Future of Work: Insights from Human + Machine

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.

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

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.


My Takeaways from: Welcome to AI – A Human Guide to Artificial Intelligence

Since I am still in the early stages of my AI journey, the title Welcome to AI immediately caught my attention, particularly because I am interested in how we as humans need to adapt driven by the rapid proliferation of AI.

David Shrier offers thought-provoking insights into the transformative impact of AI on society, emphasizing its implications for businesses and leadership.

My Key Insights from the Book:

  1. Democratization of AI
    The release of new platforms, like TensorFlow, has made advanced machine learning easy accessible, enabling widespread AI adoption. This democratization lowers entry barriers and drives innovation and competition across industries. For businesses, this directly impacts their strategic planning, including where to allocate resources effectively to stay competitive.
  2. Workforce Evolution and Job Displacement
    AI is automating tasks across various sectors, leading to the displacement of traditional roles. Entire professions, including, design, copywriting, and even engineering are evolving or becoming obsolete. However, AI is also creating demand for new roles, particularly those requiring uniquely human skills such as problem-solving, adaptability, and interpersonal communication.
  3. The Future of Work: Hybrid Teams
    The workplace of the future will be defined by hybrid teams where humans and AI systems collaborate to enhance productivity and capabilities. AI excels in handling repetitive or data-intensive tasks, freeing humans to focus on strategic, creative, and relational aspects of work. To enable this synergy, leaders must prioritize user-friendly AI systems and equip their teams with the skills to integrate AI effectively.
  4. Ethical AI Integration
    The lack of global standards for ethical AI applications calls for proactive leadership. Establishing clear ethical frameworks for AI use, centered on transparency, fairness, and accountability, is essential for maintaining trust among customers, employees, and other stakeholders.

Recommended Actions for Leaders:

  • Invest in AI Literacy: Develop a robust understanding of AI technologies to make informed and strategic decisions.
  • Foster a Culture of Continuous Learning: Promote reskilling and upskilling initiatives that emphasize soft skills and adaptability, preparing the workforce to thrive alongside AI.
  • Promote Human-AI Synergy: Implement AI tools that enhance human strengths, building teams that combine AI efficiency with human creativity and insight.
  • Establish Ethical Guidelines: Create and adhere to ethical standards for AI usage to ensure responsible implementation and long-term trust.

I am convinced that these insights and actions will empower us to navigate the AI revolution effectively, positioning our organizations for sustainable success in an increasingly AI-enhanced and automated world.

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

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

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.”