Workplace Stories by RedThread Research

Strategic workforce planning is back, and not in a nostalgic “this trend is back around” kind of way. It is back because the old staffing model, react late, hire fast, hope the market delivers, is failing more often than it works. The biggest misunderstanding is still the same one: strategic workforce planning is not long-term headcount forecasting. It is not a spreadsheet exercise dressed up with better visuals. It is a business discipline that exists for one reason, to stop leaders from committing to strategies the workforce cannot deliver.

In this episode of Workplace Stories, David Edwards, author of The Strategic Workforce Planning Handbook, lays out a definition of SWP that is refreshingly usable. Strategic workforce planning is workforce planning for the strategic things in the organization, not an attempt to plan the entire workforce. That single shift makes SWP more approachable, more realistic, and far more effective.
If you have not listened yet, this is one of those episodes worth hearing end-to-end. The conversation is practical, occasionally blunt, and full of the kind of “this is what actually happens inside companies” detail that most workforce planning content avoids.

You will want to hear this episode if you are interested in...

[00:00] A clearer, more usable definition of strategic workforce planning.
[00:43] Why SWP is back right now.
[03:20] How SWP supports scenario thinking without false precision.
[09:50] The questions SWP must answer to be useful.
[11:40] Uncertainty, talent scarcity, and skills half-life as drivers.
[14:30] Why SWP is an exercise in ambiguity, not certainty.
[17:20] Why SWP works best as a business process, not an HR project.
[20:05] What HR should do if it is not included in strategy conversations.
[22:00] How to define “strategic” beyond leadership roles.
[25:10] Why tasks matter more than skills for future work.
[28:00] The contextual data missing from most workforce planning.
[31:15] How AI forces better workforce planning questions.
[41:20] What happens when SWP forces leaders to narrow priorities.
[45:30] What to do when the business will not listen.
[46:45] Why this work matters at the human level.

Strategic Workforce Planning Starts With One Uncomfortable Question

Strategic workforce planning becomes useful the moment it stops pretending it can predict the future. The real starting point is simple: Is the workforce fit for the organization’s future business purpose? That framing does two things immediately. First, it moves SWP out of the “HR process” bucket and into the “business execution” bucket. Second, it forces the conversation away from false certainty and toward risk, trade-offs, and feasibility.

One of the most helpful parts of this episode is how clearly the conversation draws a line between strategic and long-term. Strategic does not automatically mean five years out. In some organizations, planning 15 months ahead is strategic compared to how they have historically operated. If you want the cleanest definition of SWP in the most human language possible, it is worth listening to the early part of the conversation where this is unpacked in real time.

Why Workforce Planning Has Returned

Workforce planning always comes and goes. It resurfaces when the world feels unstable, and it fades when leaders believe they can hire their way out of problems.Right now, hiring your way out of problems is not working.There is too much uncertainty, and it is coming from too many directions at once. Geopolitical instability affects where work can happen. Talent shortages continue to constrain hiring. Skills decay faster than most organizations can reskill. Generational shifts are changing expectations around mobility and development. And technology is changing the shape of work itself.

The point is not that leaders suddenly became more disciplined. The point is that the environment is forcing discipline.Strategic workforce planning is the response to that reality. Not because it gives certainty, but because it gives options. It gives a way to talk about what might happen without having to pretend anyone knows exactly what will happen.

Strategic Workforce Planning Works When It Stops Being “HR’s Thing”

A lot of SWP efforts fail for a predictable reason. They are treated like an HR deliverable. A report. A deck. A spreadsheet. A set of numbers handed over to leadership. Strategic workforce planning is not a deliverable. It is a business process. It is a feasibility process. It is a risk conversation. One of the strongest through-lines in this episode is the idea that HR must initiate this conversation, not because HR owns strategy, but because HR holds the missing information. HR knows things about recruiting realities, workforce behavior, retention patterns, internal mobility, and capability development that business leaders often overlook.

But knowledge is not enough. The shift HR has to make is from reporting to synthesis. People analytics without business context is just numbers. When workforce data is layered onto business strategy, a story emerges. A small function may be revenue-critical. A demographic cliff may be coming. The external market may not supply replacements. The timeline may be unrealistic.This is where SWP becomes sharp.

Strategic Does Not Mean Leadership Only

Many organizations quietly turn strategic workforce planning into succession planning. They define strategic as director and above, focus on leadership roles, and build plans around titles. That is leadership continuity planning. It is not strategic workforce planning. Strategic workforce planning is about what is material. Sometimes the most strategic workforce segment is a small team of individual contributors with rare expertise and direct revenue impact. They may never appear in succession planning decks. They may not have high-profile titles. But losing them becomes a board-level issue the moment revenue drops or delivery fails. 

Skills Are Not the Answer, Tasks Are the Missing Middle

Skills still matter, but the skills conversation has gotten out ahead of itself. The problem is not that skills are irrelevant. The problem is that skills are being treated as the answer to a question they cannot solve. Skills describe people. Work is made of tasks. People use skills to perform tasks. That middle layer is what connects workforce planning to reality. This becomes especially obvious when AI enters the picture. AI does not simply change which skills people need. It changes which tasks exist, how tasks are performed, and which tasks no longer require a human at all. If an organization cannot describe how work is changing at the task level, the skills conversation stays abstract. It becomes a taxonomy exercise instead of a planning exercise .This is one of the most useful reframes in the conversation, and if you are wrestling with the skills-versus-tasks debate inside your organization, it is worth hearing how this is discussed in context.

Workforce Planning Has to Include the Person, Not Just the Skill

A skill taxonomy can tell an organization that someone has a skill. It cannot tell the organization whether that person wants to use it. Whether they have demonstrated it in real execution. Whether they are willing to take on leadership. Whether they just moved into a role and are still ramping. Strategic workforce planning becomes more realistic when it includes contextual data, not just skill labels. This is where SWP becomes less about classification and more about decision-making. It stops treating people like skill containers and starts treating them like human beings with preferences, histories, and constraints.

HR Influence Requires Persistence, Risk Language, and Political Skill

Even when HR gets the analysis right, many organizations still do not listen. That is not paranoia. It is often true. In environments where HR has historically been transactional, leaders do not expect HR to challenge strategy feasibility. They do not expect HR to raise uncomfortable risks. They do not expect HR to show up with options. Strategic workforce planning forces HR into a different posture. It requires HR to speak in the language of risk, to persist, and to get political when necessary. If one group will not listen, find another that will. Engage operational risk. Borrow credibility. Use the channels that the organization already respects. This is one of those episodes where the advice is not theoretical. It is practical, and it is the kind of thing HR leaders often need to hear said out loud.

Connect With David Edwards

David Edwards on Linkedin

Connect With RedThread Research
Website: Red Thread Research
On LinkedIn
On Facebook
On Twitter

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 collaboration
03:30 Defining AI readiness today
09:55 AI's role in business transformation
15:46 AI anxiety in the workplace
22:05 Making AI adoption fun
28:11 AI expertise requires human touch
36:42 AI strategy: Three layers explained
41:31 True transformation vs. improvement
53:21 Rethinking work, technology, and AI

Overcoming AI Anxiety

Early 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 Roadmap

Syntax’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 teams
Customer Value: Enhancing solutions and services delivered to external clients
Business Transformation: Reimagining core business models and operations for strategic advantage
Many 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 Measurement

As 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 Mentioned

Experience Qualtrics Management Resources 

Connect with Dessalen Wood

Dessalen Wood on LinkedIn 
Connect With Red Thread Research

Website: Red Thread Research
On LinkedIn
On Facebook
On Twitter

Subscribe to WORKPLACE STORIES

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 organizations
12:47 AI Native enterprise structure
15:51 Dynamic AI governance framework
18:58 AI implementation foundations
23:56 Process mapping for AI integration
29:44 Balancing efficiency and leadership focus
37:02 Start small with value streams
40:59 Innovative organizational funding models
42:14 Starting a skills-focused organization
47:03 Digital Twins in Product Testing

Navigating the AI Revolution at Work
Melissa 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 Integration
According 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 Transformation
Playing 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 Streams
One 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 Success
AI’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 organized around value, are the key to lasting transformation.
 Resources & People Mentioned

Hyperadaptive: Rewiring the Enterprise to Become AI-Native 

Connect with Melissa Reeve

Melissa M. Reeve on LinkedIn 
Connect With Red Thread Research

Website: Red Thread Research
On LinkedIn
On Facebook
On Twitter

Subscribe to WORKPLACE STORIES

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 Done
At 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 Flow
For 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 Talent
Manuel 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 World
Leadership 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 feedback.

Connect with Manuel Smukalla
Manuel Smukalla on LinkedIn
Connect With Red Thread Research

Website: Red Thread Research
On LinkedIn
On Facebook
On Twitter

Subscribe to WORKPLACE STORIES

Christine Crouch, Senior Director of Learning at General Mills, joins Workplace Stories to discuss a massive shift in how one of the world's legacy food companies approaches talent development. General Mills has recently transitioned to a centralized and integrated learning model.

In this episode, Christine lays out one of the clearest cases for centralization we have heard. While efficiency is a benefit, she argues that the true drivers are decision-making power and better data. By unifying the function, General Mills gains a stronger view of learning activity and business needs, creating the strategic infrastructure necessary for the future of work.

You’ll hear how Christine’s team manages to be centralized without losing the "local feel" through a robust Learning Business Partner model. She also details how centralization unlocks the ability to correlate learning metrics with talent outcomes like retention and performance. Finally, Christine shares her philosophy on AI, not as a replacement for human connection, but as a tool to elevate the human side of learning.

You will want to hear this episode if you are interested in...

[06:07] Background on General Mills and its culture.
[07:00] The shift from decentralized to centralized L&D.
[11:11] How to make centralization feel local to business stakeholders.
[18:30] The Learning Business Partner model explained.
[21:07] Correlating learning metrics with talent outcomes.
[27:58] Managing "rogue purchases" in a centralized model.
[34:20] Why AI will elevate, not replace, the human side of learning.
[47:35] Piloting AI coaching tools like "Nadia".

The Strategic Case for Centralization
For many organizations, the move to centralize L&D is purely a cost-cutting exercise. However, Christine frames the shift at General Mills as a play for better data and strategic decision-making. A centralized function provides a unified view of the organization's needs, allowing L&D to prioritize investments that drive enterprise-wide capabilities rather than just solving isolated functional problems. As AI accelerates, this strong data infrastructure is what will allow the organization to distinguish between what people actually need to know versus what can be offloaded to technology.

The Learning Business Partner Model
Centralization often brings the fear of losing touch with the business. General Mills solves this through the "Learning Business Partner" role, individuals who sit on the leadership teams of specific functions or segments but report back to the central L&D organization. These partners act as a bridge; they understand the HR strategy and business plans of their specific function while ensuring continuity with the broader enterprise goals. They are expected to be performance consultants first, identifying the root problems to solve rather than just taking orders for training.

AI: Elevating the Human Element
Christine’s approach to AI is grounded in optimism and human-centricity. She believes AI will not replace the human side of learning but elevate it. General Mills is actively piloting AI for tasks like personalization, automation, and coaching via a tool called "Nadia," which acts as an "always-on" coach. However, Christine emphasizes that deep skill building, like change leadership, still requires human connection, peer discussion, and the ability to "read the room," skills that AI cannot fully replicate. 

Connect with Christine Crouch

Christine Crouch on LinkedIn 
Connect With Red Thread Research

Website: Red Thread Research
On LinkedIn
On Facebook
On Twitter

Subscribe to WORKPLACE STORIES