Why do so many promising innovations fail to deliver meaningful results? And what can leaders do to turn breakthrough ideas into lasting organizational success?

Harvard Business School professor Linda A. Hill, author of Genius at Scale, joins Charles Good on The Good Leadership Podcast to explore why organizations struggle to scale innovation and what separates those that consistently innovate from those that get stuck.

Drawing on her extensive research, Linda explains how leaders can navigate the stages of innovation, design experiments that produce meaningful learning, encourage constructive disagreement, and create the organizational capabilities needed to turn promising ideas into repeatable results.

Linda A. Hill is the Wallace Brett Donham Professor of Business Administration at Harvard Business School and faculty chair of the Leadership Initiative. She is internationally recognized for her research on leadership, innovation, and building organizations capable of sustained performance. Her book Genius at Scale examines how leaders can turn innovative ideas into meaningful, repeatable impact.

Chapters

00:00 Introduction to Linda Hill and the Innovation Journey

03:20 Framing the Right Problem and Customer Focus

06:20 AI, Problem Framing, and Asking Better Questions

10:09 Creative Abrasion and Diversity of Thought

13:20 Speed vs. Quality in Innovation

16:48 Evaluating Ideas Objectively and Avoiding Bias

18:38 Managing Power Dynamics and Inclusive Decision-Making

19:14 Active Listening and Understanding Different Perspectives

22:15 Incubation: Building the Conditions for Scale

23:37 Business Models and Designing for Manufacturability

25:00 Learning From Experiments and Building Trust

27:34 Hypotheses and Strategic Planning

28:12 Balancing the Core Business and Emerging Innovations

29:34 Protecting Emerging Innovations

30:26 Balancing Freedom to Learn and Organizational Support

32:46 Organizational Culture and Scaling Innovation

33:16 How to Know When Innovation Has Truly Scaled

34:25 Why Organizational Mindset Determines Scaling Success

38:36 Pfizer's COVID-19 Vaccine Innovation Case Study

40:33 Final Takeaways: The Discipline of Innovation

AI can give us answers faster than ever. But can it replace the trust, relationships, and human connections that make great leadership possible?

As organizations embrace AI to improve productivity, they risk overlooking something essential: the conversations, collaboration, and trusted relationships that drive innovation, influence, and professional growth.

In this episode of The Good Leadership Podcast, Charles Good sits down with Andy Lopata, an internationally recognized expert in professional relationships, author of Connected Leadership, and co-author of The Financial Times Guide to Mentoring, to explore why relational intelligence may be one of the most valuable leadership capabilities in the age of AI.

Andy challenges a common assumption: having a large professional network doesn't mean you have meaningful relationships. As he puts it, "Connecting is not enough."

Together, Charles and Andy explore:

• AI + HI (Human Interaction): Why technological efficiency must be balanced with meaningful human connection.

• Relational Intelligence (RQ): How understanding, developing, and strategically nurturing relationships creates professional advantage.

• The Trust Equation: Why credibility and reliability alone aren't enough to build deep, trusted relationships.

• The Curiosity Cycle: How curiosity, authentic listening, and vulnerability transform ordinary conversations into meaningful connections.

• The Corridors of Conversation: A practical framework for moving beyond superficial networking and transactional exchanges.

• Leading Hybrid Teams: Simple ways to create connection, trust, and psychological safety in increasingly digital workplaces.

• Connected vs. Trusted: Why the quality of your professional relationships matters far more than the size of your network.

One of the most important insights from this conversation is that as AI reduces our need to ask others for information, it may also reduce the opportunities we have to learn from one another, build trust, and develop the relationships we need throughout our careers.

Chapters

00:00 Introduction: Why Professional Relationships Matter
01:37 Andy Lopata's Journey in Relationship Building
05:28 Why Relationships Are a Strategic Career Advantage
07:29 Relational Intelligence: Two Essential Dimensions
11:03 Is AI Weakening Human Connection and Collaboration?
15:58 Breaking Down Silos and Building Stronger Networks
18:31 Artificial Intelligence vs. Human Interaction
21:06 The Trust Equation, Vulnerability, and Psychological Safety
23:32 Building Relationships in Hybrid and Digital Workplaces
33:29 Being Connected vs. Being Trusted
38:18 Beyond Small Talk: How to Build Meaningful Conversations
45:19 The Curiosity Cycle: Listening, Authenticity, and Vulnerability
46:38 Building Trust and Relationships in the Age of AI

Your team agrees. But have five people examined the decision or are they repeating the same recommendation?

In this episode of The Good Leadership Podcast, Part 3 of the Judgment Series, Charles Good explores how to improve team decision-making by making individual judgments, confidence levels, and assumptions visible before discussion begins.

Through a product or service launch scenario, discover how to uncover conflicting assumptions, use AI to examine the evidence, and test whether customer interest will turn into purchases.

You’ll learn how to:

• Collect individual views before the leader shares an opinion.

• Spot forecasts that rely on the same limited evidence.

• Set a clear pilot threshold, deadline, and decision rule.

• Update predictions when the plan changes.

• Turn a missed forecast into a specific improvement for the next decision.

Everybody writes before anybody speaks. The leader explains their view last and examines their own thinking first when the results arrive.

Chapters:

00:00 Better team decisions in the age of AI

00:27 What team agreement can hide

01:10 Form a judgment before the meeting

02:04 Write predictions, confidence, and assumptions

03:36 Use AI to examine evidence and assumptions

06:55 Test demand before a full launch

08:37 Update forecasts and review results

09:41 Investigate why predictions missed

10:23 Bring the lesson into the next decision

11:01 Keep improving your team’s judgment

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