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In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Alyssa Coughlin, Director and Chief of Staff for Autodesk's Data, AI, and ML Platform organization, about what it actually takes to move a large engineering company from AI-enabled to AI-native. Coughlin describes how bottlenecks have shifted away from writing code toward code review, deployment permissions, and design decisions now that AI has made execution fast and cheap. She walks through concrete changes her org has made, including abandoning two-week Scrum sprints for Kanban flow, moving from PRDs to spec-driven development consumable by both humans and AI, and building shared "knowledge graph" brains to catch duplicated work before it ships. Throughout, she frames change management as a balance of carrot and stick, arguing that engineers aren't losing their jobs so much as shifting from author to orchestrator, and that managers themselves must stay hands-on with the tools to coach effectively. She closes by describing AI as an amplifier that exposes organizational seams rather than a fix, so the real work is continuously finding and addressing the friction it reveals.

AI can now create strategy decks, journey maps, and other polished deliverables in minutes, but faster execution does not automatically create alignment. As AI makes artifacts cheaper and more abundant, the real value shifts to the shared understanding behind the work. Explore why AI-generated deliverables often fail to drive meaningful action, how collaborative processes build the context and commitment teams need, and why facilitation, conversation, and human alignment are becoming even more critical as AI accelerates organizational workflows.

AI has made execution faster than ever, but speed is no longer the competitive advantage leaders think it is. When every organization can generate polished work in minutes, the metric that matters is alignment velocity: how quickly teams reach genuine consensus, commit to a direction, and make decisions that stick. Learn why traditional execution metrics fail in AI-accelerated organizations, how misalignment creates hidden rework and reversals, and how leaders can measure commitment lag, reversal rates, and the timing of dissent to build teams that move quickly in the right direction.

AI adoption doesn’t fail because employees need more mandates, training, or oversight. It fails when organizations deploy AI without involving the people whose work will be transformed by it. Explore why employee involvement, psychological safety, and trust are critical to successful AI transformation, and why shadow AI adoption may reveal more about your workforce than traditional adoption metrics. Learn how an involve-before-mandate approach can uncover real AI opportunities, reduce resistance, build trust, and create AI-enabled workflows that employees actually want to use.

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Jeff Chow, Chief Product and Technology Officer at Miro, about where friction is showing up inside product teams as AI reshapes how work gets done. Jeff describes how PMs prototyping in high fidelity, designers coding, and engineers orchestrating agents are collapsing old role boundaries, and argues the underlying tensions — who owns a decision, who gets paged at 2am, how a designer receives feedback — aren't new, just amplified. Much of the conversation centers on decision-making as the real bottleneck: Jeff makes the case that 10x individual output doesn't translate into 10x business results unless organizations can also accelerate cross-functional alignment and cascade decisions with their underlying context, not just their conclusions. He and Douglas dig into Miro's bet that a shared visual canvas — carrying both artifacts and a decision log of why choices were made — is the connective layer that keeps AI-assisted work and human teams moving together instead of fragmenting into isolated single-player sessions. The two also trade observations on viral AI adoption patterns inside organizations, the psychology of design crits applied to AI-generated work, and the early signs of "ephemeral UI" and custom, vibe-coded widgets reshaping what software teams expect to build for themselves.

AI transformation is often measured by how much friction it removes, but not every obstacle in the workplace is waste. Some friction creates opportunities for judgment, mentorship, collaboration, and the development of critical leadership skills. Explore why leaders need to look beyond speed and efficiency when redesigning work with AI, how removing too much productive friction can weaken teams over time, and what organizations can do to preserve the human interactions that build expertise, strengthen decision-making, and prepare the next generation of leaders in an AI-enabled workplace.

AI productivity gains may be creating a hidden talent crisis. As senior employees use AI to take on work once assigned to junior staff, organizations risk “experience starvation,” weakening the pathways that build judgment, discernment, and future leaders. Explore how shrinking entry-level opportunities, skills atrophy, and disrupted talent pipelines could create long-term capability gaps, and why leaders need to design developmental friction into AI transformation. Learn how organizations can capture AI’s speed and efficiency without sacrificing the hands-on experience people need to grow into tomorrow’s experts.

Explore why AI transformation efforts focused on workforce cuts often fail to deliver meaningful ROI. Research shows that reducing headcount may create budget room, but it does not necessarily create business value. This article examines the accountability gap between measurable cost savings and the long-term opportunities organizations may be destroying in the process. Learn why successful AI transformation requires outcome-based measurement, strong governance, human expertise, and investment in the capabilities needed to guide, adapt, and scale AI systems over time.