WorkMatters

The threat of being disrupted is prompting companies to invest heavily into AI. How do they actually go about about evaluating, approving, and scaling these investments?

In this episode of Work Matters, Thomas Bertels talks with Dennis Glacken, industry professor at the Stevens School of Business. Dennis recently interviewed 32 senior IT leaders across 30 companies about how they select, govern and scale their AI investments. He expected to find a meaningful share of budgets set aside for disruptive bets. He found the opposite: most AI spending runs through the same process as any other IT project, and most of it targets personal productivity.

Dennis points out that most companies approach AI similar to how Lean Six Sigma was deployed - train lots of people, encourage lots of projects, focus on personal productivity. He argues that unless the focus shifts to salient, high value projects, that approach will deliver real but only marginal gains.

Another interesting finding was that most companies, AI governance required an additional layer of bureaucracy. Dennis explains why every project becomes a zoning variance, why traditional IT governance models don't work for AI, and what is needed to accelerate.

Also in this conversation:

• AI-native development vs. bolting AI onto legacy systems
• what fast in-house building means for SaaS pricing
• the data leakage problem when only some employees get licenses, and
• who in the C-suite should own the transformational bets if the CIO can't

Getting an AI project started is easy. Realizing real business value is hard.

Guest Bio:

Dennis Glacken is an Industry Professor at Stevens Institute of Technology, teaching Digital and Process Innovation in the business school’s undergraduate and graduate programs. Dennis brings more than 30 years in senior management roles at leading companies. His background includes leading IT, process innovation, and acquisitions, with a focus on portfolio management and aligning technology strategy with business objectives.

https://www.linkedin.com/in/dennisglacken/

Chapters:

0:00 Everyone's racing to do AI. Few are racing to disrupt.

2:24 Expecting disruptive bets, finding incremental ones

3:04 AI projects: easy to start, hard to put into production

4:54 Train everyone and let the ideas bubble up

6:31 Rolling out AI like Lean Six Sigma — and the backlash risk

7:03 The productivity paradox

10:24 The missing middle: no forum for cross-functional trade-offs

11:42 AI governance - why every AI project is a zoning variance

17:01 The real question is: "Should AI do this?"

20:29 Tens of thousands of lines of code: engineers as AI reviewers

24:13 Big AI spend, no P&L impact: what a CIO should do

27:03 Who owns AI transformation if not the CIO?

29:21 Productivity, cost or revenue? What AI projects actually target

31:29 One foot in the old world, one foot in the new

Every CEO has a boardroom. Almost none of them have what Andrea Schwarz calls the boardroom behind the boardroom — the place where people tell you the truth instead of the version that's safe to say out loud. In this episode of Work Matters, Thomas Bertels and executive coach Andrea Schwarz talk about what leaders actually need from the people around them now that AI can produce a competent answer to almost anything.

Andrea Schwarz runs IMUA OLA, a coaching house she founded after more than 20 years in senior leadership at Adidas, Reebok, and Novartis. Her model isn’t built around a single methodology. She calls it a set of “balloons”: stakeholder-centric coaching, sustainable-performance coaching, embedded strategic advisory - pulled situationally depending on what a leader actually needs: judgment, opinion, wisdom, connecting the dots.

We also get into what’s actually driving anxiety and fear around AI and her approach for how to deal with that. Her method borrows from Pixar’s Inside Out: invite the fear in, let it say what it came to say, thank it, and do it anyway, scared. It’s a practical answer to a real problem — leaders who won’t publicly admit uncertainty about where AI is taking their own jobs, let alone their teams’.

The conversation closes on a genuinely open question about her own field: if AI absorbs the scripted 90% of coaching, what’s left is a smaller, more valuable slice — the actual human connection. Schwarz isn’t worried about that trade. She’d rather be the last piece in the block, doing the work only a human relationship can do, and she’s comfortable holding two truths that seem to contradict each other — that the work is genuinely at risk, and that there’s more room for it than the anxiety suggests.

• Most boardrooms run on “acceptable truth,” not the truth people actually carry — a “boardroom behind the boardroom,” where people leave titles at the door, is how leaders get real signal.
• As AI becomes an “answering machine” rather than a search engine, the human edge shifts to discernment, judgment, and connecting dots the machine hasn’t been asked to connect.
• Coaching doesn’t need to compete with AI on the scripted 90% of the work — give the framework away, and reserve the human relationship for the 10% that’s genuinely emotional and high-stakes.
• Naming a fear precisely (of poverty, of criticism, of loss of independence) and inviting it in, rather than suppressing it, works better than reassurance — then do it scared.
• Two contradictory truths can both be real at once: AI will replace real work, and there’s more room for human judgment than the anxiety suggests.

00:00 Room for the machine and room for humans

00:16 Andrea's journey

01:51 What makes IMUA OLA’s coaching model different

03:48 Where advisory coaching ends and coaching begins

04:54 Case study: coaching a founder and his team through change

07:43 When coaching creates a conflict of interest

09:20 What it takes to join the coaching house

09:58 Three themes she’s seeing right now

13:57 Why leaders won’t say what they’re actually afraid of

16:44 The boardroom behind the boardroom

21:21 Naming the fear AI adoption is actually triggering

25:59 How to coach someone out of that fear

29:39 Sometimes coaching means helping someone leave, not stay

32:18 How much of coaching is just being heard

33:11 Will AI replace human coaches

Andrea Schwarz is the founder of IMUA OLA, an executive coaching house based in Hawaii. She spent more than 20 years in senior leadership roles at Adidas, Reebok, and Novartis, before founding her own coaching practice. IMUA OLA blends embedded strategic advisory coaching with stakeholder-centric and sustainable-performance coaching methodologies, working with founders, C-suite leaders, and their teams.

Website: imuaola.com

LinkedIn: linkedin.com/in/imuaola

Most leaders treat misalignment as a communication problem: get people talking, and agreement follows. Franz-Josef Tillmann disagrees. He argues that misalignment isn’t a failure to communicate. It’s a collision of what local rationalities — every function in an organization makes sense of the world through its own short-term, functional lens and as a result is optimizing for different things - and no amount of talking changes what someone’s function rewards them for caring about.

Tillmann is President and Managing Partner at Metaplan USA, the consultancy behind the Metaplan Method — a structured visual facilitation technique developed in Germany in 1972 by brothers Wolfgang and Eberhard Schnelle. Before Metaplan, he ran global strategic marketing for Johnson & Johnson’s nephrology business and held marketing roles at Schwarz Pharma.

Metaplan’s answer is to surface the disagreement before trying to resolve it, starting with getting every perspective on the table before converging on what the group agrees matters most. The signature tool is what Tillmann calls the “flash mark”: a structured way to flag disagreement in the room without it becoming personal, which forces the group to clarify what it actually meant rather than assume an alignment that isn’t there.We get into where this approach applies (multi-stakeholder execution problems, cross-functional initiatives, situations where a leader needs buy-in rather than compliance) and where it doesn’t — when a leader has already decided the outcome and just wants it pushed through.

Tillmann is candid that top-down execution has its place. It’s just not alignment.Franz-Josef explains the structural paradox at the center of his work: organizations are optimized for the vertical, which means even the best people, dropped into a cross-functional initiative that cuts against that structure, often underperform anyway. Not because they’re the wrong people. Because the structure was never built to reward what they’re being asked to do.

We also talk about why a facilitation method built in the 1970s, using cards and markers instead of software, still works in today's workplace.

Key Takeaways

- Misalignment usually isn’t a communication failure — it’s a collision of “local rationality,” where each function optimizes for a different, often short-term, definition of success.

- You need to surface disagreement before resolving it. Metaplan’s format deliberately diverges — every perspective visible — before converging on shared priorities.

- The “flash mark” — a structured way to flag disagreement without making it personal — usually produces clarification, not conflict, because most disagreement turns out to be a misunderstanding of what was actually meant.

- Cross-functional leaders are set up to underperform by design — organizations optimize for the vertical (function, department), so even top performers struggle when asked to lead across it.

Chapters

00:00 Why functions do not like alignment

00:30 What the Metaplan method actually is

05:41 What problems this technique is built for

07:18 When alignment is the wrong tool: the top-down leader

08:53 Why the questions matter more than the answers

10:29 The flash mark: naming disagreement out loud

12:23 Getting past high-level agreement into the details

13:48 Why most workshops fail after they end

16:31 A 1970s method in the age of AI

20:51 Culture, hierarchy, and adapting across markets

22:30 The cross-functional paradox

25:37 Advice for a leader with one meeting next week

Guest Bio

Franz-Josef Tillmann is President and Managing Partner at Metaplan USA, where he has led strategic planning, alignment, and change management workshops for global organizations for 25 years. Before Metaplan, he was Director of Global Strategic Marketing for Nephrology at Johnson & Johnson, and held marketing leadership roles at Schwarz Pharma. He holds an MBA from NYU Stern School of Business and a Master of Science from the University of Bayreuth.

Website: metaplan.com

LinkedIn: linkedin.com/in/fjtillmann

The five-day workweek can feel inevitable. But it was designed for an industrial economy—and may no longer make sense for how work gets done today. In this episode of Work Matters, Thomas Bertels speaks with Joe O’Connor, CEO of Work Time Revolution and co-author of Do More in Four, about the four-day workweek as a catalyst for redesigning work.

Joe argues that reducing working time should not simply be treated as an employee benefit. When tied to clear performance expectations, it can create the incentive organizations need to eliminate low-value activity, rethink workflows, adopt new tools, and focus people on outcomes rather than hours. Thomas and Joe also explore what the rise of AI means for productivity. Will organizations use AI to accelerate an already overloaded workplace—or to remove clutter and give people more capacity for creativity, judgment, and meaningful work?

In this episode, you’ll learn:

- Why the five-day workweek may be poorly suited to knowledge work

- How a four-day week can create employee engagement in work redesign

- Why hours worked are an increasingly unreliable measure of productivity

- Why autonomy must be paired with accountability

- Why organizations need a clear narrative about the purpose, boundaries, and benefits of AI

Key themes

Work design, operating model redesign, future of work, AI adoption

Key Takeaways

- The five-day week is a Henry Ford-era design choice, not a productivity law — built for factory output, not knowledge work.

- A shorter week works as a forcing function: the promise of it motivates redesign, the process breaks time-based habits, and the recovery compounds performance afterward.

- The AI backlash toward 996 schedules and presence-as-commitment misreads where value is headed — volume-based work is what’s being automated, so leading for value beats leading for hours logged.

- It only sticks when autonomy and accountability are both present. Freedom without ownership, or ownership without the power to remove blockers, and it collapses.

- Most organizations don’t have real measures for knowledge work. A shorter week forces that overdue conversation instead of letting it stay overdue.

Guest Bio

Joe O’Connor is CEO and co-founder of Work Time Revolution, a consulting and research firm focused on redesigning work for the AI era. He pioneered the world’s first coordinated four-day workweek pilot in Ireland in 2020, then led the large-scale trials across the UK, North America, and Australasia as CEO of 4 Day Week Global. He is co-author, with Jared Lindzon, of Do More in Four (Harvard Business Review Press, January 2026). He also advises on AI adaptation with Splendid Torch.

Website: worktimerevolution.com

LinkedIn: linkedin.com/in/joeoconnor990

Episode Overview

00:00 Is AI here to speed up work, or slow it down?

03:03 The five-day week is a habit, not a law

04:16 Why hours and output no longer correlate

07:11 The productivity case for a shorter week

09:39 Case study: how Unilever made it stick

11:16 The 996 backlash — and what it misunderstands

15:58 Is the four day work week a one-way door?

18:22 What has to change in how the work gets done?

21:30 The knowledge-work measurement problem

24:48 What does it take to implement?

26:46 When four-day pilots get reversed

30:25 How to get started this quarter

In this episode of Work Matters, Thomas Bertels speaks with Sergio Caredda about why AI is forcing organizations to confront a much deeper issue: no one really owns work design. HR owns roles and skills. IT owns systems and platforms. Operations owns processes. Strategy owns intent. But as AI begins to reshape tasks, decisions, capabilities, accountability, and governance, organizations need a more integrated way to design work.

Sergio argues that AI adoption is not just a technology implementation or a change management exercise. It requires operating model redesign, decision governance, and a clearer “grammar” of work. Organizations need to understand what work is, how decisions are made, where human judgment is required, and who is accountable when algorithms or agents start making decisions that affect customers, employees, and business outcomes.

The conversation also explores why traditional responses — a new C-suite role, a center of excellence, or a project office — are not enough. Work design is a transversal problem - it cuts across HR, IT, operations, strategy, and management. The answer is not another box on the org chart, but a persistent design authority with real mandate, governance, and measurement.

Ultimately, this episode is about AI, but it is also about something more fundamental: organizations cannot redesign work with tools alone. They need to understand the work itself.

In this episode, we discuss:

• Why AI predictions often confuse jobs with tasks

• Why treating AI as an IT implementation misses the bigger design problem

• Why capability does not have a clear institutional home in most organizations

• Why HR owns skills, but not necessarily enterprise capabilities

• Why organizational design may need a basic grammar of work

• What a persistent design authority for work could look like

• Why leaders need to use AI themselves to understand its implications

• How organizations can start by auditing their most important value chains

• Why work design is becoming urgent, not just important

Key themes:

Work design, operating model redesign, organizational design, decision rights, algorithmic accountability, capabilities, HR transformation, future of work, AI adoption, management, governance

Memorable idea:

AI is not just changing tasks. It is exposing the fact that most organizations do not have a clear owner for how work is designed, governed, and measured.

Guest Bio:

Sergio works at the intersection of organization design, HR, work design, and AI-enabled transformation. A senior HR leader with more than 25 years of international experience, he helps organizations translate strategy into operating models where human work creates value through the integrated disciplines of organization design, capability building, talent development, and technology adoption.

He currently serves as Interim CHRO at Campari Group, where he leads one of the more mature HR AI adoption programs in European FMCG, with more than 40 active AI use cases and a close partnership with the CIO on AI capability building. This work has been recognized by SAP with a European Innovation Award.

Sergio is known for building high-performing HR teams that operate as true business partners, and for his ability to connect business strategy, people strategy, operating model, and technology into one coherent system. His perspective is that AI transformation cannot be treated as a technology rollout alone. It requires an end-to-end view of work, organization, leadership, and capability — which is where senior HR leadership can make a distinctive contribution.

He writes regularly on organization design, leadership, and the future of work at sergiocaredda.eu, and is currently writing The Intentional Work Trilogy on organization design. Sergio previously joined Work Matters to discuss the intentional organization.

Connect with Sergio:

LinkedIn: https://www.linkedin.com/in/sergiocaredda/

Website: https://sergiocaredda.eu

Substack: https://newsletter.sergiocaredda.eu

00:00 AI Job Loss Hype

01:10 Tasks Versus Jobs

02:57 AI As General Tech

06:19 Who Owns Work Design

09:21 Accountability And Governance

14:49 Persistent Design Authority

16:43 Campari’s Digital Twin Experiment

24:40 No Quick Wins Mindset

27:26 Work Design and Managers

31:49 Employees and Job Fear

36:36 Rethinking Change Management

42:20 CEO 90 Day Playbook

45:42 Value Chains and Human Glue

Everyone is talking about the future of work. But much of the conversation is filled with buzzwords, vendor language, and overly simple predictions about what AI will do to jobs, organizations, and HR.

In this episode of Work Matters, Thomas Bertels speaks with Andy Spence about what is real, what is hype, and what leaders should actually be paying attention to as AI changes the way work gets done. The conversation starts with a lightning round on some of today’s most common future-of-work ideas: agentic recruiting, skills-based organizations, talent marketplaces, and leaderless organizations. Andy argues that some of these ideas are real, but none of them should be accepted uncritically. Agentic recruiting, for example, is not entirely new. It is part of a longer history of using chatbots, algorithms, video tools, and data in hiring. Skills-based organizations are useful in some contexts, but skills are only one factor in determining whether someone will succeed in a role, team, or organization. From there,

Thomas and Andy explore AI’s impact on work and the labor market. Andy argues that it is too simplistic to look at one task that AI can perform and conclude that an entire job or profession will disappear. Work is more complex than that. Tasks get unbundled and rebundled in new ways across employees, freelancers, platforms, automation, agents, and external partners. That rebundling process may be one of the most important shifts leaders need to understand.

The episode then turns to work design. Andy and Thomas discuss how the discipline of organization design and work design has faded in many companies — just when it is needed most. Traditional org charts and spreadsheets are not enough to answer today’s questions about how work should be structured, sourced, automated, coordinated, and measured. As organizations introduce AI, agentic systems, internal talent marketplaces, freelancers, and new operating models, leaders need a much broader view of work design.

The conversation also explores HR transformation. Andy argues that reducing administration does not automatically make HR strategic. Without proactive changes to the people, skills, mindset, and capabilities inside HR, the function often ends up with less admin but not necessarily more strategic impact.

Ultimately, this episode is about how leaders can move beyond the AI hype and think more seriously about the future of work. Technology will matter. But the organizations that succeed will be the ones that redesign work intentionally, build the right human capabilities, use data wisely, and connect HR strategy directly to business outcomes.

In This Episode, We Discuss

• Why agentic recruiting is real, but not entirely new

• The limits of skills-based organizations and skills taxonomies

• The promise and mixed results of internal talent marketplaces

• Why leaderless organizations are unlikely to eliminate the need for human accountability

• How AI is forcing us to rethink what leadership actually is

• Why work design may be due for a renaissance

• How HR technology can reinforce outdated work models

• Why “AI adoption” is the wrong goal for organizations

• Why HR transformation requires more than automation and admin reduction

• What capabilities HR leaders need to build now

• How successful HR functions enable self-empowered teams

Key Themes

AI and work, future of work, agentic recruiting, skills-based organization, talent marketplaces, work design, organization design, HR transformation, CHRO strategy, HR technology.

Guest Bio

Andy Spence is a leading voice in workforce and talent strategy, with more than 20 years of experience advising global organizations and the C-suite on how to build people-centric, future-ready organizations. His career spans Big 4 consultancies, start-ups, and his own advisory firm, working with clients including the NHS, John Lewis Partnership, Novartis, Deloitte, and CIPD. Andy publishes the Work 3 Newsletter, read by more than 30,000 professionals each week. Known for turning global megatrends into practical insight, Andy helps HR leaders design talent models fit for a rapidly changing world.

Connect with Andy

Work 3 Newsletter: https://wrk3.substack.com

LinkedIn Profile: https://www.linkedin.com/in/andrewspencehrtransformation/ Future of Work

Speaker Profile: https://workforcefuturist.substack.com/p/workforce-futurist-speaker-profile

00:00 AI Adoption Is Not The Goal

02:13 Lightning Round

05:32 What Future Of Work Means

09:08 AI Myths And Human Value

13:57 Work Design Renaissance

16:53 Why HR Tech Is Outdated

19:19 CHRO Capabilities And Megatrends

23:37 Resetting The People Function

27:34 What Makes HR Transformation Work

30:51 People Analytics Ethics And KPIs

Many organizations are built around the assumption that problems can be analyzed, controlled, and solved through cause and effect. That assumption works well when the problem is clear or complicated. It is far less useful when the problem involves people, culture, leadership, relationships, change, or uncertainty.

In this episode of Work Matters, Thomas Bertels speaks with Boudewijn Bertsch about the Cynefin framework, developed by Dave Snowden, and how it helps leaders make sense of different types of problems. Cynefin distinguishes between domains such as clear, complicated, complex, chaotic, and confused. Each domain requires a different way of thinking, deciding, and acting. Boudewijn explains why the dominant management narrative has been shaped by the industrial revolution and the machine metaphor. As a result, many organizations try to force complex human problems into ordered systems of control, accountability, optimization, and best practice. But when the challenge is complex, context matters, outcomes cannot be guaranteed, and leaders need to learn through safe-to-explore experiments.

The conversation explores what this means for AI adoption, performance management, leadership, organizational change, and the way we treat people at work. Thomas and Boudewijn discuss why people are not simply resources, why fear undermines experimentation, why leaders need to open collective intelligence, and why the work of management increasingly requires facilitation, sense making, and the ability to navigate uncertainty.

Ultimately, this episode is about learning to diagnose the type of problem before choosing the solution. In a complex world, the answer is often not something a leader already has. It is something the organization discovers together.

In this episode, we discuss:

• What Cynefin is and why Dave Snowden describes it as a framework, not a model

• The five Cynefin domains: clear, complicated, complex, chaotic, and confused

• Why modern management is shaped by cause-and-effect thinking

• How organizations accidentally make complex problems worse by forcing them into ordered systems

• Why leadership, culture, relationships, and human performance are complex

• How to use safe-to-explore experiments in complex environments

• Why fear, punishment, and traditional accountability can block experimentation

• How AI adoption can be approached through multiple experiments and learning loops • The limits of performance reviews and linear rating systems

• Why organizations need to lead interactions, not just individuals

• How leaders can use collective intelligence to make sense of complex problems

• Why leadership style should depend on context

• What to do when a team is confused or facing chaos

• How to get started by asking better “we” questions

Key themes: Cynefin, complexity, sensemaking, leadership, management, organizational culture, human complexity, safe-to-explore experiments, collective intelligence, performance management, AI adoption, accountability, uncertainty, organizational change, complex adaptive systems

Memorable idea: In complex work, the answer is not something leaders can simply analyze their way toward. It is something people discover through context, collective intelligence, and safe-to-explore experiments.

Guest Bio:

Boudewijn Bertsch works with individuals, teams, and executives on how to bring out the best in themselves and others. Trained as a business economist and sociologist, he draws on neuroscience, systems and complexity science, somatics, evolutionary psychology, and biology to help leaders work more effectively in complex environments.

Over the past 35 years, Boudewijn has worked with leader around the world. He is a certified somatic leadership coach, a former assistant professor at Erasmus University Rotterdam, and a longtime executive education faculty member and advisor to organizations. Boudewijn is co-editor and contributing author of Cynefin: Weaving Sense-Making into the Fabric of Our World.

Connect with Boudewijn: LinkedIn: https://www.linkedin.com/in/boudewijn-bertsch-3b79a54a/

Cynefin Wiki: https://cynefin.io/wiki/Main_Page

The Cynefin Co: https://thecynefin.co/

Book: Cynefin - Weaving Sense-Making into the Fabric of Our World https://thecynefin.co/library/cynefin-weaving-sense-making-into-the-fabric-of-our-world/

00:00 Why Complexity Matters

02:02 Cynefin Explained

02:40 Five Domains Overview

06:07 Diagnosing Problems at Work

09:29 Safe to Explore Experiments

11:36 AI as a Complexity Case

15:33 Humans Are Not Machines

19:56 Rethinking Accountability

23:43 Tools for Collective Sensemaking

24:33 Sensemaker and Triads

30:19 Leading by Context

34:28 Confused and Chaotic Domains

39:48 Getting Started with Cynefin

42:29 Complexity in Healthcare

46:00 Closing and Takeaways

Talent acquisition is one of the most important processes in any organization. It determines who gets hired, what capabilities the organization builds, and whether the business can actually execute its strategy. And yet, in many companies, the process feels completely broken.

In this episode of Work Matters, Thomas Bertels speaks with Gabriella D. Salvatore about how AI is changing HR and talent acquisition — and why the current system may be creating more volume, more friction, and more frustration without necessarily producing better hiring outcomes. Gabriela argues that AI has improved productivity in parts of HR, but often at the expense of human judgment, connection, and quality.

The conversation explores why talent acquisition has become too transactional, why generic job descriptions are part of the problem, and why HR must move beyond order-taking to become a true strategic partner to the business. Gabriella and Thomas discuss the need to rethink job design, rebuild trust in the hiring process, and use technology more wisely — not as a substitute for human judgment, but as a tool that supports better decisions. Ultimately, this episode is about more than recruiting. It is about how organizations can design work, roles, and systems in ways that are more human, more intentional, and more effective.

In this episode, we discuss:

- Why talent acquisition is a critical business process — and why it feels so broken today

- Why recruiters are overwhelmed and candidates are frustrated

- The limits of applicant tracking systems and AI-driven screening

- How generic job descriptions create downstream problems in hiring

- Why HR business partners need to act as strategic talent advisors, not order takers

- The role of work design and job design in better hiring

- Why candidate experience is often damaged by automation and poor process design

- What it might take to rebuild talent acquisition for a more human future

Guest Bio:

Gabriela D. Salvatore works with leaders and organizations on the human side of business, helping them tap into the power of their people. Her work focuses on leadership, HR, organizational effectiveness, and creating more human-centered ways of working.

Connect with Gabriela: https://www.linkedin.com/in/gabriellasalvatore

00:00 The System is Broken

00:28 The impact of AI on HR productivity

01:10 The Keyword Trap

03:42 AI as Research Aid

05:08 Candidate Experience Fallout

06:58 Fixing Talent Acquisition

09:18 Strategic Job Design

10:16 HR as Trusted Advisor

14:26 The BANI world

16:44 Tech Stack Pain Points

19:58 Alignment Over Automation

25:46 Onboarding and Growth Gaps

28:11 Two Tier Hiring Reality

30:05 Business Driven Talent Pools

Workforce planning has always been difficult. For years, many organizations treated it as an annual headcount exercise: review the org chart, forecast roles, estimate capacity, and move on. But AI is changing the equation.

In this episode of Work Matters, Thomas Bertels speaks with Ram Venkatraman about how strategic workforce planning needs to evolve as organizations move from AI as a tool, to AI as an assistant, to AI as an autonomous agent. The old workforce planning model focused largely on people, roles, and headcount. The emerging model has to account for human workers, digital teammates, bots, agents, skills, tasks, and changing business models.

Ram argues that organizations need to rethink the classic “build, buy, borrow, and bot” framework and expand it for the age of autonomous agents. That shift requires leaders to deconstruct work at the task level, distinguish between cognitive and non-cognitive work, understand which skills will matter most, and decide where human judgment, empathy, ethics, and orchestration are still essential.

The conversation explores why workforce planning can no longer be a once-a-year HR ritual, why skills inventory is becoming a critical starting point, and why CHROs need to work much more closely with business leaders to understand where the organization is headed. Thomas and Ram also discuss the risks of punching holes into jobs, the danger of overloading humans with only cognitively intense work, and the need to protect meaning, engagement, and wellbeing as AI changes how work gets done.

Ultimately, this episode is about more than workforce planning. It is about how organizations can prepare for a future where humans and agents work side by side — and why the leaders who succeed will be the ones who redesign work with intention, clarity, and care.

In this episode, we discuss:

- Why traditional workforce planning often fails before the year is even underway

- How AI agents are changing the workforce planning equation

- Why organizations need to move beyond headcount and roles toward skills and tasks

- The shift from AI as a tool, to AI as an assistant, to AI as an autonomous agent

- How the classic “build, buy, borrow, and bot” framework may need to evolve

- Why task deconstruction is essential for planning the future workforce

- The difference between cognitive and non-cognitive work

- What it means for managers to orchestrate both human and digital teammates

- Why AI fluency is becoming critical for middle managers and leaders

- How CHROs can partner with the business on future-focused workforce strategy

- Why employee fear, skepticism, and ambiguity must be addressed directly

- The risks of treating AI adoption as a war on humans

- Why wellbeing, engagement, and growth still matter in an AI-enabled organization

Key themes:

Strategic workforce planning, AI agents, agentic workforce, future of work, skills-based organization, task deconstruction, digital teammates, AI fluency, CHRO strategy, change management, human-centered transformation, employee engagement, wellbeing, work design

Memorable idea:

The future of workforce planning is no longer just about forecasting headcount. It is about deciding what work should be done by humans, what work can be done by agents, and how leaders can design a system where both create value.

Guest Bio:

Ram is an experienced management consultant with more than 25 years of experience across firms including EY, KPMG, and IBM. He now works at the intersection of consulting, academia, AI, HR, and talent strategy, including as an Executive in Residence with Rutgers University, where he focuses on AI, HR talent strategies, responsible AI, and future-focused strategic workforce planning. In his view, AI collapses the coordination noise so the human superpower of judgment can lead.

Connect with Ram: https://www.linkedin.com/in/ramvenkatraman/

00:00 Welcome and Background

00:42 Why Planning Fails

02:05 From Headcount to Skills

02:55 Five Bs

03:53 Deconstructing Work

05:51 Knowledge Work in AI Era

09:11 Managing Digital Workers

14:19 Where CHROs Start Now

16:29 Meaningful Work Concerns

22:29 Change Readiness Playbook

26:01 Fear, Engagement, Reality

30:19 Five Year Outlook

32:20 Takeaways

Governance is not usually a word that gets people excited. For many leaders, it brings to mind compliance, bureaucracy, policies, and control. But Jesper Persson sees governance differently.

In this episode of Work Matters, Thomas Bertels speaks with Jesper about why governance should be understood as the way an organization steers itself. At its core, governance is about three practical questions: How do we set expectations? How do we give people the mandate to act? And how do we follow up on performance?

Jesper argues that many organizations struggle with effectiveness not because people are not working hard, but because the steering system is unclear. Functions optimize locally. Processes break down across boundaries. Decisions get escalated too far up the organization. Shadow systems emerge. And over time, people normalize workarounds instead of fixing the underlying operating model.

The conversation explores why functional efficiency is not the same as enterprise effectiveness, why process maps are not the same as process governance, and why large transformations often get stuck in what Jesper calls “the messy middle.” This is the point where the bold vision is clear, and the functions are moving, but the organization has not yet figured out how to prioritize, make decisions, rebalance resources, and resolve cross-functional trade-offs.

Thomas and Jesper also discuss how leaders can diagnose governance problems by asking simple questions across leadership, processes, functio ns, and IT architecture. They explore the risks of governance overload, the importance of feedback loops, and the idea of “balanced centralization” — deciding what minimum set of things must be done the same way across the organization while leaving room for local flexibility.

Ultimately, this episode is about reclaiming governance from bureaucracy. Done well, governance is not about adding more control. It is about creating the clarity, mandate, and feedback loops organizations need to become more effective.

In this episode, we discuss:

- Why governance is often misunderstood as bureaucracy or compliance

- The original meaning of governance as “steering”

- Why governance is central to organizational effectiveness

- The three core governance questions: expectations, mandate, and follow-up

- Why functional performance does not automatically create enterprise performance

- How weak governance creates silos, workarounds, shadow IT, and duplicated effort

- Why process mapping is not the same as process governance

- How unclear mandates drive escalation and slow decision-making

- What happens when multiple governance models overlap without coherence

- Why transformations often get stuck in the “messy middle”

- How leaders can prepare for cross-functional trade-offs before they happen

- Why feedback loops are essential to improving effectiveness

- The concept of balanced centralization

- How organizations can decide what needs to be standardized and what should remain local

Key themes:

Governance, organizational effectiveness, operating model, cross-functional performance, transformation, process governance, functional silos, decision rights, mandate, feedback loops, balanced centralization, messy middle, enterprise effectiveness, work design

Memorable idea:

Governance is not bureaucracy. Governance is how an organization steers itself — by setting expectations, giving people the mandate to act, and following up on performance.

Guest Bio:

Jesper works with organizations on governance, operating models, and cross-functional effectiveness. His interest in governance developed through years of work on complex projects and transformations across industries, where he saw that many performance problems were not caused by effort or capability, but by unclear expectations, mandates, and feedback loops.

Connect with Jesper:

LinkedIn: https://www.linkedin.com/in/jesper-persson-2811771/ Website: http://www.nimbleway.se Article: https://www.linkedin.com/pulse/beware-messy-middle-jesper-persson-ozuff/

00:00 Welcome and Setup

00:47 Why Governance Matters

02:28 Governance Means Steering

04:00 The KPI Mandate Gap

06:08 Process Governance Reality

08:44 Avoiding Escalation Overload

09:57 Manufacturing Case Example

11:47 Culture Under Weak Governance

15:10 Three Questions Diagnostic

18:32 Transformations Messy Middle

22:12 Tollgate Decision Paralysis

24:05 Spotting Governance Signals

30:18 Balanced Centralization Framework

34:16 Wrap Up and Resources