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AI governance is no longer theoretical. Recent cases involving Air Canada's chatbot and iTutorGroup's AI recruiting system show that organizations, not AI tools, are legally accountable for AI-generated outcomes. This article explores what these landmark cases reveal about AI liability, governance failures, and the risks of deploying AI without human oversight. Learn why monitoring, data quality, human review, and cross-functional decision-making are essential for responsible AI implementation. Discover four practical governance patterns that help organizations reduce risk, improve accountability, and build AI systems that are both innovative and defensible.

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.

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 adoption is accelerating, but many organizations are discovering a troubling disconnect between leadership expectations and employee reality. While executives report strong productivity gains, frontline workers often see little impact and remain uncertain about AI’s role in their future. This article explores the growing perception gap revealed by recent enterprise AI research, why traditional change management approaches are falling short, and how trust, involvement, and collaborative decision-making influence successful AI transformation. Learn why the biggest barrier to AI success may not be the technology itself, but the human dynamics shaping how organizations adapt to change.

In the inaugural episode of New Friction, host Douglas Ferguson and Erik Skogsberg explore how AI has shifted organizational friction from execution to decision-making and alignment. While AI accelerates production, it magnifies existing dysfunctions when teams lack collaborative habits. They introduce the concept of "multiplayer AI"—moving beyond individual productivity gains toward team-level collaboration. The conversation emphasizes that facilitation, judgment, and organizational health are now the critical differentiators. Practical takeaways include assessing whether your organization operates in "single player" or "multiplayer" AI mode and intentionally slowing down at key decision points to maximize human impact.

Organizations are no longer debating whether AI matters. They are being pulled into two very different futures. This post explores the growing divide between companies investing heavily in AI infrastructure and automation, and those focusing on the human capabilities required to make AI actually work inside organizations. Drawing from nearly a decade of experience in facilitation and AI transformation, it examines why trust, decision-making, collaboration, and organizational adaptability are becoming the real differentiators in the age of AI. A thought-provoking look at the widening gap between technological acceleration and human readiness, and why the middle ground is quickly disappearing.

The 2026 Facilitation Lab Summit brought eight extraordinary facilitators together in Austin, Texas to explore the theme of Edges: the moments of tension, uncertainty, and emergence where the most powerful work gets done. Across two days of hands-on sessions, participants explored collective wisdom, whole intelligence, embodied presence, trauma-informed facilitation, the power of metaphor, and what it truly means to show up as a facilitator rather than just perform as one. This recap covers every session with links to the full blog post and video for each speaker, offering both a window into what happened and a practical resource for your ongoing facilitation practice.

Most organizations are investing heavily in AI adoption but seeing little return because traditional training models fail to create lasting behavior change. Research from organizations like Gartner and Anthropic reveals that employees quickly forget one-time AI training and struggle to integrate AI into daily workflows. While licenses and training programs increase, real usage and collaboration remain low. This article explores why AI adoption is a design problem rather than a training problem, highlighting emerging research, behavioral insights, and a new three-part framework that helps organizations build true AI fluency through practice, iteration, and collaborative ways of working.