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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…
Healthcare organizations are discovering that the most effective AI implementations don't replace clinicians—they partner with them. When medical professionals and AI systems work as true teammates, diagnostic accuracy improves, clinical workflows accelerate, and patient outcomes reach new heights.
In this episode of the New Friction podcast, host Douglas Ferguson speaks with Jeff Grabill, Dean of the College of Arts and Sciences at the University at Buffalo, recorded in the immediate aftermath of the IHE US AI Summit 2026, which both men attended. Grabill recounts what emerged from that two-day working convening: the foundation of the Buffalo Statement, a collective public agenda for AI in higher education, and reflects on why the room's patience, grounded confidence, and willingness to question prior assumptions exceeded his expectations. The conversation explores why universities, often criticized for moving slowly, may possess exactly the right instincts for AI transformation: designing conversations intentionally, engineering productive friction, and moving fast and slow at the same time. Ferguson and Grabill dig into how AI has relocated rather than eliminated friction, particularly in learning environments, where effortless output now threatens the productive struggle that actually builds expertise and ideas. They close on a librarian's insight from the summit — "I don't care if AI created it, I care if it's true" — and Grabill's call for businesses and universities to actively seek one another out as partners in working through this moment.
Why do so many AI initiatives lose momentum after launch? This guide explores why AI rollouts stall and how structured change management drives lasting adoption. Learn why installation is not the same as behavior change, the common mistakes organizations make, and the five essential elements of successful AI transformation: executive sponsorship, stakeholder alignment, communication architecture, workflow-based training, and reinforcement. Discover practical frameworks, readiness diagnostics, and leadership strategies that help Directors and VPs turn AI deployments into sustainable organizational change and measurable business outcomes.
When AI expresses confidence differently than expected, something surprising happens: teams make better decisions together. Understanding uncalibrated AI models reveals new pathways for collaboration and collective sensemaking that challenge conventional assumptions about human-AI partnerships.
AI is quietly reshaping the workforce in ways most leaders aren’t measuring. While concerns often focus on entry-level job loss, the bigger risk is the erosion of apprenticeship and skill development. Drawing on research from Cornell, MIT, Yale, Microsoft, and real-world examples from organizations adopting generative AI, this article explores how “AI chains” remove the learning experiences that turn juniors into future experts. Learn why experience starvation threatens leadership pipelines, how hidden AI adoption creates governance blind spots, and what organizations can do to preserve mentorship, judgment, and long-term capability while still capturing AI-driven productivity gains.
AI tools will keep changing, but the skills that matter most endure. This article explores the durable capabilities that compound across every wave of AI innovation, helping professionals, leaders, and teams focus on what remains valuable regardless of which model, platform, or framework dominates next. Learn why critical thinking, facilitation, problem framing, collaboration, judgment, and systems thinking create long-term leverage in an AI-driven world. Instead of chasing every new tool, discover how to invest in the human skills that amplify AI, strengthen decision-making, and build career resilience for the decade ahead.
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