Digital Transformation

Future of Work: Insights from Human + Machine

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Human + Machine: Reimagining Work in the Age of AI, written by Paul R. Daugherty and H. James Wilson, has garnered widespread acclaim for its insightful and practical approach to integrating artificial intelligence into business operations. Readers have praised its clear analysis and inspiring examples of AI applications across various industries.

The book is recognized as a thought-provoking and essential resource for understanding the future of work, emphasizing the symbiotic relationship between humans and machines. It makes complex AI concepts approachable, providing a compelling roadmap for leaders aiming to harness AI’s full potential while navigating its ethical and operational complexities.

More than just a technical guide, Human + Machine serves as a strategic playbook for executives seeking to lead AI-driven transformation effectively.

The Core Premise: Collaborative Intelligence

At the heart of Human + Machine is the concept of collaborative intelligence—the idea that AI is not a replacement for human talent but a powerful complement that enhances human capabilities. The book challenges the traditional view of automation as a job eliminator and instead presents a more optimistic, structured framework where AI and humans work symbiotically to create exponential value.

Daugherty and Wilson introduce the MELDS Framework, which identifies five crucial shifts in how businesses can approach AI transformation:

  • Mindset Shift – Moving from a technology-first approach to a human-centered AI adoption strategy.
  • Experimentation – Encouraging a culture of iterative learning and agile AI deployments.
  • Leadership – Ensuring executives play a hands-on role in AI integration and ethics.
  • Data – Harnessing the right data in ethical, transparent, and responsible ways.
  • Skills – Investing in upskilling and reskilling employees to thrive in AI-driven environments.

This review captures the key insights from each chapter and provides actionable takeaways for leaders looking to embrace AI effectively.

Chapter Summaries and Leadership Actions

Chapter 1: The AI Work Redesign Imperative

  • AI does not simply replace jobs; it transforms them by reshaping roles and responsibilities.
  • Leadership Action: Conduct workforce planning to identify roles that AI will augment rather than replace. Create structured transition plans to help employees adapt.

Chapter 2: The Missing Middle: Humans + AI

  • Successful AI adoption requires a balance between automation and human judgment.
  • Leadership Action: Invest in training programs that help employees collaborate with AI, emphasizing decision-making, creativity, and ethics.

Chapter 3: Reimagining Business Processes with AI

  • AI-driven process redesign should focus on innovation rather than mere efficiency.
  • Leadership Action: Develop a framework to assess which processes should be augmented, automated, or reinvented entirely using AI.

Chapter 4: AI and Data: The Foundation of Intelligent Workflows

  • AI’s effectiveness depends on high-quality, structured, and unbiased data.
  • Leadership Action: Implement strong data governance policies to ensure data integrity, fairness, and transparency in AI applications.

Chapter 5: Scaling AI Across the Enterprise

  • Many companies struggle to scale AI beyond initial pilot projects.
  • Leadership Action: Create cross-functional AI implementation teams and define clear metrics to measure AI adoption success.

Chapter 6: AI and the Future of Work

  • AI will create new job roles while transforming existing ones.
  • Leadership Action: Establish continuous learning initiatives and reskilling programs to equip employees with AI-relevant competencies.

Chapter 7: The Responsible AI Framework

  • AI governance should focus on transparency, accountability, and fairness.
  • Leadership Action: Develop and enforce AI ethics guidelines to ensure responsible deployment and mitigate bias.

Chapter 8: A Leader’s Guide to Reimagining Processes

  • Leaders must actively drive AI-powered transformation by fostering an experimental and adaptable mindset.
  • Leadership Action: Encourage a culture of AI-driven experimentation, allowing teams to iterate on AI solutions and adapt based on real-world learnings.

Chapter 9: Eight New Fusion Skills for an AI Workplace

  • AI-driven work environments require hybrid skill sets that combine human expertise with AI capabilities.
  • Leadership Action: Create mentorship and coaching programs that help employees develop these fusion skills:
    • Intelligent Inquiry – Leveraging AI insights effectively through critical questioning.
    • Bot-Based Empowerment – Collaborating with AI tools to enhance productivity.
    • Reciprocal Learning – Ensuring continuous feedback between humans and AI systems.
    • Relentless Reimagination – Consistently rethinking processes and strategies.
    • Holistic Judgment – Balancing AI-generated insights with human intuition.
    • Ethical Guardian – Upholding ethical standards in AI development and deployment.
    • AI Exponential Thinking – Using AI-driven innovation to scale business impact.
    • Constructive Skepticism – Evaluating AI recommendations critically to avoid over-reliance.

Final Thoughts

Human + Machine provides a compelling roadmap for senior executives and transformation leaders seeking to leverage AI as a force multiplier for their businesses. By integrating real-world case studies, actionable frameworks, and the latest AI trends, the updated edition is more relevant than ever for organizations embarking on or refining their AI journeys.

The book’s optimistic yet pragmatic approach distinguishes it from other AI literature, making it an essential read for leaders looking to harness AI’s full potential while navigating its ethical and operational complexities. If you are serious about the future of work and digital transformation, Human + Machine is a must-read that will equip you with the strategies needed to stay ahead in an AI-powered world.

Effective Stakeholder Management in Digital Transformation

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Digital transformation is a complex journey that requires strategic stakeholder management to ensure success. Engaging and managing stakeholders effectively across the four key phases—Strategy to Plan, Plan to Execution, Execution to Integration, and Sustainable Adoption to Value Realization—is essential. Below, we explore how to assess and engage key stakeholder groups throughout the transformation, leveraging three industry-proven frameworks I have applied in several different formats.

Using Mendelow’s Power-Interest Matrix for Stakeholder Assessment

To determine which stakeholders to focus on, organizations can use Mendelow’s Power-Interest Matrix, which categorizes stakeholders based on their level of influence (power) and engagement (interest). Stakeholders with high power and high interest should be closely managed, as they are critical to transformation success. Those with high power but low interest should be kept satisfied with updates, while those with low power but high interest should be informed and engaged appropriately. Finally, stakeholders with low power and low interest require only periodic monitoring to ensure alignment.

Applying the ADKAR Model to Assess Stakeholder Participation

The ADKAR model (Awareness, Desire, Knowledge, Ability, Reinforcement) is useful for evaluating where stakeholders stand in terms of engagement. Leaders should assess whether stakeholders are aware of the transformation, desire to participate, have the knowledge and ability to support the changes, and receive reinforcement to sustain engagement. This structured approach helps tailor communication and interventions to move stakeholders through the engagement journey effectively.

Leveraging Kotter’s Change Model for Stakeholder Engagement

Once stakeholders are assessed, Kotter’s 8-Step Change Model provides a roadmap for actively engaging them. This includes creating urgency, building coalitions of support, developing a clear vision, removing obstacles, generating quick wins, and institutionalizing change. By applying these principles, organizations can maintain momentum and ensure stakeholders are aligned and invested throughout the transformation.

In the following section, I will describe how to assess and engage with five main stakeholder groups—Employees, Managers, Executives/Leadership, Board of Management/Shareholders, and Customers/Suppliers/Industry Stakeholders—leveraging the aforementioned models.

1. Employees

Assessing Employees

The ADKAR model provides a structured approach to assessing employees’ engagement in digital transformation. Organizations should evaluate:

  • Awareness: Do employees understand the need for transformation?
  • Desire: Are they motivated to participate and embrace change?
  • Knowledge: Do they have the necessary training and information to contribute?
  • Ability: Can they effectively apply new skills and technologies?
  • Reinforcement: Are there mechanisms in place to sustain long-term adoption?

By leveraging this model, leaders can identify gaps and tailor interventions to support employees throughout the transformation journey.

Activities Across the Phases:

  • Strategy to Plan: Measure awareness, attitudes, and perceived impact on roles.
  • Plan to Execution: Identify resistance points and skill gaps.
  • Execution to Integration: Measure adoption levels and operational challenges.
  • Sustainable Adoption to Value Realization: Track engagement and digital proficiency.

Engaging Employees

  • Strategy to Plan: Communicate the vision, expected impact, and upskilling opportunities.
  • Plan to Execution: Involve in pilot programs and provide structured change management support.
  • Execution to Integration: Celebrate quick wins and reinforce cultural alignment.
  • Sustainable Adoption to Value Realization: Foster continuous learning and career growth.

2. Managers

Assessing Managers

For managers, the ADKAR approach can also be applied. The activities across the four phases include:

  • Strategy to Plan: Evaluate readiness to champion change and operational alignment with business plans.
  • Plan to Execution: Determine their ability and capacity to lead teams through change.
  • Execution to Integration: Assess change leadership effectiveness.
  • Sustainable Adoption to Value Realization: Ensure they incorporate the new ways of working into daily practices and sustain leadership in digital culture.

Engaging Managers

  • Strategy to Plan: Provide training and involve them in shaping implementation roadmaps, ensuring they are part of the guiding coalition as per Kotter’s model.
  • Plan to Execution: Equip with leadership coaching and transformation frameworks, enabling them to remove barriers and create short-term wins.
  • Execution to Integration: Ensure ongoing coaching and recognition programs to sustain acceleration and institutionalize changes.
  • Sustainable Adoption to Value Realization: Embed digital thinking in business processes and continuously reinforce new behaviors.

3. Executives/Leadership

Assessing Executives/Leadership

Executives and leadership play a crucial role in digital transformation due to their high power and interest, as highlighted in Mendelow’s Power-Interest Matrix. Given their influence, continuous engagement is essential to ensure alignment and sustained commitment.

Engaging Executives/Leadership

  • Strategy to Plan: Secure sponsorship, define measurable transformation goals, and obtain commitment on resources.
  • Plan to Execution: Maintain active participation in governance structures and involve them early through pilots and demos.
  • Execution to Integration: Adapt strategies based on real-time insights and ensure transformation adoption is part of leadership reviews.
  • Sustainable Adoption to Value Realization: Ensure transformation becomes an ongoing capability.

4. Board of Management/Shareholders

Assessing Board of Management/Shareholders

  • Strategy to Plan: Identify expectations and risk tolerance.
  • Plan to Execution: Monitor risk perceptions and alignment with corporate goals.
  • Execution to Integration: Measure financial and strategic outcomes.
  • Sustainable Adoption to Value Realization: Validate return on investment and future opportunities.

Engaging Board of Management/Shareholders

  • Strategy to Plan: Present a compelling business case with ROI projections.
  • Plan to Execution: Provide transparent reporting on progress and early wins.
  • Execution to Integration: Align transformation metrics with business performance.
  • Sustainable Adoption to Value Realization: Demonstrate sustained business value and future scalability.

5. Customers, Suppliers, and Industry Stakeholders

Engaging Customers

  • Strategy to Plan: Communicate potential benefits and involve key customers in feedback loops.
  • Plan to Execution: Gather feedback through prototype testing and focus groups.
  • Execution to Integration: Showcase improvements and deepen customer relationships.
  • Sustainable Adoption to Value Realization: Reinforce engagement through personalization and innovation.

Engaging Suppliers/Partners

  • Strategy to Plan: Engage in co-innovation discussions and assess digital readiness.
  • Plan to Execution: Co-develop implementation roadmaps.
  • Execution to Integration: Strengthen collaboration through integrated systems.
  • Sustainable Adoption to Value Realization: Strengthen ecosystems with emerging technologies.

Engaging Community & Industry/Competitors

  • Strategy to Plan: Share thought leadership and collaborate on industry best practices.
  • Plan to Execution: Establish partnerships for innovation and shared learning.
  • Execution to Integration: Share success stories and participate in industry forums.
  • Sustainable Adoption to Value Realization: Lead industry conversations and future transformations.

By structuring stakeholder management through a stakeholder-centric approach across the four phases of digital transformation, organizations can maximize adoption, mitigate risks, and ensure long-term success.

The Power of Clarity: Why Clear RACIs Are Essential for Successful Transformations

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One of the biggest challenges in implementing transformations and new processes is defining who is responsible for what. Unclear roles can lead to inefficiencies, confusion, and delays—both during the transition phase and once the new process is fully operational. To avoid these pitfalls, organizations must establish clear RACI (Responsible, Accountable, Consulted, and Informed) matrices upfront.

The Role of RACI in the Implementation Phase

During the implementation phase of a transformation, multiple teams and individuals must collaborate effectively. Without a well-defined RACI, responsibilities can overlap or fall through the cracks, leading to bottlenecks and misalignment. Here’s how a well-structured RACI enhances the transition phase:

  1. Clear Accountability: Identifies who owns each task, ensuring that decisions are made efficiently.
  2. Defined Responsibilities: Distinguishes between those executing the work (Responsible) and those ensuring it is done correctly (Accountable).
  3. Seamless Collaboration: Engages key stakeholders (Consulted) for input without causing unnecessary delays.
  4. Effective Communication: Keeps relevant parties (Informed) updated, reducing misunderstandings and redundant efforts.

By establishing a clear RACI at the outset, organizations can drive smoother transitions, reduce resistance, and keep projects on track.

The Importance of RACI in the End State

Once the new process is fully implemented, maintaining role clarity is just as critical. Many transformation efforts stumble post-implementation due to a lack of sustained accountability. A well-defined RACI ensures:

  1. Operational Efficiency: Employees understand their ongoing responsibilities, reducing friction in daily operations.
  2. Consistent Decision-Making: Clear lines of accountability ensure that decisions are made efficiently and by the right stakeholders.
  3. Sustained Process Adoption: By assigning ownership, organizations can ensure that new processes remain effective and continuously improved.
  4. Reduced Role Ambiguity: Employees feel confident in their responsibilities, leading to higher engagement and performance.

Best Practices for Implementing RACIs

  1. Engage Stakeholders Early: Involve key players in defining roles to ensure buy-in and practical alignment.
  2. Keep It Simple and Actionable: Avoid overly complex RACIs that create confusion rather than clarity.
  3. Review and Adapt: RACIs should be dynamic, evolving with organizational needs and process improvements.
  4. Communicate and Train: Ensure that all stakeholders understand their roles and how they contribute to the transformation’s success.

Conclusion

Defining clear RACIs is not a bureaucratic exercise—it is a strategic enabler for transformation success. By ensuring clarity in responsibilities during both the implementation phase and the steady state, organizations can drive accountability, efficiency, and long-term sustainability. Investing time upfront in a well-structured RACI matrix pays dividends in reducing friction and ensuring transformation efforts deliver lasting impact.

Using Knowledge Management to Drive Sustainable Digital Transformation

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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.

Implementing Portfolio Management for Transformations

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In setting up portfolio management, I have frequently relied on the materials and thinking of the Project Management Institute (PMI). These resources offer a structured approach to prioritize, execute, and govern transformation initiatives effectively. By aligning projects and programs with strategic objectives, portfolio management empowers leaders to maximize value delivery and adapt to changing circumstances.

Understanding Portfolio Management in Transformations

Portfolio management, as articulated by PMI, involves the centralized management of one or more portfolios to achieve strategic goals. Unlike project or program management, which focuses on delivering specific outputs or outcomes, portfolio management takes a holistic view. It ensures that all initiatives within the portfolio are aligned with organizational strategy, properly resourced, and balanced in terms of risk and reward.

When applied to transformations, portfolio management enables organizations to:

  1. Align Initiatives with Strategy: Ensure all transformation efforts contribute to overarching business goals.
  2. Optimize Resource Allocation: Efficiently distribute finite organizational resources across initiatives to maximize impact.
  3. Balance Risk and Value: Evaluate and manage the risks associated with each initiative while ensuring optimal value delivery.
  4. Monitor and Adapt: Continuously assess portfolio performance and adjust to external and internal changes.

Key Components of Portfolio Management

PMI outlines several key components essential for effective portfolio management:

  1. Portfolio Governance: Establish a decision-making framework that defines roles, responsibilities, and criteria for evaluating and prioritizing initiatives.
  2. Strategic Alignment: Ensure every initiative aligns with the organization’s strategic goals and delivers measurable value.
  3. Performance Management: Track the performance of portfolio components to ensure they deliver expected benefits.
  4. Risk Management: Identify, assess, and mitigate risks at the portfolio level.
  5. Stakeholder Engagement: Actively involve stakeholders to gain insights, address concerns, and ensure buy-in.

Applying Portfolio Management to Transformations

1. Defining a Transformation Portfolio

Begin by identifying all potential initiatives that could support the transformation. These may include process improvements, technology upgrades, workforce development programs, or customer experience enhancements. Categorize and group these initiatives based on their strategic importance, expected benefits, and interdependencies.

2. Establishing Governance Structures

Create a governance framework tailored to the transformation effort. This should include:

  • A portfolio steering committee responsible for prioritizing and approving initiatives.
  • Defined criteria for evaluating initiatives, such as strategic alignment, risk, cost, and potential benefits.
  • Regular review cycles to monitor progress and make data-driven decisions.

3. Prioritizing and Sequencing Initiatives

Use prioritization techniques, such as scoring models or weighted criteria, to rank initiatives based on their alignment with strategic goals, potential impact, and resource requirements. Sequencing initiatives appropriately helps manage dependencies and ensures smooth execution.

4. Resource Optimization

Assess the organization’s resource capacity, including budget, personnel, and technology. Allocate resources to high-priority initiatives while maintaining flexibility to reallocate as needed.

5. Continuous Monitoring and Adaptation

Establish key performance indicators (KPIs) to measure the success of portfolio initiatives. Regularly review portfolio performance to identify underperforming initiatives, reallocate resources, or adjust strategies in response to changing circumstances.

Benefits of Portfolio Management in Transformations

  1. Enhanced Strategic Alignment: Ensures all transformation initiatives are purposefully aligned with business goals.
  2. Increased Efficiency: Optimizes the use of resources across initiatives, reducing waste and duplication.
  3. Improved Decision-Making: Provides leaders with clear visibility into portfolio performance, enabling informed decisions.
  4. Risk Mitigation: Proactively identifies and addresses risks, reducing the likelihood of costly setbacks.
  5. Agility: Allows organizations to adapt quickly to evolving market conditions and business needs.

Case Example: Portfolio Management in a Digital Transformation

Consider the following example of how a company would apply the:

  • Identify Initiatives: Key initiatives include implementing an ERP system, automating commercial and supply chain processes, and launching employee upskilling programs.
  • Prioritize Projects: The ERP implementation is prioritized as it serves as the foundation for other initiatives, which can then roll out with higher speed and effectiveness.
  • Allocate Resources: Budget and personnel are allocated based on the strategic importance and interdependencies of initiatives, considering organizational resource capacity.
  • Monitor Progress: KPIs such as system adoption rates, productivity improvements, and training completion rates are tracked to measure success.
  • Adapt Plans: When process disruptions occur, resources are reallocated to initiatives focused on fixing critical issues.

This approach results in a cohesive, strategically aligned transformation that delivers measurable improvements in efficiency and capability.

Conclusion

Portfolio management provides a robust framework for navigating the complexities of business transformations. By aligning initiatives with strategic goals, optimizing resource allocation, and maintaining agility, leaders can drive meaningful change and deliver lasting value. Adopting PMI’s portfolio management principles equips organizations with the tools needed to turn ambitious transformation visions into reality.

Mastering Change Management – combining Kotter & Kubler-Ross

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Although Change Management plays an important role in all phases of a transformation, I have put it in the Execution to Integration phase, where most of the heavy lifting is done. There are two concepts I have been leveraging successfully across several transformations. These approaches not only ensure the structural changes are implemented effectively but also address the critical human dimensions of change, enabling long term success.

Effective change management ensures that the human side of transformation aligns with technological and operational shifts, enabling sustainable results. It acts as a bridge between organizational goals and employee adaptation, addressing both strategic objectives and emotional well-being. By leveraging John Kotter’s 8-Step Process for Leading Change and acknowledging the emotional journey described in the Kubler-Ross Change Curve, organizations can navigate the complexities of transformation with a structured and empathetic approach.

Kotter’s 8-Step Process: A Roadmap for Change

John Kotter’s 8-Step Process for Leading Change provides a proven framework to drive successful organizational change. Below, we explore how these steps integrate with the human dimensions of transformation.

  1. Create a Sense of Urgency: Digital transformation requires a compelling narrative to convey why change is essential. Highlight market disruptions, customer demands, and competitive pressures to galvanize action. Aligning urgency with the emotional reality of employees helps mitigate fear and denial, stages often seen early in the Kubler-Ross Change Curve.
  2. Build a Guiding Coalition: Assemble a cross-functional team of influential leaders and change champions who can drive the initiative. Trust and emotional intelligence are crucial here, as people in the “anger” or “resistance” stages of the Change Curve need strong role models to inspire confidence.
  3. Develop a Vision and Strategy: A clear and compelling vision simplifies the complexity of digital transformation. By connecting this vision to employees’ values and addressing their emotional concerns, organizations can foster greater buy-in.
  4. Communicate the Change Vision: Frequent and transparent communication is key to overcoming skepticism. Tailor messages to different stakeholder groups, acknowledging their unique emotional journeys through the Kubler-Ross Curve, whether they’re experiencing doubt, curiosity, or acceptance.
  5. Empower Broad-Based Action: Identify and remove obstacles that hinder progress. This step often coincides with individuals moving past resistance into exploration, a phase where empowerment and support are critical to maintaining momentum.
  6. Generate Short-Term Wins: Early victories validate the transformation effort and boost morale. Recognizing and celebrating these milestones helps people move into the “adjustment” stage of the Kubler-Ross Curve, where they begin to see tangible benefits.
  7. Consolidate Gains and Produce More Change: Use the credibility of early wins to drive deeper transformation. Address lingering resistance and reinforce the emotional shift from uncertainty to optimism.
  8. Anchor New Approaches in the Culture: Sustain change by embedding new behaviors into the organizational culture. This involves reinforcing the emotional stability achieved at the “acceptance” stage and ensuring long-term alignment with the digital vision.

Kubler-Ross Change Curve: Navigating the Emotional Landscape

While Kotter’s framework provides a structured roadmap, the Kubler-Ross Change Curve offers insights into the emotional journey employees undergo during transformation. Originally developed to explain the stages of grief, this model applies to any significant change, including digital transformation. The stages—denial, anger, bargaining, depression, acceptance, and commitment—illustrate the human side of change.

  1. Denial: At the onset of transformation, employees may resist acknowledging the need for change. Leaders must communicate urgency and provide clarity to address uncertainty.
  2. Anger: As the implications of change become clear, frustration and resistance may arise. Empathy and active listening are critical to navigating this phase.
  3. Bargaining: Employees may seek to negotiate the terms of change, clinging to familiar processes. Leaders should remain firm yet supportive, emphasizing the benefits of transformation.
  4. Depression: A sense of loss or doubt may emerge as employees grapple with the reality of change. Providing support, training, and resources helps build resilience during this phase.
  5. Acceptance: Gradually, employees begin to embrace the new reality. Celebrating milestones and reinforcing the vision strengthens this acceptance.
  6. Commitment: At this stage, employees internalize the change and actively contribute to its success. Recognition and reinforcement ensure the transformation’s sustainability.

Integrating Kotter and Kubler-Ross: A Holistic Approach

Combining Kotter’s structured steps with the emotional insights of the Kubler-Ross Curve creates a holistic approach to change management. Leaders must:

  • Align strategic objectives with emotional realities, ensuring that both the “head” and “heart” are engaged.
  • Foster a culture of trust and openness, where employees feel supported throughout their journey.
  • Provide consistent communication and resources to navigate each phase of the transformation.

Conclusion

Digital transformation is as much about people as it is about technology. By integrating Kotter’s 8-Step Process with the Kubler-Ross Change Curve, organizations can address both the structural and emotional dimensions of change. This dual approach not only accelerates adoption but also fosters a resilient, adaptive workforce ready to thrive in the digital age. In the end, successful transformation hinges on the ability to manage change—not just in systems and processes, but in the hearts and minds of people.

Defining Strategy & Objectives: Leveraging SWOT, BSC, and OKRs for Business Success

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In my decades of experience guiding strategy across diverse industries, three tools have consistently proven invaluable in defining strategic priorities and translating them into actionable business objectives:

  • SWOT (Strengths, Weaknesses, Opportunities, Threats): A classic tool to evaluate internal strengths and weaknesses alongside external opportunities and threats, providing a foundation for strategic direction.
  • BSC (Balanced Scorecard): A comprehensive framework for translating vision into a cohesive strategy by aligning performance measures across four critical dimensions: Financial, Customer, Internal Processes, and Learning & Growth.
  • OKRs (Objectives & Key Results): A goal-setting methodology that connects organizational priorities with measurable outcomes, ensuring alignment from leadership to individual contributors.

By combining SWOT, BSC, and OKRs, you can create a powerful and structured approach to crafting and executing strategy, particularly in fast-evolving environments like digital transformation. Below, I’ll guide you step-by-step on how to leverage these tools together for maximum impact.

1. Start with SWOT for Strategic Context

  • Purpose: Assess the internal and external environment to identify key factors that will influence your strategy and transformation.
  • Steps:
    1. Strengths: Identify what your organization does well (e.g., strong brand, skilled workforce, innovative culture).
    2. Weaknesses: Pinpoint areas for improvement (e.g., outdated systems, skill gaps).
    3. Opportunities: Highlight trends and external factors to capitalize on (e.g., market demand for digital solutions, emerging technologies).
    4. Threats: Recognize external risks (e.g., competition, regulatory challenges).

Output:

  • A clear understanding of your strategic position.
  • Prioritized areas to address through transformation initiatives.

Example:
Opportunity: Growing demand for AI-powered customer service → Translate into a strategic initiative in the BSC.

2. Use BSC to Build the Strategic Framework

  • Purpose: Translate insights from the SWOT analysis into a cohesive strategy by defining objectives across four perspectives (Financial, Customer, Internal Processes, and Learning & Growth).
  • Steps:
    1. Map SWOT findings to the BSC perspectives:
      • Strengths align with opportunities in Financial and Customer perspectives.
      • Weaknesses inform Internal Processes and Learning & Growth goals.
      • Threats guide risk mitigation strategies.
    2. Develop a strategy map:
      • Financial: Increase digital revenue by 25%.
      • Customer: Improve digital customer experience.
      • Internal Processes: Automate customer service workflows.
      • Learning & Growth: Upskill employees in AI technologies.
    3. Assign KPIs for each objective to measure success.

3. Deploy OKRs to Drive Execution

  • Purpose: Break down the high-level goals from the BSC into actionable, measurable objectives and key results for teams and individuals.
  • Steps:
    1. Define Objectives aligned with BSC goals:
      • Objective (from Learning & Growth): Upskill employees in AI technologies.
    2. Set Key Results to track progress:
      • Key Result 1: Train 80% of employees in AI basics by Q2.
      • Key Result 2: Certify 30% of employees in advanced AI tools by Q3.
    3. Cascade OKRs to teams and individuals:
      • Team Goal: Develop a company-wide AI training program.
      • Individual Goal: Complete AI certification by Q2.
    4. Track and adapt OKRs quarterly to ensure alignment with strategic priorities.

Example:
OKR Objective: “Improve digital customer engagement.”

  • Key Results:
    • Launch a new mobile app by Q2.
    • Increase app adoption rate by 20% within 6 months.
    • Achieve an NPS of 70+ for app users.

4. Create Feedback Loops and Monitor Progress

  • Use BSC to monitor long-term strategic outcomes through lagging indicators.
  • Use OKRs to track short-term execution progress through leading indicators.
  • Regularly review SWOT to reassess internal and external dynamics and adjust priorities.

Example Monitoring:

  • BSC KPI: Customer Satisfaction Score (CSAT) = 85% (measured quarterly).
  • OKR Key Result: Resolve 90% of customer queries within 24 hours (measured monthly).
  • SWOT Insight: Competitor launching a similar app → Accelerate product updates.

How They Work Together

  1. SWOT identifies where to focus the strategy by analyzing your environment.
  2. BSC builds a structured, holistic strategy to address SWOT findings.
  3. OKRs break down the strategy into actionable goals and drive execution across teams.

Benefits of the Combined Approach

  • Strategic Alignment: SWOT ensures relevance; BSC provides structure; OKRs drive execution.
  • Agility: OKRs enable rapid adaptation to changing conditions without deviating from the overarching strategy.
  • Measurability: BSC tracks long-term outcomes, while OKRs track immediate results, ensuring balanced performance management.
  • Focus: SWOT and BSC prioritize initiatives, and OKRs ensure focus on execution.

By leveraging SWOT, BSC, and OKRs together, organizations can create a clear, actionable roadmap for strategy implementation and transformation, balancing long-term vision with short-term adaptability.

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

/ Hans van 't Riet / Leave a comment

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.

How to Build an AI Transformation Journey Based on HBR 10 Must Reads on AI

/ Hans van 't Riet / Leave a comment

As organizations navigate the transformative potential of artificial intelligence (AI), developing a structured approach to AI adoption and integration is essential. To gain deeper insights, I turned to Harvard Business Review’s 10 Must Reads on AI. While all the articles provided valuable perspectives, three stood out as a practical roadmap: “Developing a Digital Mindset,” “Getting AI to Scale,” and “Stop Tinkering with AI.” Together, they outline a journey organizations must undertake to fully realize the potential of AI.

1. Foundation: Developing a Digital Mindset

This article establishes the groundwork for digital transformation by emphasizing the importance of cultivating a mindset that embraces technology. Key steps include:

  • Cultural Shift: Build an organization-wide appreciation for the value of data, AI, and algorithms by fostering transparency and shared understanding.
  • Skill Development: Encourage continuous learning through workshops, AI bootcamps, and peer mentoring to equip employees with the tools to think and act digitally.
  • Experimentation: Create safe spaces for teams to pilot AI technologies, enabling learning without fear of failure.

Takeaway: Without a solid foundation of a digital mindset, efforts to scale or integrate AI may face resistance or lack alignment with broader business goals.

2. Execution: Getting AI to Scale

Building on the digital mindset, this article focuses on the practical steps required to move AI initiatives from isolated projects to enterprise-wide capabilities. It emphasizes:

  • Infrastructure Investment: Develop robust data pipelines and scalable platforms to support AI systems at scale.
  • Strategic Alignment: Ensure AI initiatives directly contribute to organizational objectives by prioritizing use cases with measurable impact.
  • Governance: Establish ethical standards, accountability, and metrics to manage AI responsibly and mitigate risks.

Takeaway: With the right infrastructure and strategic focus, organizations can transform a digital mindset into actionable, measurable outcomes.

3. Maturation: Stop Tinkering with AI

This article builds on the previous two by addressing barriers that prevent organizations from fully integrating AI into their core operations. Key themes include:

  • Moving Beyond Pilots: Transition from experimentation to meaningful deployment by integrating successful pilots into everyday operations.
  • Accountability: Assign leaders and define metrics to ensure AI delivers tangible value.
  • Strategic Focus: Align AI with long-term business goals and embed it into workflows and decision-making processes.

Takeaway: Achieving full integration requires organizations to stop tinkering and commit to scaling AI in a way that drives strategic outcomes.

The three articles collectively present a clear and actionable AI transformation journey:

  1. Start with People and Culture: Develop a digital mindset to prepare the workforce for technological change and foster a culture of experimentation.
  2. Build the Necessary Infrastructure: Scale AI by investing in systems, processes, and platforms that enable organization-wide adoption.
  3. Achieve Full Integration: Move beyond experimentation to embed AI into core operations, driving strategic and measurable outcomes.

Final Thoughts

Embracing AI is not just a technological shift—it’s a cultural and strategic transformation. By following this roadmap, organizations can unlock AI’s full potential and position themselves for long-term success.

Are you ready to take your organization from tinkering with AI to transformation?

Full details of the arcticles can be found in above HBR’s 10 Must Reads on AI

Co-Intelligence – Maximizing AI Collaboration for Success

/ Hans van 't Riet / Leave a comment

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.”