The Future Of Less Work

What happens when AI moves into places where error is not an option like banking approvals, medical claims, pharmaceutical compliance, the future of work becomes legal, operational, and deeply human.In this episode of The Future of Less Work, host Nirit Cohen sits down with Arya Bolurfrushan, Founder and CEO of AppliedAI, to explore what happens when automation enters highly regulated, mission-critical workflows where every decision must be auditable, defensible, and accountable.Together, they unpack the concept of “supervised automation” where AI performs the bulk of the execution while humans remain responsible for final judgment, mid-process checkpoints, and liability. Arya explains why fully autonomous systems struggle in regulated environments, how “compliance as code” can embed legal constraints directly into AI workflows, and why accountability cannot simply disappear as machines take over execution. What does it mean to be accountable when you no longer do most of the work? How do professionals develop judgment if entry-level “grunt work” disappears? And who carries risk, reputation, and responsibility in an AI-native organization?If you’re leading AI transformation in regulated industries—or simply wondering what remains uniquely human when machines execute most of the process, this episode will challenge how you think about work, accountability, and meaning in the age of AI.https://youtu.be/F9Ty5yLKx0Y Guest Information:Arya H. Bolurfrushan is the Founder and CEO of AppliedAI, the world's most boring artificial intelligence company focused on increasing productivity of mission-critical workflows in regulated industries by an order of magnitude.Previously, Arya co-founded Accrete Capital, a technology-backed investment platform democratizing alternative investments that has deployed over $2B in equity. Before that, Arya served as GM, CFO and on the Nominations Committee of the Board of Directors of RAK Petroleum, taking the company public on the Oslo Stock Exchange (RAKP.OL). He began his career at Goldman Sachs’s Investment Strategy Group in New York, focused on private equity and technology.In addition to studies at Oxford, Stanford, Cambridge, and the Cordon Bleu, Arya received his Master of Science and Bachelor of Science in the application of computer science in industry from Carnegie Mellon and an MBA from Harvard Business School.  Chapters:00:00 What Happens When AI Enters Highly Regulated Industries?00:47 How Do You Design AI Where Error Is Not An Option?02:16 How Do Companies Safely Deploy AI In Regulated Environments?02:44 What Is Supervised Automation In AI Workflows?04:50 Why Can’t You Fully Automate Regulated Industries?05:51 Who Is Liable When AI Makes Decisions?09:06 Is Human Oversight In AI A Temporary Or Permanent Model?10:40 Does AI Make Knowledge Work Better Or Worse?12:20 How Does AI Increase Productivity In Regulated Workflows?12:46 How Do Workers Develop Judgment In AI-Driven Jobs?13:48 Will AI Eliminate Entry-Level Jobs In Regulated Industries?16:36 Can You Train Judgment Without Hands-On Experience?17:31 How Does AI Force Companies To Document Hidden Knowledge?17:53 How Should Regulation Change For AI In Critical Work?18:51 Where Should Humans Fit In AI-Native Workflows?19:45 Why Does AI Require Business Process Reengineering?20:14 Why Is AI Transformation A Human Problem Not A Tech Problem?20:58 Why Is Innovation Harder In Highly Regulated Industries?21:30 Can AI Embed Compliance Directly Into Workflows?23:45 What Will AI In Regulated Industries Look Like In 5 Years?24:58 Will Regulation Protect Human Jobs In The Age Of AI?28:15 What Happens If Work Is No Longer Needed For Income?29:29 Who Should Benefit From AI Productivity Gains?31:23 Why Do Employees Resist AI Adoption?32:41 Why Should Labor Be Priced By Output Instead Of Time?

As AI accelerates across organizations, most conversations still focus on tools, pilots, and productivity gains. But AI is also exposing cracks that were already there - misaligned leadership, siloed systems, fragile middle layers, and outdated assumptions about how transformation works. In this episode of The Future of Less Work, host Nirit Cohen sits down with Barbara Wittmann, founder of the Digital Wisdom Collective and former interim CIO, to explore why AI is less a technology shift and more a leadership mirror. Barbara argues that the real constraint in AI transformation isn’t infrastructure - it’s human infrastructure. Together, Nirit and Barbara unpack why middle management is the critical layer where strategy meets execution. They explore how AI surfaces governance gaps, weak data foundations, and cultural misalignment faster than any previous technology wave. Barbara challenges leaders to look under the hood and redesign how people, systems, and decision-making work together. The conversation dives into what “Digital Wisdom” really means - the human capacity for asking better questions, sensing system dynamics, aligning across functions, and building collective intelligence in an era of accelerating change. They also examine why HR and IT can no longer operate in parallel universes, and why transformation must shift from episodic shake-ups to continuous evolution.Finally, Barbara offers practical advice for managers in the middle: build coalitions of the willing, step out of the echo chamber, and strengthen the uniquely human capabilities that AI cannot replicate.If you’re navigating AI transformation and wondering whether the real upgrade needed is technical or human, this conversation will challenge how you see both leadership and work itself.https://youtu.be/fai6Nofk4t4Guest Information:Barbara Wittmann is the founder of the Digital Wisdom Collective, helping organizations unlock the human side of AI and transformation. A strategist, advisor, and former interim CIO, she specializes in aligning business, IT, and people by developing the “Human Infrastructure” that makes technology work. Barbara is known for her work with global companies, her cross-industry leadership programs, and her bold message that the middle layer holds the key to the future of work.Links:www.digitalwisdom.cohttps://www.linkedin.com/in/barbarawittmann/https://www.linkedin.com/company/digitalwisdomcollective/https://substack.com/@barbarawittmann/Chapters:00:00 What Is Digital Wisdom In The Age Of AI01:06 Why Do We Need A Digital Wisdom Collective02:15 What Is The Human Advantage Over AI03:21 Do Humans Need To Reskill For AI03:57 Why Answers Are Cheap And Questions Matter More04:24 How AI Changes Organizational Thinking Models05:04 How Should Leaders Redesign Work With AI05:37 Why Middle Management Is Critical In AI Transformation06:01 Should Companies Eliminate Middle Management06:03 Why You Can’t Delegate Complexity To AI07:30 Why AI Pilots Fail Without Process Redesign08:22 What Is Human Infrastructure In Organizations09:18 Why HR And IT Must Work Together In AI10:34 How AI Transformation Is Different From Digital Transformation11:09 How AI Exposes Organizational Misalignment11:45 Why AI Pilots Often Fail In Companies13:10 Does AI Transformation Start From The Bottom Up13:45 Why Poor Data Breaks AI Strategy14:16 Is AI Just Automation Rebranded15:26 Why Companies Must Fix Data Before AI16:18 Who Is Responsible For AI Transformation17:01 Should HR And IT Be Combined18:15 What Should Managers Do Differently With AI18:29 How To Build A Coalition Of The Willing At Work19:42 Why Transformation Must Become Continuous20:51 What Is The Most Important Future Of Work Question21:20 How Humans And AI Will Co-Create Work22:16 How Do You Find Your Unique Human Value23:08 Why You Must Step Outside Your Echo Chamber24:11 How To Discover Your Unique Value At Work24:49 Why Self-Awareness Matters More In The AI Era

AI is no longer just another workplace technology. It is a general-purpose capability that is beginning to reshape how work is designed, how decisions are made, and how organizations create value.In this episode of The Future of Less Work, host Nirit Cohen sits down with David Mallon, Chief Futurist and Head of Research for Deloitte’s Human Capital practice, to unpack the biggest insights from the 2026 Deloitte Global Human Capital Trends report.  The conversation explores why this moment of AI adoption is fundamentally different from past technology shifts. Unlike earlier tools that automated tasks, generative AI is entering knowledge work itself, acting as collaborator, analyst, and decision partner. That shift forces organizations to rethink how humans and machines work together and how leadership, productivity, and expertise evolve in an AI-enabled workplace.Nirit and David discuss why many companies are approaching AI through a narrow productivity lens, focusing on efficiency rather than redesigning work around human-machine collaboration. They examine the growing need for leaders to intentionally design how people interact with AI systems, orchestrate work across humans and intelligent tools, and rethink performance when technology can dramatically amplify individual output.The episode also explores a deeper challenge: how workers develop expertise when AI increasingly performs the early tasks that traditionally built experience. As organizations move faster to adopt AI, leaders must decide how to balance productivity, learning, and human judgment in a workplace where machines are part of the team.If you’re trying to understand what AI really means for organizations—not just tools, but the design of work itself—this conversation looks at the choices leaders must make as human and machine collaboration becomes the new operating model of work.Want to dive deeper into this topic? Read Nirit’s Forbes article, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠"⁠⁠⁠⁠AI Is Creating Culture Debt In Organizations⁠⁠", to explore these ideas further.  https://youtu.be/1KJeMLNXyZEGuest Information:David Mallon, a managing director at Deloitte Consulting LLP, is the head of research and chief futurist for Human Capital in the United States. With more than 25 years of experience in human capital, he helps organizations sense, analyze, and act with purpose. Mallon has been a key contributor to Deloitte’s Global Human Capital Trends study since its inception and leads Insights2Action—Deloitte’s Human Capital decision intelligence capability. Links: 2026 Human Capital Trends report: https://www.deloitte.com/us/en/insights/topics/talent/human-capital-trends.htmlForbes article on Culture Debt: https://www.forbes.com/sites/niritcohen/2026/03/04/ai-is-creating-culture-debt-in-organizations/Chapters:00:00 — What Is the Deloitte 2026 Human Capital Trends Report About?01:29 — Why Is AI Different From Previous Technology Revolutions?05:46 — Will AI Replace Human Thinking or Augment It?10:03 — How Will Workers Build Experience If AI Does the Work?13:28 — How Should Humans and AI Work Together at Work?17:49 — Are Companies Using AI Only for Productivity Gains?21:09 — What Must Change in Leadership for the AI Era?23:09 — What Surprised Researchers in the 2026 Human Capital Trends Report?24:01 — What Is Culture Debt and Why Should Leaders Care?26:57 — Are Executives Trusting AI Decisions Too Much?27:47 — What Is the Most Important Leadership Insight About AI?28:54 — How Should Organizations Design Human–AI Collaboration?31:46 — What Question Should Leaders Ask About the Future of Work? 

What if the biggest barrier to AI transformation isn’t technology at all, but the fact that most organizations don’t actually understand how work gets done?In this episode of The Future of Less Work, host Nirit Cohen sits down with Vijay Swaminathan, co-founder and CEO of Draup, to explore what really breaks when AI meets industrial-age assumptions about jobs, roles, and headcount.The conversation begins with a fundamental shift in strategic workforce planning. Once a headcount exercise buried inside HR, it is now being pulled into the center of enterprise strategy as organizations try to allocate work between humans, machines, contractors, and AI agents. Vijay explains why traditional job descriptions no longer reflect reality, how large portions of work remain hidden inside workflows and processes, and why this invisible layer holds the greatest opportunity for AI-driven productivity.Together, Nirit and Vijay unpack how roles are fragmenting into builders, orchestrators, and synthesizers, why labor arbitrage is losing its power, and how leaders often underestimate the complexity of the human work that remains after automation. They also explore why metrics of power, control, and success built around headcount and org charts are starting to collapse, and what replaces them.This episode is a deep dive into the uncomfortable truth behind AI transformation: before organizations can redesign jobs, they must first see the work itself. And that shift, from org charts to workflows, may be the hardest change of all.If you’re trying to make sense of AI, skills volatility, and the future of workforce planning, this conversation offers a clear lens into what’s already changing beneath the surface. https://youtu.be/zkSdBx8L8zM Guest Information:Vijay Swaminathan is the Co-Founder & CEO of Draup, an AI copilot that helps global enterprises make strategic talent decisions. A recognized thought leader in the talent space, Vijay brings deep expertise in product ideation, concept-to-product transitions, and platform enablement. His career is marked by a strong command of data analytics, operations research, and strategic management. Vijay has designed numerous quantitative models and heuristics focused on global talent dynamics, cutting-edge business analytics, and strategic business maneuvers. He is also the co-founder of Zinnov, a leading research & advisory firm, and TalentNeuron, which was acquired by CEB, a Gartner company (NYSE: IT). Previously, Vijay held senior positions at Hewitt Associates and KPMG Consulting. Links:https://draup.com/talent/ceo-newsletter/mastering-ai-readiness-for-hr-leadershttps://draup.com/talent/ceo-newsletter/the-hidden-work-in-hr-building-on-mits-project-iceberg https://draup.com/talent/ceo-newsletter/tech-talent-strategies-of-2025-draups-annual-reporthttps://draup.com/talent/ceo-newsletter/the-new-frontier-of-strategic-workforce-planning Chapters:00:00 – How AI Is Changing Strategic Workforce Planning01:25 – What Does Strategic Workforce Planning Mean in the Age of AI?05:35 – Why Job Descriptions No Longer Reflect Real Work07:48 – How AI Exposes Hidden Work Inside Organizations09:25 – Can AI Read Process Maps and Redesign Workflows?11:02 – Why Companies Struggle to Document Real Tasks12:58 – Builders vs Orchestrators: Who Actually Uses AI in Enterprises?15:38 – Where Leaders Oversimplify AI Workforce Transformation17:25 – Why Human Verification Gets More Complex With AI20:09 – What Happens to Work in an AI-Driven Organization?22:36 – Why AI Forces Companies Beyond the Org Chart24:23 – Rethinking Headcount Models in the Age of AI26:21 – Should Workforce Planning Focus on Workflows Instead of Jobs?

When work breaks apart into tasks and AI steps in as a real participant, not just a helper, the familiar structure of jobs begins to unravel. The real question becomes how work gets recomposed, who orchestrates it, and how people redefine their value when execution is no longer the core of the role.In this episode of The Future of Less Work, host Nirit Cohen sits down with Carrol Chang, President of Andela, to explore what happens when the atomic unit of work shifts from jobs to tasks. Drawing on her experience leading global talent systems and marketplaces, Carrol explains why AI is forcing organizations to rethink not just productivity, but how work is allocated, priced, managed, and ultimately experienced.Together, Nirit and Carrol unpack why breaking work into tasks doesn’t eliminate the need for humans, but radically elevates it. As AI takes on execution, people move into roles that demand judgment, coordination,feedback, and orchestration. The conversation explores why every individual contributor is becoming more like a manager, how professional identity evolves when tasks change faster than titles, and why leadership now depends on setting expectations for continuous role reinvention.The episode also looks at what this shift unlocks beyond organizational boundaries. As work becomes more modular, highly specialized skills can be deployed across multiple projects, opening the door to new ways of earning, learning, and balancing life. Platforms, AI-enabled onboarding, and global talent networks emerge as the infrastructure that makes this possible, allowing work to scale without forcing everyone into a 40-hour week designed for the industrial era.If you’re thinking about how AI reshapes careers, why flexibility is moving upstream into high-skill work, and what it really means to design work in a world where intelligence is abundant, this conversation offers a grounded and human-centered lens on what comes next.https://youtu.be/nYkUjZBL0sEGuest Information:Serving as CEO of Andela since September 2024, Carrol Chang is committed to scaling the business while remaining true to its mission-driven approach of connecting brilliance with opportunity. She joined Andela from Uber, where she led efforts to improve work for nearly 7 million flexible workers around the world as the Global Head of Driver & Courier Operations.Carrol has held positions with McKinsey Company, Portraits of Hope, and the administration of President Barack Obama. She is passionate about expanding opportunity in all forms to underrepresented populations and making commerce more generous and kind for all stakeholders.  She holds a BA from Harvard and both a JD and MBA from Northwestern University. Chapters:00:00 Why Jobs Are No Longer the Basic Unit of Work01:36 What Does It Mean to Break Jobs Into Tasks With AI?03:44 Can Task-Based Work Scale Inside Large Organizations?04:10 How AI Turns Every Employee Into a Manager05:40 Do Companies Need to Redesign Jobs After AI?07:44 How Should Professionals Redefine Their Identity in the Age of AI?09:39 Will AI Change Job Titles and Organizational Structures?11:41 Do We Need to Rebundle Work Into New Job Structures?13:29 Can Specialized Skills Be Deployed Across Multiple Projects?15:51 How Can Organizations Manage Work Done by Fractional Talent?17:00 Is This the Evolution of the Gig Economy for High-Skilled Work?18:45 How AI Makes Platform-Based Work Scalable for Enterprises19:36 What Happens to Culture When Work Is Unbundled?21:49 Can AI Accelerate Employee Onboarding and Culture Fit?23:42 How Company Email Norms Reveal Organizational Culture24:30 Will AI Reduce the 40-Hour Workweek?26:30 How Should Leaders Prepare for the AI Change Curve?

Bhavin Shah, co-founder and CEO of Moveworks (now part of ServiceNow), builds agentic AI that automates workplace workflows. He discusses frontline-led AI innovation. He explains how agentic systems take action across systems, the move from shadow IT to sanctioned autonomy, practical guardrails for trust, and how routine work shifts to higher-value roles.

Rebecca Hinds, organizational researcher who founded Asana’s Work Innovation Lab and the Work AI Institute, and author of Your Best Meeting Ever. She explains why meetings became broken. She introduces the 4D test for deciding when a meeting is needed. She critiques hour-long defaults and suggests shortening meetings. She explores AI’s mixed impact and which recurring meetings to cut first.

Tim Sanders, Chief Innovation Officer at G2 and Harvard digital fellow, explores how AI agents shift work from repetitive tasks to human judgment. He discusses jobs-as-tasks-and-judgment, augmentation versus substitution, the move from productivity to velocity, trust and adoption hurdles, new roles like agent designers, and how workflow design determines whether AI speeds or stalls organizations.

Becky Frankiewicz, President & Chief Strategy Officer at ManpowerGroup, a leader in workforce solutions, breaks down ManpowerGroup’s 2026 Global Talent Barometer. Short takes cover workers’ confidence vs. AI anxiety. They explore job hugging, internal mobility, skills rising over degrees, and why human reasoning and creativity matter as AI automates tasks.

Arjan Singh, author and strategy consultant who teaches at SMU, specializes in corporate war games and scenario planning. He discusses using war games to rehearse disruptive futures. He explains shifting from annual plans to continuous strategy sprints. He explores how AI makes data abundant while human judgment and culture remain crucial. He outlines changing manager roles and practicing comfort with ambiguity.