Key Insight: People Need Safety and Status

The authors argue that AI adoption doesn鈥檛 stall because employees are irrationally anti-tech. It stalls because, from their point of view, the risks are personal and immediate, while the benefits are abstract and uncertain. The article identifies three intertwined people problems: uncertainty about what AI will actually do, fear of being replaced, and fear of looking less competent. The last of the three, which the authors call 鈥榯he self-image problem鈥�, explains why some professionals secretly use AI but hide it from their colleagues, worried that it makes them look lazy or less skilled. 

Key Insight: AI Only Works When You Redesign the Work Around It

The authors frame this challenge at three levels: nodes (individual workflows), edges (interactions between teams), and the broader network (end-to-end systems). For one example at the edge level, a Japanese cosmetics company used generative AI to turn store-level anecdotes into structured, credible intelligence, enabling headquarters to run faster campaigns and iterate in real time.

Key Insight: AI Rewrites Internal Power

Hierarchy disruption can appear when junior employees using AI outperform their seniors, undermining tenure-based status. The authors report that some firms have responded by explicitly baking AI mastery into competency models and speeding up promotion cycles, so learning new tools pays off quickly. At the same time, managers whose influence is tied to headcount may quietly block automation that would shrink their teams. If AI changes who has information, who has leverage, and who gets credit (or blame), then adoption operates at the intersection of politics and culture.

Why This Matters

For business leaders and executives, capturing ROI from AI won鈥檛 be a function of model sophistication alone. The takeaway is that AI strategy is organization design strategy. You must confront employee fears, rebuild workflows from the ground up, and actively manage the power shifts AI introduces.

If these insights sparked your curiosity, the offers a deeper, research-driven look at the people, processes, and politics that determine whether AI actually delivers value. It鈥檚 packed with data, case studies, and practical examples that go further into the organizational realities, and opportunities, of AI adoption.

References

[1] Li, Jin, Feng Zhu, and Pascal Hua, 鈥淥vercoming the Organizational Barriers to AI Adoption,鈥� 性视界 Business Review, November 11, 2025, . 

[2] Li et al., 鈥淥vercoming the Organizational Barriers to AI Adoption.鈥�

[3] Li et al., 鈥淥vercoming the Organizational Barriers to AI Adoption.鈥�

is Zhang Yonghong Professor in Economics and Strategy, Director of the Centre for AI, Management and Organization (CAMOM), and Area Head of Management and Strategy at Hong Kong University Business School.

is the MBA Class of 1958 Professor of Business Administration at 性视界 Business School, lead of the HBS AI Institute Platform Lab, and co-chair of the 性视界 Business Analytics Program (HBAP). Professor Zhu is an expert on platform strategy, digital innovation and transformation, competitive strategy, and business model innovation.

is National Managing Partner of Technology and Transformation at Deloitte China.

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Key Insight: Mapping AI Resistance

Participants rated a sample from 940 occupations twice: first under current AI capabilities, then imagining AI that can exceed humans at the job while doing so at a lower cost. The researchers also developed and validated a new scale meant to measure moral repugnance towards AI, the perception that using AI in certain contexts is inherently wrong, irrespective of benefits. This scale thereby taps into fundamental concerns about human dignity, betrayal, and categorical prohibitions that no amount of engineering can overcome. As a result, the researchers came to distinguish two fundamentally different sources of resistance to AI: performance-based concerns and principle-based objections.

Key Insight: Performance Concerns Fade Fast

The researchers identify performance-based resistance to AI as opposition due to AI鈥檚 current technical capabilities, including factors such as accuracy, reliability, cost, and speed. We might expect this type of resistance to recede as AI technology becomes more capable and cost-effective over time, a result backed up by the study. This was especially true for occupations that were deemed morally permissible for AI help (augmentation) and replacement (automation) like clerks, transportation planners, and data entry keyers. 

Key Insight: The Principle Line

Other occupations, including clergy, childcare workers, and therapists, fall into the category of principle-based resistance towards AI. In these cases, AI faces complete rejection that doesn鈥檛 budge even when it鈥檚 positioned as better, faster, and cheaper. The use of AI in these roles is deemed morally repugnant regardless of capability. What makes these occupations special? They share common threads of caregiving, emotional labor, public speaking, and spiritual leadership. The researchers highlight that the dynamic between AI capabilities and human repugnance creates 鈥渕oral friction zones鈥� where capability meets rejection (e.g. school psychologists and fraud examiners) and 鈥渓atent zones鈥� where acceptance is actually ahead of current ability (e.g. cashiers, conveyor operators). [4]

Why This Matters

For business leaders and executives, this research is both liberating and sobering. Liberating, because a large share of public hesitation is performance-based: as your models improve, acceptance will follow in line. Sobering, because a line remains where AI is judged intrinsically inappropriate. The strategic response isn鈥檛 abandoning AI, but designing hybrid solutions that preserve human touchpoints in morally sensitive tasks, carefully framing AI as augmentation rather than replacement, and investing in transparency and ethics communication. 

Bonus

Just as this research shows that better AI doesn鈥檛 guarantee broader acceptance, earlier HBS AI Institute work revealed that improving AI capabilities can actually reverse inequality effects in unexpected ways. For more on how AI鈥檚 relationship with workers shifts as technology advances, check out Who Benefits When Bots Get Better?

References

[1] Simon Friis and James W. Riley, 鈥淧erformance or Principle: Resistance to Artificial Intelligence in the U.S. Labor Market,鈥� 性视界 Business School Working Paper No. 26-017 (October 6, 2025): 6, . 

[2] Friis and Riley, 鈥淧erformance or Principle,鈥� 5.

[3] Friis and Riley, 鈥淧erformance or Principle,鈥� 5.

[4] Friis and Riley, 鈥淧erformance or Principle,鈥� 16.

Meet the Authors

is a postdoctoral fellow at the Laboratory for Innovation Science at 性视界 (LISH), part of the HBS AI Institute. His research focuses on the social and economic impacts of generative AI.

is an Assistant Professor of Business Administration in the Organizational Behavior unit at 性视界 Business School. He is an economic sociologist, conducting ethnographic research to produce qualitative studies on the role of status, norms, social valuations, and organizational culture within innovation-driven organizations, creative industries, and cultural markets.

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Jen Stave Jen Stave

Key Insight: Agentic AI as Managed Teammates

Agentic tools are moving beyond chatbots and image generation. Unlike traditional automation that follows rigid scripts, AI agents function more like human employees: capable of independent decision-making after being given high-level goals and objectives.

Key Insight: An Uncertain Future

On one hand, the impact of AI is already here and being measured, as evidenced by how the use of AI agents at Salesforce led to a 17% customer service cost reduction over nine months. But the article also raises a range of undecided questions related to economic capture, quality and accountability, and the right balance between human and AI worker numbers.

Why This Matters

For forward-thinking executives, increasingly the question isn鈥檛 whether to adopt agentic AI, but how to operationalize it productively and responsibly. While the efficiency gains are compelling, success requires thoughtful integration by leaders who are ready to address challenges of workforce transition, quality control, and ROI measurement.

Bonus

To read more about Agentic AI and digital labor, read 鈥�,鈥� co-authored by Jen Stave, for the 性视界 Business Review.

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Key Insight: Falling Asleep at the Wheel

One of the most striking findings Professor Lakhani mentioned came from the HBS AI Institute study with Boston Consulting Group (BCG). When used for tasks within its strengths, AI can catapult average performers to the 95^th percentile, meaning that expertise is no longer scarce and businesses can be filled with entire teams of top performers. However, even high performers saw their results decline when AI was applied to tasks outside of its current capabilities, a phenomenon HBS postdoctoral researcher calls 鈥淔alling Asleep at the Wheel.鈥�

Key Insight: From Tool to Teammate to Boss

An HBS AI Institute study with Procter & Gamble (P&G) showed that AI can help individuals and teams to produce higher quality ideas, 鈥渄emocratizing鈥� expertise by leveling the playing field. Beyond productivity gains, AI functioned as a collaborative partner, providing balance across domains and enabling those with technical expertise to incorporate a commercial perspective into their innovation efforts, and vice-versa for those with commercial expertise. What鈥檚 more, organizations in the future may use AI agents to lead teams. As Lakhani mentioned, Uber already utilizes this operating model by putting algorithms in charge of HR decisions like hiring and firing.

Key Insight: Exponential Acceleration

The speed of AI advancement, compared to how most companies are adopting and integrating these tools, is creating a widening gap that smart executives will target. Unlike previous technologies such as WiFi or web browsers that organizations could evaluate slowly, AI fundamentally changes the nature of work itself, and companies that fail to keep pace may find themselves behind competitors who successfully ride the AI wave.

Key Insight: The Playbook

At the end of his talk, Lakhani outlined a strategic framework for leaders navigating the AI revolution. Learning requires continuously understanding AI鈥檚 capabilities and impact, and growing your AI skillset. Doing means actually using AI tools, and in particular executives need to get their feet wet with AI rather than just delegating experimentation to their employees. Imagining involves conceiving new operating models and workflows that AI can unlock. Acting requires driving organizational change to accommodate these new ways of working.

Bonus: in a recent article for the 性视界 Business Review, Lakhani and several co-authors added a fifth step to this playbook. Learn what it is here.

Why This Matters

For business leaders across industries, the HBS AI Institute鈥檚 research underscores that AI is reshaping business fundamentals. Understanding AI鈥檚 dual role as a democratizing force in expertise and an accelerating differentiator is crucial for future-proofing your organization. Understanding its strengths and weaknesses, fostering AI-augmented teamwork and keeping pace with AI advancement are essential for maintaining a competitive edge. Embrace AI strategically, invest in continuous learning, and be prepared to transform your organization鈥檚 approach to work.

About the Speaker

is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is also the Co-Founder and Faculty Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界.

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For the session , Bobby Yerramilli-Rao, Chief Strategy Officer at Microsoft, and HBS AI Institute co-founder Karim Lakhani discussed the far-reaching implications of AI on business operations, organizational structures, and strategic planning. Their insights and research offer a compelling vision of how companies must adapt to thrive in an era of proliferating access to expertise.

Key Insight: Expertise is No Longer Scarce, it鈥檚 Scalable

The most immediate impact of AI is appearing in productivity and performance, with gains that defy traditional economic expectations. AI is effectively raising the floor of competency on difficult tasks that once required years of specialized training across a wide range of fields. Expertise, which used to be a key driver of competitive advantage, is now democratized, and the implications are seismic.

Key Insight: You are More Than an Individual

Yerramilli-Rao and Lakhani discussed a future where employees regularly incorporate their own AI agents into their work, and even bring them along across jobs and educational experiences. According to Yerramilli-Rao and Lakhani, companies will need to integrate these AI agents into their systems while maintaining control, governance, and security. For hiring purposes they will need to identify individuals who can effectively collaborate with human-AI teams. The outcome will be flatter structures and less-siloed employees compared to traditional departmental architecture. One vivid example the speakers gave was Focus Fuel, a startup launched by three friends working part-time using GPT tools to develop, market, and scale a new consumer product, all without prior Consumer Packaged Goods (CPG) experience.

Key Insight: Know Your Core Value Proposition

The competitive landscape may be entering a phase of continuous acceleration where companies must simultaneously leverage AI while preparing for advances in AI to match and then exceed their current capabilities. If AI levels the playing field, companies must clarify what truly sets them apart. What are you uniquely good at, and what expertise is replicable by AI or your competitors using AI?

Why This Matters

For business leaders, these insights signal the beginning of a new era where strategic value comes from focus, speed, and broad AI implementation. Those who treat this as a technology upgrade rather than a fundamental shift risk being outpaced. The question is no longer whether AI will transform your industry, but whether your organization will lead or scramble to catch up. Embracing these changes and proactively reshaping your organization around AI capabilities may be the key to unlocking previously unheard of levels of innovation, efficiency, and success in the years to come.

Read their article .

Meet the Speakers

is Chief Strategy Officer at Microsoft. He has co-founded several companies, and has served at organizations including Vodafone and McKinsey. He holds an MA from the University of Cambridge and a PhD from the University of Oxford.

is the Dorothy & Michael Hintze Professor of Business Administration at 性视界 Business School. He specializes in technology management, innovation, digital transformation, and artificial intelligence. He is the Co-Founder and Chair of the HBS AI Institute and the Founder and Co-Director of the Laboratory for Innovation Science at 性视界.

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Key Insight: The Prevalence of Managerial Sabotage

Research highlights the reality that managerial sabotage is widespread in corporate environments. Zaman and Lakhani鈥檚 study reveals that over 70% of executives have witnessed such behaviors during their careers, with nearly one-third observing sabotage directly within their organizations. In addition, approximately 28% of survey respondents said they were victims of TDS within their current organizations, and 60% were affected by it during their careers.

Key Insight: The Root Cause鈥擣ear

The research identifies the root cause of managerial sabotage: fear. Managers, particularly in hierarchical organizations, may perceive talented subordinates as threats to their status and pride. This insecurity drives them to pre-emptively undermine their team members, which can hurt employees鈥� careers and the organization鈥檚 culture and performance.

Key Insight: The Role of Relative Performance Evaluations (RPEs)

The study delves into the impact of relative performance evaluations (RPEs), a common method used to assess employees by comparing their performance. While RPEs can drive productivity, they may also inadvertently encourage sabotage, particularly when managers have significant discretion in determining promotions. Zaman and Lakhani found that firms relying heavily on subjective RPE systems saw a marked increase in sabotage incidents. By contrast, organizations with more objective and transparent evaluation processes experienced significantly lower levels of sabotage.

Key Insight: Building a Culture That Prevents Sabotage

The research underscores the critical role of organizational culture in combating sabotage. Companies that emphasize open communication, collaboration, and transparency are less likely to experience managerial undermining. Strategies such as implementing and enforcing 360-degree feedback systems (in which feedback is gathered from multiple sources about an employee鈥檚 performance); ensuring performance evaluations are transparent, standard, and objective; and shifting incentives away from individual to team-based performance measures can significantly reduce the fear and competitiveness that drive sabotage.

Why This Matters

TDS is more than a human resources challenge鈥攊t is a strategic business issue with far-reaching consequences. It weakens organizational performance, makes it difficult to attract and retain employees, and can jeopardize succession plans. C-suite and business leaders can address this problem by taking a few key actions: 

• Increasing transparency and objectivity in performance evaluation
• Enforcing the use of 360-degree feedback systems
• Creating a culture of collaboration, openness, and communication
• Aligning incentives with team-based performance metrics

References

[1] Hashim Zaman and Karim R. Lakhani, 鈥淒eterminants of Top-Down Sabotage鈥�, HBS Working Paper 25-007 (August 22, 2024): 1-81, 2.

[2] Zaman and Lakhani, 鈥淒eterminants of Top-Down Sabotage鈥�, HBS Working Paper 25-007 (August 22, 2024): 9-10.

[3] Zaman and Lakhani, 鈥淒eterminants of Top-Down Sabotage鈥�, HBS Working Paper 25-007 (August 22, 2024): 10.

[4] Zaman and Lakhani, 鈥淒eterminants of Top-Down Sabotage鈥�, HBS Working Paper 25-007 (August 22, 2024): 26.

Meet the Authors

isa Post-Doctoral Fellow at the Laboratory for Innovation Sciences at 性视界. His research lies at the intersection of information economics, strategy and finance. He uses observational data and field experiments to study the role of economic incentives in mitigating agency issues in organizations. In addition, he uses machine learning methods to study the impact of social media sentiment on firm performance.

is the Dorothy & Michael Hintze Professor of Business Administration at the 性视界 Business School. His innovation-related research is centered around his role as the founder and co-director of the and as the principal investigator of the NASA Tournament Laboratory. He is also the co-founder and chair of the HBS AI Institute and the co-founder of the 性视界 Business Analytics Program, a university-wide online program transforming mid-career executives into data-savvy leaders.

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Key Insight: With Proper Guidance, LLMs Can Effectively Estimate Earnings

The researchers tested two approaches: a “lazy analyst” method, with minimal guidance, and a “sequential prompt” strategy that broke down the task into structured steps. Shaffer and Wang found that when given minimal instructions (the lazy analyst), LLMs often made conceptual errors in estimating core earnings. However, when provided with a structured, step-by-step approach (sequential prompt), the models produced valid core earnings measures that outperformed traditional metrics in predicting future earnings.

Key Insight: AI-Generated Core Earnings Measures Show High Persistence and Predictive Power

Shaffer and Wang found that the AI-generated core earnings measure using the sequential-prompt approach better captured the persistent components of earnings that are reflected in market valuations over longer horizons. When predicting stock prices two years ahead, the Sequential Prompt Core Earnings per Share measure achieved an adjusted R虏¹ of 0.7585, outperforming both Compustat measures and GAAP net income. This alignment between market valuations and stock prices seen at longer horizons, shows that the information available through the sequential prompt approach could be effectively utilized by investors when determining future stock prices.

Key Insight: LLM-Based Core Earnings Estimates Are Highly Cost-Effective

One significant advantage of the LLM-based approach is its efficiency and cost-effectiveness. The authors note that their method produces core earnings estimates at a fraction of the cost and time required for traditional approaches, which could greatly reduce the expenses associated with processing and analyzing financial disclosures. The authors highlight that the advancements in ChatGPT-4 were pivotal in enabling the completion of an analysis like this one, as its predecessor, ChatGPT-3.5, lacked the necessary analytical capacity. They further suggest that, if the capabilities of LLM models continue to progress as anticipated, leveraging LLMs for such analyses could become a standard practice.

Why This Matters

Shaffer and Wang’s findings highlight the transformative potential of AI in financial analysis, particularly for decision-makers navigating the growing complexity of corporate disclosures. The AI-powered approach to core earnings estimation offers a scalable, cost-effective solution that delivers high-quality insights at a fraction of the time and cost of traditional methods. This innovation could be especially valuable for smaller firms and individual investors who lack access to expensive financial services, leveling the playing field in a data-intensive industry. Moreover, the AI-generated measures’ strong predictive power and correlation with market valuations suggest their utility in investment decisions, strategic planning, and performance evaluation.

However, the study emphasizes the importance of careful implementation when adopting AI tools for financial analysis. The stark difference in performance between the “lazy analyst” and “sequential prompt” approaches underscores the need for well-designed, structured prompts to harness the full potential of these technologies. As AI continues to evolve, it presents an opportunity for business leaders to integrate these tools into their processes, in order to enhance efficiency.

Footnotes

(1) R² (r-squared), also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance in a dependent variable that is explained by an independent variable or variables in a regression model. R² normally ranges from 0 to 1: an R² of 0 indicates that the model does not explain any of the variance in the dependent variable, an R² of 1 means that the model perfectly explains all the variance in the dependent variable. That is to say, generally, the closer the R² arrives to 1, the stronger the relationship between earnings predictions and future actual earnings and market valuation.

References

[1] Matthew Shaffer and Charles C.Y. Wang, 鈥淪caling Core Earnings Measurement with Large Language Models鈥�, (October 8, 2024): 1-45, 6.

[2] Shaffer and Wang, 鈥淪caling Core Earnings Measurement with Large Language Models鈥�, 4.

[3] Shaffer and Wang, 鈥淪caling Core Earnings Measurement with Large Language Models鈥�, 5.

Meet the Authors

is an Assistant Professor at USC’s Marshall Business School. He received his doctoral degree from 性视界 Business School and his bachelor鈥檚 degree from Yale. His research focuses on valuation and corporate governance, especially valuation practice in institutional settings such as M&A. His work has been published in the Journal of Financial Economics, and presented at leading conferences in accounting, finance, and law.

is the Tandon Family Professor of Business Administration at 性视界 Business School. He is a research member of the European Corporate Governance Institute (ECGI) and an associate editor of Management Science and Journal of Accounting Research, two leading management journals. His research and teaching focus on corporate governance and valuation.

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Key Insight: The Two Forces

The research team suggests that businesses face two transformative forces today. Global expertise is constantly growing; for example, in the field of biotech, over 45,000 academic biology papers reference AI today and, with the rate of discovery, firms can no longer keep up with advances. At the same time, falling costs of expertise access, for example, as enabled in the media landscape by creator tools like Instagram and TikTok, have lowered barriers for new entrants, intensifying competition.

This isn鈥檛 new; since the 1980s, technological innovations have increasingly allowed for the outsourcings of operations, to the point that today a mid-sized direct-to-consumer retailer can compete with bigger firms because it can rely on external tools for many operational functions while focusing internally on driving competitive differentiation. The researchers argue, however, that with the advent of AI the narrowing of in-house expertise is accelerating.

Key Insight: The Triple Product – AI鈥檚 Potential for Transformation

Utilizing AI, businesses can transform their processes and improve efficiency. For instance, Moderna has transformed its operations through the use of AI by creating over 900 specialized AI assistants that enable the workforce to complete weeks’ worth of tasks in minutes.

AI tools can also be leveraged to improve efficiency, as shown in a randomized controlled trial, run by the consulting firm BCG and the HBS AI Institute, in which AI-augmented consultants completed 12% more tasks on average, and did so 25% faster.

AI, finally, can enable companies to deploy resources differently, focusing in-house processes on their unique capabilities. An example is the company FocusFuel, which set up its entire production鈥攆rom ideation to marketing, manufacturing, and distribution鈥攊n just months by leveraging AI-enabled platforms, while their team has concentrated on building relationships and growing a customer base.

Key Insight: How to Get Started

There are several steps leaders can take to leverage AI in this new landscape. First, the research teams suggests starting small by choosing a few processes to transform. Second, companies must ensure they have clearly stated safety and governance guidelines to safeguard their AI efforts from bias, misinformation, and cyber attacks. Third, leadership must focus on change management to ensure the entire workforce gets onboard and learns to use the AI tools. Finally, companies must be willing to allocate the necessary budgets to their transformation; if implemented correctly, their investment will pay off in returns.

Key Insight: Reevaluating Strategy in the AI Era

A company should ask itself which problems it currently solves that customers will use AI to solve in the future. For example, in the travel industry, as AI enables customers to plan tailored itineraries, travel agents, to stay relevant, will need to organize experiences that require a human touch.

Specialists should consider, in their field, which types of expertise will need to evolve most. In medicine, as AI excels at diagnostic imaging, doctors can differentiate themselves by emphasizing human-centric skills, such as empathy and caregiving.

Leaders should ask what assets their company can use to enhance its ability to stay competitive. The article names brands, customer relations, and network effects as examples of expertise areas that AI is unlikely to disrupt. So, a consumer-product design company may pivot to focus on customer relations and understanding the customer vision as AI takes over design tasks.

Why This Matters

CEOs should act now to leverage AI to streamline functions that are not core to their businesses, enabling their organizations to focus on unique areas of expertise. Executives can begin by reflecting on their company鈥檚 unique strengths and identifying the core competencies that set them apart. By reallocating resources to refine these competencies and leveraging AI to streamline non-core tasks, leaders can gain a competitive edge. The research teams suggests that embracing AI and evolving with it will be necessary to survival in the new business landscape.

References

[1] Bobby Yerramilli-Rao, John Corwin, Yang Li, and Karim R. Lakhani, 鈥淪trategy in an Era of Abundant Expertise鈥�, 性视界 Business Review (November 20, 2024): .

[2] Yerramilli-Rao et al., 鈥淪trategy in an Era of Abundant Expertise鈥�, .

[3] Yerramilli-Rao et al., 鈥淪trategy in an Era of Abundant Expertise鈥�, .

[4] Yerramilli-Rao et al., 鈥淪trategy in an Era of Abundant Expertise鈥�, .

Meet the Authors

is the Chief Strategy Officer at Microsoft, joining the company effective January 2020. He was also elected as a board member of GlobalFoundries in 2022, and serves as the Chair of Strategy and Investment Committee. He holds a Doctorate in Robotics (Electrical Engineering) from the University of Oxford.

is the General Manager for Corporate Strategy and Development at Microsoft. His background is in SaaS growth strategy, monetization strategy, sales strategy, and operations in B2B environments. Prior to his time at Microsoft, Corwin worked as the Head of Principle Strategy at LinkedIn.

is the Director of Corporate Strategy at Microsoft. Prior to her time at Microsoft, Li worked as a consultant at Houlihan Lokey as well as a Business Analyst at McKinsey & Company.

is the Dorothy & Michael Hintze Professor of Business Administration at the 性视界 Business School. His innovation-related research is centered around his role as the founder and co-director of the and as the principal investigator of the NASA Tournament Laboratory. He is also the co-founder and chair of the HBS AI Institute and co-founder of the 性视界 Business Analytics Program, a university-wide online program transforming mid-career executives into data-savvy leaders.

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In this highly competitive sector, efficient production is critical for profitability, yet worker absenteeism presents a major challenge. In the study sample, the average daily absenteeism rate among workers was 11%, and each production line experienced absenteeism rates of 20% or higher at least once every 10 days. Managers in garment factories must make daily decisions to mitigate these disruptions, often relying on informal agreements to borrow workers, which lead to suboptimal outcomes. Adhvaryu and his team consider how managers navigate these challenges in order to suggest improved performance models that would increase competitiveness in this vital industry.

Key Insight: Relational Contracts as a Solution

To combat absenteeism, managers often rely on informal agreements, or relational contracts, where they lend and borrow workers to manage daily fluctuations in attendance. These contracts are not formally mandated but are driven by a mutual need. The trust and relationships built over time among managers allow for the flexible allocation of workers where they are needed most.

Key Insight: Limitations in Worker Borrowing

Despite the potential for improving productivity through worker sharing, the study found that many beneficial trades remain unrealized. The finding suggests that managers tend to rely on a small, tight-knit circle of trusted colleagues, with 72% of all worker trades occurring between managers who maintained active relationships with only two or three other managers out of a possible 20-22. This gap, between the potential and actual borrowing, results in productivity losses during high absenteeism periods, as managers are limited by the small number of relationships they maintain.

Key Insight: Physical and Demographic Barriers to Worker Sharing

The research reveals that managers who are located closer to each other on the factory floor are more likely to engage in worker trades, as are managers who share similar gender, education, and age characteristics. In fact, 72% of trades happen within a 20-foot radius on the factory floor and 80.2% of trades occur between managers of the same gender. These barriers suggest that expanding the pool of managers with whom one can trade requires deliberate efforts to foster more inclusive and diverse relationships.

Key Insight: The Financial Impact of Missed Borrowing Opportunities

Without the limitations of informal relational contracts, worker reallocation could increase productivity by as much as 1.3%, which would translate into $1.45 million in annual profit for the firm. As one potential intervention, the research team suggests promoting a more inclusive environment or using technology, such as a low-cost messaging app, to facilitate communication between managers across physical and demographic lines. They also suggest hiring a 鈥榖uffer stock鈥� of floating workers who would be optimally distributed across production lines each day to respond to absenteeism

Why This Matters

For C-suite executives and business professionals, the insights from this research provide a strategic lens on workforce management and productivity. In labor-intensive industries, absenteeism is inevitable, but its impact can be mitigated through the implementation of relational contracts or even more formalized worker-sharing systems. By fostering stronger and more numerous relationships between managers, firms can increase operational flexibility, enhance productivity, and generate significant financial gains. For decision-makers, investing in systems or technologies that reduce the friction in these interpersonal collaborations can unlock new levels of efficiency and profitability within the firm.

References

[1] Achyuta Adhvaryu, Jean-Fran莽ois Gauthier, Anant Nyshadham, and Jorge Tamayo, “Absenteeism, Productivity, and Relational Contracts Inside the Firm”, Journal of the European Economic Association 22, no. 4 (August, 2024): 1628鈥�1677, 1643.

[2] Adhvaryu, et al., “Absenteeism, Productivity, and Relational Contracts Inside the Firm”, 1631.

[3] Adhvaryu, et al., “Absenteeism, Productivity, and Relational Contracts Inside the Firm”, 1653.

[4] Adhvaryu, et al., “Absenteeism, Productivity, and Relational Contracts Inside the Firm”, 1632.

Meet the Authors

is the Tata Chancellor鈥檚 Professor of Economics at the School of Global Policy and Strategy at UC San Diego and the inaugural director of the 21st Century India Center. He is also the co-founder of Good Business Lab, a global nonprofit dedicated to improving the well-being of low-income workers. Adhvaryu鈥檚 research portfolio spans the fields of development economics, organizational economics, labor economics and health economics.

an assistant professor in the Department of Applied Economics at HEC Montreal. His research areas are Personnel Economics, Labor economics, and Development Economics.

is an Associate Professor for Business Economics and Public Policy at the University of Michigan鈥檚 Ross School of Business and co-founder and Chief Strategy Officer at the Good Business Lab. His recent work focuses on enterprise, firm, and worker characteristics and decision-making, and the resulting performance dynamics, particularly in developing countries.

is an Assistant Professor of Business Administration in the 性视界 Business School Strategy Unit as well as a faculty co-director of the HBS AI Institute Digital Reskilling Lab. Professor Tamayo is an applied microeconomist primarily interested in industrial organization and development economics. Professor Tamayo earned his PhD in economics from the University of Southern California. He has a BA in economics and an MS in applied mathematics from Eafit University in Medellin, Colombia.

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The post From Absenteeism to Efficiency: How Managerial Networks Shape Productivity appeared first on 性视界 Business School AI Institute.

Key Insight: Initial Decline in Communication Post-Transition

The arrival of a new CEO often ushers in a period of reduced communication. This phenomenon stems from the uncertainty and strategic realignment associated with new leadership, which temporarily disrupts established channels and norms of communication.

Key Insight: Subsequent Increase and Stabilization

After the initial drop, there is a notable resurgence in communication flows. This increase is not merely a return to baseline but an enhancement, often reaching higher levels than before the transition. This surge likely reflects the new CEO’s efforts to establish a fresh operational cadence and strategic clarity.

Key Insight: Impact on Different Types of Communication

The research highlights that the rebound in communication is particularly pronounced in cross-departmental interactions, involving employees from different functional departments, and vertical interactions, involving communications between managers and individual contributors. These dynamics suggest a drive from the top to foster greater collaboration and alignment across the organization, particularly as the new CEO seeks to communicate their new strategic vision for the firm.

Key Insight: Correlation with Organizational Performance

For the 51 public firms in their sample, Impink and his team used monthly stock market measures to gauge performance before and after the CEO transition, measuring the Cumulative Abnormal Returns (CARs) between six months before and six months after transition. They found a positive correlation between the increase in communication after a change of CEO and subsequent firm performance measured by CARs. That is, firms that experience a greater increase in communication tend to have better stock market performance in the medium term.

Why This Matters

For business professionals and C-suite executives, understanding the impact of CEO turnovers on internal communication can be useful. It not only helps in anticipating the challenges that might arise during such transitions but also in strategizing ways to harness these dynamics for organizational benefit. Effective communication in the wake of leadership changes is pivotal in maintaining operational continuity, driving employee engagement, and ensuring the successful implementation of new strategic visions. By proactively managing communication flows during these periods, leaders can stabilize their organizations faster and position them for future success.

References

[1] Stephen Michael Impink, Andrea Prat, Raffaella Sadun, 鈥淐ommunication Within Firms: Evidence from CEO Turnovers,鈥� National Bureau of Economic Research Working Paper 29042 (September 2022): 1-65, 3.

[2] Impink, Prat, Sadun, 鈥淐ommunication Within Firms: Evidence from CEO Turnovers,鈥� 3.

[3] Impink, Prat, Sadun, 鈥淐ommunication Within Firms: Evidence from CEO Turnovers,鈥� 4.

[4] Impink, Prat, Sadun, 鈥淐ommunication Within Firms: Evidence from CEO Turnovers,鈥� 5.

Meet the Authors

is an Assistant Professor of Strategy at HEC Paris and a research affiliate at Hi! Paris (AI for Society and Business) and Boston University TPRI. He holds a PhD in Management from NYU Stern, and his research focuses on how digitization impacts firm structure and performance.

is the Richard Paul Richman Professor of Business at Columbia Business School and Professor of Economics at the Department of Economics, Columbia University. His work focuses on organizational economics and political economy. His current research in organizational economics explores – through theoretical modeling, field experiments, and data analysis – issues such as incentive provision, corporate leadership, employee motivation, and organizational language.

is the Charles E. Wilson Professor of Business Administration at 性视界 Business School, and is a Co-Chair of 性视界 Business School鈥檚 Project on Managing the Future of Work and co-director of the Digital Reskilling Lab. Her research focuses on managerial and organizational drivers of productivity and growth in corporations and the public sector.

The post Navigating the Waves of Change: The Impact of CEO Turnovers on Organizational Communication appeared first on 性视界 Business School AI Institute.