Workplace Stories by RedThread Research Podcast Por Stacia Garr & Dani Johnson arte de portada

Workplace Stories by RedThread Research

Workplace Stories by RedThread Research

De: Stacia Garr & Dani Johnson
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Workplace Stories is a podcast for HR and people leaders who are tired of noise and need clarity that actually holds up. It is hosted by Stacia Garr and Dani Johnson of RedThread Research.

Each episode features candid conversations with practitioners, thinkers, and executives who are navigating real decisions inside complex organizations. Not hypotheticals. Not vendor promises. Real tradeoffs, real experiments, and real lessons learned along the way.

You’ll hear how leaders are making sense of skills, AI, organizational design, and culture when there’s no clear playbook and pressure to show progress is high. The focus is always the same: what’s actually working, what isn’t, and what leaders are doing next.

Workplace Stories helps you make sense of complexity, build credibility with evidence, and move from ideas to action with more confidence.

Want to be part of the conversation? Join our community for free and connect with others shaping the future of work.

Learn more about RedThread Research here: https://redthreadresearch.com/homeRedThread Research 2026 Economía Gestión Gestión y Liderazgo
Episodios
  • Authentic AI Adoption and Cultural Impact: Dessalen Wood #WS121

    Authentic AI Adoption and Cultural Impact: Dessalen Wood #WS121
    Feb 17 2026
    From overcoming initial anxieties through hackathons and playful experiments, to setting an ambitious organizational roadmap for AI, Dessalen Wood shares how Syntax is embedding artificial intelligence across departments, focusing on pragmatic progress rather than hype.You’ll hear stories about driving excitement, learning by doing, and the all-important challenge of measuring real impact. More than just technology, this episode dives into the culture shifts, collaboration with IT, and leadership mindsets that are pushing companies out of their comfort zones and into the future, while keeping authenticity and humanity front and center.You will want to hear this episode if you are interested in...00:00 Overcoming AI fear through collaboration03:30 Defining AI readiness today09:55 AI's role in business transformation15:46 AI anxiety in the workplace22:05 Making AI adoption fun28:11 AI expertise requires human touch36:42 AI strategy: Three layers explained41:31 True transformation vs. improvement53:21 Rethinking work, technology, and AIOvercoming AI AnxietyEarly stages of AI adoption in organizations are often marked by fear. Employees worry about being displaced, making mistakes, or failing to keep up. At Syntax, Dessalen Wood and her fellow leaders tackled these concerns by creating safe, engaging, and transparent opportunities to experiment.One of the most effective strategies was an organization-wide AI hackathon. Everyone, regardless of their role, was invited to submit ideas for automation and improvement—ideas that the tech team then built. Not only did this demystify AI, but it also provided a healthy dose of competition and excitement. Dessalen describes that, “Instead of people fearing automation, it became a competition... People were saying, please, automate my tasks!” This shift from apprehension to enthusiasm helped break through adoption barriers and foster a culture of creative problem-solving.Structuring Success: A Multi-Layered AI RoadmapSyntax’s approach moves AI from a buzzword to a set of actionable strategies. The leadership distinguished between three core areas:Department Initiatives: Leveraging AI for productivity and process improvement within teamsCustomer Value: Enhancing solutions and services delivered to external clientsBusiness Transformation: Reimagining core business models and operations for strategic advantageMany organizations mistakenly assume one AI initiative will magically improve all three—but real impact comes from tailored strategies for each. In practice, this means differentiating between continuous improvement (making existing tasks more efficient) and true reinvention (fundamentally transforming how and why work gets done).The creation of AI champions, employees trained as internal advocates and solution designers, helped ensure that innovative ideas didn’t just sit in a backlog. Instead, those not ready for large-scale investment could be adapted, piloted, and iterated by these champions, keeping the spirit of experimentation alive while prioritizing resources for the highest-value initiatives.The Human Element: Authenticity, Experimentation, and MeasurementAs AI tools become more prevalent, a new challenge emerges: maintaining authenticity in communication, development, and leadership. The team discussed the “hollowed-out leader” phenomenon—where over-reliance on AI could dilute critical thinking and personal investment. Dessalen explains why expertise, context, and human customization are more important than ever: If it doesn’t demonstrate expertise and isn’t highly curated, it just turns people off.Measurement is also evolving. Early wins in AI productivity are being tracked, not just in terms of completion rates or tool adoption, but in demonstrable business outcomes and stretch goals. Syntax uses tools that help employees articulate their productivity gains and set new impact targets, ensuring that activity translates into organizational value.Resources & People MentionedExperience Qualtrics Management Resources Connect with Dessalen WoodDessalen Wood on LinkedIn Connect With Red Thread ResearchWebsite: Red Thread ResearchOn LinkedInOn FacebookOn TwitterSubscribe to WORKPLACE STORIES
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    58 m
  • Five Levels of Becoming AI Native: Melissa Reeve

    Five Levels of Becoming AI Native: Melissa Reeve
    Feb 4 2026
    The way organizations think about artificial intelligence (AI) in the workplace has shifted dramatically over the past few years. While early conversations centered on isolated experiments and technological hype, organizations now face the much harder task of integrating AI into the fabric of how work gets done. We welcome Melissa Reeve, author of “Hyper Adaptive: Rewiring the Enterprise to Become AI Native,” to discuss what AI adoption really means for people, processes, and culture.Melissa tackles some tough questions about organizational complexity, shifting operating models, and the critical role of culture and systems thinking in successful AI integration. Listeners will get candid advice on starting small, experimenting with purpose, and preparing for the rewiring ahead. You will want to hear this episode if you are interested in...03:38 Integrating AI into organizations12:47 AI Native enterprise structure15:51 Dynamic AI governance framework18:58 AI implementation foundations23:56 Process mapping for AI integration29:44 Balancing efficiency and leadership focus37:02 Start small with value streams40:59 Innovative organizational funding models42:14 Starting a skills-focused organization47:03 Digital Twins in Product TestingNavigating the AI Revolution at WorkMelissa Reeve’s journey began on the factory floors of Toyota, learning firsthand how small process shifts can drive system-wide change. Building on years of research and influence from Lean, Agile, and DevOps practitioners, Reeve authored a five-stage maturity model she calls hyperadaptive, designed to guide organizations through the incremental steps needed to become truly AI-native.The five stages of Melissa's model:Foundation – Build organizational understanding of AI; create dynamic governance structures and clarify guardrails. Optimization – Identify and optimize business processes for AI interactions; move beyond basic experimentation. Agents & Automation – Develop and manage AI agents that execute tasks and processes autonomously. Rewiring – Shift organizational architecture from rigid hierarchies to flexible, value-stream teams funded and incentivized differently. Hyperadaptive – Fully sense-and-respond organizations capable of real-time adaptation.Melissa splits these into two main categories: Basecamp (the first three stages, where most companies currently operate) and the Emerging Frontier (rewiring and hyper adaptivity).Why Organizations Struggle with AI IntegrationAccording to Melissa, most organizations are stuck because they underestimate the support structures required for successful AI adoption. It’s not just about updating technology, in fact, 70-80% of AI success depends on people, culture, and processes, not algorithms. Companies often rush to deploy AI agents or experiment without a clear North Star, leading to pilot fatigue and an 80% failure rate. Many organizations haven’t even finished laying the foundational groundwork, such as establishing unified governance or mapping work processes.Another common pitfall is the tendency to try everything at once. Pressure for fast results drives teams to bite off too much, resulting in burnout and costly errors.Moving from Experimentation to Purposeful TransformationPlaying with AI is not a strategy. While experimentation is necessary, organizations must put bounds on these efforts, know why they're experimenting, what hypothesis they're testing, and what success will look like.One necessary precursor is getting to grips with how your organization actually works. Many leaders lack visibility into workflows, decisions, and skillsets, making process optimization difficult. Reeve suggests collaborative process mapping—sometimes supported by AI tools—to unlock tacit knowledge and identify where AI can augment or reinvent workflows.Organizing Around Value StreamsOne of the most transformative elements is the shift from function-based silos to cross-functional value stream teams. Melissa draws on examples from Toyota, Zappos, and Unilever—organizations that reimagine workflows, funding mechanisms, and team incentives to deliver value rather than preserve hierarchy. Dynamic budgeting, focused experimentation, and flexible team structures help organizations scale AI success without tearing up everything at once.Culture, Upskilling, and Durable SuccessAI’s impact will be decided by how well organizations invest in people. Unilever’s Future Fit program exemplifies this approach, aligning reskilling efforts to individual purpose and business needs. It’s not algorithms that set successful organizations apart, but their ability to create cultures and support systems that empower people to adapt, reinvent themselves, and thrive amidst change.Start small, experiment with purpose, invest in support structures, and prepare to rewire not just technology, but how your organization thinks about work itself. AI may be the catalyst, but people, empowered and ...
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    50 m
  • Reimagining Work at Scale: Manuel Smukalla on Skills, Dynamic Shared Ownership, and the Future of Bayer

    Reimagining Work at Scale: Manuel Smukalla on Skills, Dynamic Shared Ownership, and the Future of Bayer
    Jan 21 2026
    Manuel Smukalla, Global Talent Impact, Skills Intelligence, and Systems Lead at Bayer, joins Workplace Stories to unpack one of the most ambitious organizational transformations underway today. As Bayer confronts significant market, legal, and profitability pressures, the company has taken a radically different approach to how work, leadership, and talent are structured, rethinking everything from management layers to career progression.In this episode, Manuel walks through Bayer’s shift to Dynamic Shared Ownership (DSO), a decentralized operating model built around networks of teams, 90-day work cycles, and leaders who coach rather than control. He explains why skills visibility became a foundational requirement for this model to work and how Bayer is using skills data to democratize opportunities, improve talent flow, and fundamentally rethink careers inside a global enterprise.You’ll hear how Bayer reduced management layers by more than half, redesigned leadership expectations through its VAC (Visionary, Architect, Catalyst, Coach) model, and moved toward a culture where employees are empowered, and expected, to own their work, development, and impact.You will want to hear this episode if you are interested in...[01:01] Why Bayer embarked on a radical organizational transformation.[04:30] What Dynamic Shared Ownership really means in practice.[06:55] Moving from hierarchical structures to networks of teams.[10:40] Why skills visibility became a critical business problem.[14:05] How 90-day work cycles change accountability and outcomes.[18:10] Building organizations around customer problems, not functions.[21:15] Launching skills profiles as a starting point, not an endpoint.[23:00] How Bayer’s talent marketplace democratizes opportunity at scale.[27:00] The three pillars of a skills-based organization.[33:00] Rethinking careers, performance management, and feedback.[43:10] The VAC leadership model explained.[52:30] Measuring success in a decentralized organization.[53:45] Advice for organizations considering similar transformations.Dynamic Shared Ownership: Redesigning How Work Gets DoneAt the core of Bayer’s transformation is Dynamic Shared Ownership, an operating model that replaces traditional hierarchies with flexible networks of teams. Manuel explains how Bayer reduced its management layers from thirteen to six and reorganized work into 90-day cycles focused on clear outcomes. After each cycle, teams reflect on what worked, what didn’t, and whether the work should continue at all.This approach decentralizes decision-making and forces a shift away from command-and-control leadership. Leaders are no longer expected to direct every task; instead, they create the conditions for teams to succeed, setting direction while trusting teams to determine how outcomes are achieved.Skills as the Engine of Talent FlowFor Dynamic Shared Ownership to function, Bayer needed a new way to understand and deploy talent. Manuel shares a pivotal realization: managers were turning to LinkedIn to understand employee skills because the organization lacked internal visibility. That insight sparked Bayer’s skills journey.Rather than starting with complex taxonomies, Bayer focused first on skill visibility. Employees created and maintained skills profiles, supported by workshops on how to describe capabilities effectively. Over time, this evolved into a talent marketplace that matches people to work based on skills, not job titles, career level, or location, helping democratize access to opportunities across the enterprise.Moving Talent to Work, Not Work to TalentManuel outlines three defining pillars of a skills-based organization. First, talent must move to work rather than work being constrained by static roles. Second, organizations must commit to permanent upskilling, recognizing that development is continuous, not episodic. Third, opportunities must be democratized at scale, reducing reliance on manager sponsorship or informal networks.Bayer’s marketplace supports fixed roles, flex roles, and fully agile project-based work, encouraging employees to actively shape their careers while remaining accountable for outcomes. This model challenges long-held assumptions about promotions, ladders, and linear advancement.Leadership and Performance in a Decentralized WorldLeadership at Bayer has been redefined through the VAC model: Visionary, Architect, Catalyst, and Coach. Leaders set direction, help teams design how value is created, remove barriers, and support rapid cycles of learning. This requires significant unlearning for leaders shaped by traditional hierarchies.Performance management has also shifted. Goals are set in 90-day cycles at the team level, with feedback coming from peers and work leads rather than solely from a direct manager. Over time, this creates richer data on contribution and impact, but also demands a cultural shift toward transparency, shared accountability, and continuous ...
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    59 m
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