • Transparent Use:
    • If sharing AI-generated content externally, be clear about its origin when appropriate.

9. Develop a Habit of Continuous Learning

  • Experiment Regularly:
    • Dedicate time to exploring new AI tools and features that can further enhance productivity.
  • Follow Industry Trends:
    • Stay updated on the latest advancements in AI to continuously integrate new capabilities into your workflow.

10. Use AI for Self-Improvement

  • Personalized Learning:
    • Use AI as a tutor for learning new skills or exploring areas of interest.
    • Example: “Explain blockchain technology in simple terms for a beginner.”
  • Feedback on Your Work:
    • Use AI to critique and improve your writing, presentations, or pitches.
    • Example: “Evaluate this report for clarity and suggest improvements.”