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Feedback plays a crucial role in shaping workplace culture, employee development, and learning processes. A strong feedback culture fosters interpersonal skills, enhances motivation, and drives continuous learning. This article explores effective approaches to feedback, its effects on student learning, workplace dynamics, and the impact of feedback on motivation. Learn how to create a meaningful, high-information feedback environment for sustainable professional growth.
In professional development, the principles of practice and feedback are vital. Much like athletes and musicians refine their skills through rehearsal and critique, professionals must engage in deliberate practice and seek feedback to master their craft. This blog post explores these elements, providing insights and strategies to foster growth and success in the workplace. Facilitators, in particular, need ongoing practice and feedback to enhance their skills. Structured practice opportunities and feedback loops help identify strengths and areas for improvement. Embracing a growth mindset is essential, as it encourages seeking feedback and viewing challenges as learning opportunities. In professional settings, practice often gets neglected, overshadowed by the expectation to perform flawlessly. To foster growth, professionals need environments where they can safely practice and receive feedback, similar to athletes rehearsing before a performance.
Meetings are nothing without the participation of its attendees. No matter how incredible a facilitator may be, a meeting will not be successful without the…
I first learned about the Google SPRINT framework in early 2017 from Matt Randall, the founder of Twyla. Google Ventures (GV) funded Twyla’s series A…
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Joe McLean, Group Product Manager for the AI Stream at Miro, who led the overhaul of Miro's Sidekicks and Flows AI surfaces launched at Canvas 25. Joe traces how his hobbyist love of Eurorack modular synthesizers shaped Flows, arguing that visible, patchable connections reveal what a chat box hides, and that a good tool's structure can enable rather than constrain creativity. He and Douglas dig into what he calls the "visual trace" - treating an AI agent like a new hire who needs onboarding, check-ins, and a replayable record so a whole team, not just one operator, can trust and build on its work. The conversation covers how cheap execution is reshaping product development, from teams showing up to meetings with working prototypes instead of slide decks, to Miro's internal VibeLab tool solving the "Git problem" of AI-generated design branches, to the rise of throwaway personal software built for an audience of one. They close on a candid discussion of the switching costs of chat-interface lock-in and Joe's conviction that the healthiest relationship with AI comes from building things with it, not just asking it questions.
Strong facilitation doesn't happen by chance. The most effective organizations build a facilitation practice with shared frameworks, trained facilitators, and leadership support that makes high-impact conversations repeatable. Instead of relying on one person to run every important meeting, they develop a scalable capability that improves decision-making, alignment, and collaboration across teams. Learn the four stages of facilitation maturity, common pitfalls to avoid, and practical steps for building an organizational practice that thrives through growth, change, and the demands of the AI era.
AI transformation succeeds or fails on more than technology. It requires a leader who can bridge AI capabilities with the people, workflows, and organizational changes needed for real adoption. Explore what an AI transformation leader actually does, the skills that make the role effective, and the Alignment Trifecta of technical translation, change management, and adoption-focused product management. Learn the common mistakes that derail AI initiatives, how to assess whether your organization is ready, and what to look for when building or hiring this critical leadership role.
AI transformation doesn’t fail because organizations lack the right tools. It fails when no one has the authority, skills, and roadmap to turn AI capabilities into lasting organizational change. Learn what an effective AI transformation leader actually does, why cross-functional facilitation is one of the most critical skills for the role, and how to build an AI product management roadmap teams will follow. Explore the AI Transformation Leader Stack, common pitfalls that derail adoption, a five-question leadership diagnostic, and practical 30-, 60-, and 90-day steps for turning AI strategy into real organizational behavior.
AI adoption in higher education and learning organizations isn't a technology problem — it's a change management challenge. This article explores how educational institutions can move beyond scattered pilots to build durable, ethical, and faculty-enabled AI-augmented learning cultures.
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Taran Lent, Chief Technology Officer at Illumia, about how his organization moved from individual AI experimentation to enterprise-wide capability. Lent describes building an "enablement task force" that deliberately avoided becoming a governing bottleneck, instead creating conditions for employees to play, learn, and share what worked, before this year's push to elevate personal AI habits into shared team and company-wide skills, standards, and vetted tools. He talks about using AI as a contrarian thought partner and even having it grade his own interviewing and meeting behavior, while cautioning that transcripts alone miss tone and body language. The conversation also covers the governance side of scaling AI, including a stakeholder review process for new tools and skills, the four-to-six-week approval timeline for new vendors, and guardrails to prevent incidents like an unauthenticated internal dashboard. Lent connects this to Illumia's recent merger of Transact and CBORD, crediting a shared "humble, hungry, smart" culture for making the integration smoother, and closes by arguing that judgment, creativity, and human discernment remain the differentiators as AI adoption accelerates.
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