SESSION 1: HYBRID WORKING – CHALLENGES, OPPORTUNITIES AND LESSONS The shift to hybrid work is one of the most significant workplace transformations of the past decade. We will examine opportunities and challenges hybrid work poses for organizational culture, team cohesion, and performance, as well the impact on talent acquisition, diversity, and employee wellbeing. REQUIRED LEARNING MATERIALS: • Article: Gratton, L. (2023). "Redesigning How We Work." Harvard Business Review, March–April 2023. • Podcast: “Nicholas Bloom on the unbundled workplace” https://www.hbs.edu/managing-the-future-of-work/podcast/Pages/podcast-details.aspx?episode=2236831514 • Podcast: Navigating the Hybrid Work Dilemma, https://hbr.org/podcast/2025/04/navigating-the-hybrid-work-dilemma QUESTIONS • What are opportunities and challenges of hybrid work? • What are some of approaches for effective hybrid work?

SESSION 2: DIGITAL WORK AND AMBIDEXTERITY Digitally enabled and transformed work requires individuals, teams organizations to simultaneously optimize existing operations (exploitation) and experiment with new capabilities (exploration), a tension at the heart of organizational ambidexterity. This session examines the foundations of organizational ambidexterity and how digitalization intensifies the exploration–exploitation tension. REQUIRED LEARNING MATERIALS: • Article: Birkinshaw, J., & Gibson, C. (2004). "Building Ambidexterity into an Organization." MIT Sloan Management Review, Summer 2004. • Article: Westerman, G., & Bonnet, D. (2015). "Revamping Your Business Through Digital Transformation." MIT Sloan Management Review, Spring 2015. • Article: Liu, S. (2026). Algorithmic ambidexterity: rethinking exploration and exploitation in the age of AI. In Handbook of Artificial Intelligence and Strategy (pp. 71-90). Edward Elgar Publishing. QUESTIONS • How do individuals, teams and companies manage organizational ambidexterity? • How does digital technology impact the management of organizational ambidexterity?

SESSION 3: AI-ENHANCED WORK Artificial intelligence (AI) is reshaping the nature of knowledge work at the individual level, augmenting human capability in ways that are both profound and uneven. This session critically examines the current evidence on AI-driven productivity gains and explores the practices for harnessing AI technology. The session also explores the types of tasks and roles most amenable to AI support distinguish between tasks within and beyond the current 'jagged technological frontier.'. REQUIRED LEARNING MATERIALS: • Article: “Discovering AI’s jagged frontier — and what we’ve learned since” https://professorkl.substack.com/p/discovering-ais-jagged-frontier-and • Article” Holmström, J., & Carroll, N. (2024). How organizations can innovate with generative AI. Business Horizons. • Podcast: "AI Is Not Improving Productivity: Nobel Laureate Daron Acemoglu" https://sloanreview.mit.edu/audio/ai-is-not-improving-productivity-nobel-laureate-daron-acemoglu/ QUESTIONS • What does the ‘jagged technological frontier” mean for AI-supported work? • How does AI support innovation work?

SESSION 4: SAFELY USING AI FOR WORK As generative AI (GenAI) technologies are increasingly integrated into our work processes, it is essential to understand not just what GenAI can do but also how we should use it safely and responsibly. This module will examine some of the major categories of risks that come with using GenAI to do work. We will review examples of these risks and explore the different types of AI work associated with some of these risks. We will also work through and apply the AI Risk Identification Scoring & Containment framework (AI-RISC) for recognising, evaluating, and mitigating potential failure modes that can come with the different categories of risk. REQUIRED LEARNING MATERIALS: • Article: Hannigan, T. R., McCarthy, I. P., & Spicer, A. (2024). Beware of botshit: How to manage the epistemic risks of generative chatbots. Business Horizons, 67(5), 471-486. • Article: Isik Ö.; Joshi, A., & Goutas L. (2024) 4 Types of Gen AI Risk and How to Mitigate Them. Harvard Business Review. May, 2024 • Podcast: "Navigating the Future of AI Governance" https://www.brookings.edu/articles/navigating-the-future-of-ai-governance-the-techtank-podcast/ QUESTIONS • What are the different categories of risk that come with AI use? • What the different types and modes of failure that can occur for each category of risk.

SESSION 5: AI WORK AND SYSTEMS THINKING In this session you will explore how systems thinking principles and tools can be used to understand how to use AI effectively and safely. Rather than treating AI as a standalone technology, your will learn how it is a technology whose value and risks emerge from how people, processes, and AI interact. You will gain insight into systems principles and tools to help you better frame and take action to create sustainable value, mitigate unintended consequences, and ensure you and organization thrive in an AI-driven future. REQUIRED LEARNING MATERIALS: • Article: Causal Loop Construction: The Basics, By Colleen Lannon • Article: Hannigan, T. R., McCarthy, I. P., & Spicer, A. (2024). Beware of botshit: How to manage the epistemic risks of generative chatbots. Business Horizons, 67(5), 471-486. • Article: Isik Ö.; Joshi, A., & Goutas L. (2024) 4 Types of Gen AI Risk and How to Mitigate Them. Harvard Business Review. May, 2024 • Podcast: "Navigating the Future of AI Governance" https://www.brookings.edu/articles/navigating-the-future-of-ai-governance-the-techtank-podcast/ QUESTIONS • What are the different categories of risk that come with AI use? • What the different types and modes of failure that can occur for each category of risk.

This session introduces the concepts, theories, and methods that can be used to deliver effective and lasting change associated with AI innovation in the workplace. We will examine the different types of change and the frameworks for building momentum, overcoming resistance and guiding people as they transition through change. REQUIRED LEARNING MATERIALS: • Article: De Freitas, J. (2025, January). “Why people resist embracing AI”. Download Why people resist embracing AI. • Article: Leading Change Why Transformation Efforts Fail, by John Kotter • Article Confronting Indifference Toward Truth by Ian McCarthy et al. • Podcast: “How the practice of ‘painstorming’ improves change leadership” https://www.futureoffieldservice.com/painstorming-change-leadership/ QUESTIONS • Think about examples of change you have experienced where the proposed initiative was reasonable or even excellent, but the implementation or rollout of the initiative was highly problematic. Identify the organizational and management factors that made the change unsuccessful.

SESSION 7: LABOUR LAW, TECHNOLOGY, AND THE FUTURE OF WORK This session introduces the legal foundations of work regulation in the digital age. It examines why labour law traditionally relies on the distinction between subordinate employment and self-employment, and why digital, platform-based, and AI-mediated forms of work challenge that distinction. The session also introduces the ILO decent work framework and the role of international labour standards in governing technological transformation. REQUIRED LEARNING MATERIALS: • Professor's slides • ILO, Rules of the Game: An introduction to the standards-related work of the International Labour Organization, selected chapters. • Treu, T., "The ILO and the future of work”. QUESTIONS: • Why does labour law rely so heavily on worker classification? • Are traditional concepts such as subordination and employer control still adequate in digital work? • What role can international labour standards play when work is organized across platforms, borders, and algorithmic systems?

SESSION 8 - PLATFORM WORK AND EMPLOYMENT STATUS This session focuses on platform work as a central legal test case for the future of work. It examines food delivery, ride-hailing, and online micro-task platforms, focusing on the distinction between employee, self-employed worker, and intermediate categories. The session also considers the Italian experience with hetero-organized work, litigation concerning riders, and comparative European case law on platform workers. REQUIRED LEARNING MATERIALS: • Professor's slides • Hiessl, C. (2022). "The legal status of platform workers: regulatory approaches and prospects of a European solution." Italian Labour Law e-Journal. • Defossez, D. (2021). "The employment status of food delivery riders in Europe and the UK: Self-employed or worker?" Maastricht Journal of European and Comparative Law. Suggested legal and policy materials: • Directive (EU) 2024/2831 on improving working conditions in platform work. • Selected national and EU case law on riders, platform workers, and algorithmic control. QUESTIONS: • Is platform work genuinely autonomous, or does it create a new form of digital subordination? • Should platform workers be reclassified as employees, protected as self-employed workers, or regulated through an intermediate category? • What does the Italian rider experience reveal about the limits and potential of intermediate legal categories?

SESSION 9 - EU REGULATION OF PLATFORM WORK AND ALGORITHMIC MANAGEMENT This session examines the EU regulatory response to platform work, with particular attention to the determination of employment status, transparency, algorithmic management, automated decision-making, worker representation, and enforcement. The session compares the regulatory ambitions of the EU Platform Work Directive with the limits of GDPR-based protection and broader debates on AI at work. REQUIRED LEARNING MATERIALS: • Professor's slides • Maes, S. (2023). "EU Platform Workers' Directive: A test for regulating the future of work." Policy Paper. Suggested legal and policy materials: • Directive (EU) 2024/2831, especially provisions on employment status, algorithmic management, transparency, human oversight, worker information, and consultation. QUESTIONS: • Is a rebuttable presumption of employment an effective tool against bogus self-employment? • What information should platform workers and their representatives receive about algorithmic management? • Should the regulation of algorithmic management apply only to platform work or to all workplaces?

SESSION 10 - ALGORITHMIC MANAGEMENT, DATAFICATION, AND WORKER PRIVACY This session focuses on algorithmic management beyond platform work. It examines how employers use algorithms and digital tools to allocate tasks, set pay, evaluate performance, monitor productivity, and discipline workers. The session connects labour law, data protection, occupational health and safety, worker dignity, and the emerging EU AI Act framework. REQUIRED LEARNING MATERIALS: • Professor's slides • Selected materials on GDPR and worker monitoring. QUESTIONS: • When does algorithmic management become employer control? • Can productivity tracking, biometric timekeeping, and remote surveillance be reconciled with worker dignity and privacy? • Does the EU AI Act solve labour law problems, or does it mainly add a compliance layer?

SESSION 11 - AI RECRUITING, EQUALITY, AND NON-DISCRIMINATION This session examines AI-based recruitment and HR decision-making. It analyzes automated CV screening, video interviews, personality profiling, predictive hiring, and workforce analytics. The legal focus is on data protection, anti-discrimination law, indirect discrimination, proxy variables, black-box decision-making, causality, and burden of proof. REQUIRED LEARNING MATERIALS: • Professor's slides • Malorny, F., and Rieger, T. "AI-driven recruiting: A consideration on data protection- and anti-discrimination law." Italian Labour Law e-Studies. • Selected case materials on algorithmic discrimination in recruitment. Suggested legal and policy materials: • Directive 2000/43/EC, Directive 2000/78/EC, and Directive 2006/54/EC. QUESTIONS: • Can an apparently neutral algorithm produce indirect discrimination? • How should the law deal with proxy variables such as residence, employment history, availability, career gaps, or language use? • Who should bear the burden of proof when discriminatory outcomes are generated by non-transparent AI systems?

SESSION 12 - - PAY TRANSPARENCY, GENDER EQUALITY, AND STRATEGIC HR LEGAL RISK MANAGEMENT This final session brings together equality, transparency, accountability, and regulatory design. It examines the gender pay gap, pay transparency, people analytics, and the use of workplace data as both a risk and a tool for equality. The session closes the course by asking how organizations should design legal risk management systems for technology-enabled work, combining statutory rights, social dialogue, algorithmic transparency, audits, human oversight, and internal governance. REQUIRED LEARNING MATERIALS: • Professor's slides • Zilli, A. (2021). "EU Strategy against gender pay gap through wage transparency: the best is yet to come." Italian Labour Law e-Journal. Suggested legal and policy materials: • Directive (EU) 2023/970 on pay transparency and equal pay for equal work or work of equal value. QUESTIONS: • Can transparency correct information asymmetries in employment relationships? • Can workplace data be used in a pro-equality way without undermining privacy? • What is the best regulatory mix for technological work: statutory rights, algorithmic transparency, collective bargaining, audits, human oversight, or litigation?