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Published by Himanshu
A weekly podcast hosted by Himanshu Warudkar — unpacking academic research on AI, organisational strategy, and business models for technology leaders and practitioners. Every week, Himanshu picks one research paper and turns it into an engaging conversation — making dense academic scholarship accessible and actionable for practitioners. Available on Spotify.
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As organizations are adopting AI @ Scale, measuring productivity gains attributable to AI is also a problem statement that requires deeper research. A recent NBER paper by Diane Coyle and John Lourenze Poquiz reveals why business leaders struggle to quantify AI’s real value. Link to the paper - https://lnkd.in/dhMm4_PV Here is what practitioners need to know: 📉 The Efficiency Paradox: By automating tasks like customer service or scheduling, AI reduces recorded transactions. This means internal efficiency gains can look like a decline in official output, even as organisational effectiveness and customer value soar. ⏳ Time Reallocation is the Key: AI’s true benefit lies in saving time on routine cognitive tasks (like data cleaning or drafting), freeing up your team for higher-value, creative work. 💡 Quality over Quantity: Standard metrics track volume, but AI drives dynamic, unpriced quality improvements and process re-engineering. By automating routine cognitive and logistical tasks, these organizations are capturing massive time savings and quality improvements that legacy reporting structures simply cannot see. For business leaders, the takeaway is clear: if you are only measuring success by traditional headcount or volume metrics, you are blind to the quiet productivity boom happening inside your workflows. To measure your AI ROI, design internal reporting around task-based time savings and outcome-focused quality gains .
As organizations rush to adopt Generative AI to boost productivity, a silent threat is emerging in the workplace.A groundbreaking study published in the Journal of Service Management reveals that when employees collaborate with AI, they frequently fall into the trap of AI complacency —the tendency to intentionally neglect verifying AI-generated output, even when it contains systematic errors.And here is the kicker: It has nothing to do with how tech-savvy or experienced your employees are.
This paper explores the potential emergence of an economy driven by autonomous AI agents capable of executing complex tasks with minimal human supervision. The authors examine how these digital entities might function as consumers and producers , potentially leading to market collusion or a decoupling of prices from true human preferences. They suggest that AI could drastically alter firm structures by reducing coordination costs, though this may also introduce systemic fragility through correlated errors. To manage these risks, the text argues for the creation of new digital institutions and legal frameworks to handle agent identity and accountability. Ultimately, the researchers call for a distinct economic theory to address the "alignment problem," ensuring that autonomous systems remain beneficial to human society.
This article by Seidl, Ma, and Splitter examines the concept of strategy-as-practice by addressing the fundamental ambiguity regarding what makes an activity "strategic." The authors propose a new framework that categorises strategic activities into four distinct perspectives: those with significant consequences , those formally labelled as strategy, those performed by recognised strategists , and those constituting a recurrent pattern of action. Each viewpoint introduces unique research questions, ranging from how specific outcomes are produced to how professional identities are constructed. By differentiating these views, the researchers aim to integrate strategy-as-practice with broader management theories such as managerial cognition and dynamic capabilities . Ultimately, this framework provides a structured approach for scholars to expand the boundaries of strategic management and develop more cumulative knowledge. This multidimensional perspective ensures that the field captures the full complexity of how strategy is actually performed within organisations.
This academic article proposes a significant ontological shift in how we understand artificial intelligence , moving away from viewing it as a standalone entity or autonomous agent. The authors argue that AI is more accurately defined as an organizing capability that is fundamentally connective , codependent , and emergent . Rather than residing solely within software, this capability arises through the complex system of relations between human participants and learning algorithms. By focusing on these human-algorithm relations , the research highlights how organizational tasks like analysing, learning, and acting are collectively produced rather than pre-programmed. Ultimately, this perspective suggests that AI is a doing and becoming process that reconfigures organizational structures, power dynamics, and intelligence.
This National Bureau of Economic Research paper investigates the productivity impact of generative AI on software development by tracking three generations of coding tools. The authors analyse data from over 100,000 GitHub developers to compare simple autocomplete features, interactive sync agents, and autonomous async agents. While these tools dramatically increase task-level activity , such as code volume and commit frequency, the study reveals that these gains diminish significantly as they move toward final output. This attenuation is attributed to a "weak-link" effect , where human bottlenecks in code review and project management limit the overall speed of software releases. Finally, the research finds that while AI has increased the supply of new applications in digital marketplaces, there has been no corresponding increase in total user engagement.
The future of customer experience is no longer just conversational—it’s deeply vocal. 🎙️Many organizations still rely on robotic, inflexible Interactive Voice Response (IVR) systems that frustrate customers. While digital assistants like Siri or Alexa set the stage for basic voice interaction, modern service organizations need to aim much higher
This research investigate the psychological and social effects of integrating artificial intelligence into collaborative work teams. Through three empirical studies, the authors explore how an AI teammate's social presence influences a person's motivation to contribute to collective tasks. The results indicate that while people initially perceive AI as having less presence than humans, this gap can be narrowed by increasing the transparency and familiarity of the system. Crucially, the studies reveal that social presence does not always drive productivity directly; instead, it works by fostering a willingness to depend on others and a sense of team-oriented commitment . Ultimately, the findings suggest that successful human–AI collaboration relies less on making robots look human and more on building interpersonal trust and clear operational understanding.
This academic editorial from the Journal of Management Studies examines the profound ways artificial intelligence is currently reshaping work, leadership, and institutional structures. The authors argue that scholarly research must move beyond treating AI as a single tool, proposing instead a heuristic framework that distinguishes between predictive, generative, agentic, and embodied systems. By categorising these different modes, the text clarifies how specific technologies alter core organisational concepts such as expert authority , decision-making, and professional identity. The collection explores the human-AI relationship , highlighting tensions between algorithmic efficiency and human judgment alongside the risks of occupational displacement. Ultimately, the sources set a comprehensive research agenda to address the governance, ethics, and shifting power dynamics defined by this rapid technological acceleration. Throughout the analysis, the writers emphasise that AI acts as an active participant in organisational life rather than a passive instrument.
We've all seen AI fail in customer service. But how should your AI system apologize to actually repair trust?Recent research on "AI Apology" highlights that as we deploy more customer-facing AI—from support chatbots to hospitality robots—we must intentionally design how they recover from mistakes. A poorly designed apology can severely damage the user's perception of your system, while a good one makes the AI seem more trustworthy and easy to work with.If you are building or adopting AI for customer interactions, here is a practical, research-backed framework for designing effective AI apologies.
This research examines how generative artificial intelligence (GenAI) acts as an agentic force that transforms service systems through its interaction with human actors. By moving beyond the view of technology as a passive tool, the authors introduce the concept of human–GenAI hybrids that co-create hybrid intelligence and alter traditional task delegation. The paper identifies two unique GenAI capabilities, autoreflexivity and autoreformation , which allow these hybrids to recognise and intentionally reshape the institutional arrangements governing service exchanges. These dynamics influence the mechanisms of transformation , leading to more frequent and rapid phase transitions within service ecosystems. Ultimately, the study challenges human-centric assumptions in service design by positioning GenAI as an active contributor to systemic change and market evolution.
In our fast-paced, unpredictable business environments, leaders constantly face chaotic challenges. Weick and his co-authors remind us that we don't just decide our way out of chaos; we "make sense" of it. Sensemaking involves turning an overwhelming flow of circumstances into a situation that is comprehended in words and serves as a springboard into action. It’s the critical moment when a team collectively asks, “what’s the story here?” and “what do we do next?”.
This academic paper explores the evolution of absorptive capacity , redefining it as a dynamic capability essential for a firm’s long-term success. The authors distinguish between potential capacity , which involves finding and processing information, and realised capacity , which focuses on applying that knowledge to create value. By clarifying these definitions, the research highlights how companies can better adapt and innovate within rapidly changing markets. The study provides a comprehensive framework to help organisations understand how internal processes turn external data into a sustainable competitive advantage . Ultimately, the text offers a more precise way to measure and implement strategies that enhance an enterprise’s intellectual agility .
Scaling AI isn't just about deploying the latest technology; it is about fundamentally redefining how you co-create value.While "co-creation" is a popular buzzword, it is rigorously defined as the enactment of creation through interactions across platforms that connect people, processes, interfaces, and digital artifacts. When adopting AI at scale, practitioners aren't just installing software; they are building complex interactive system-environments. Research highlights that the digital transformation driven by AI is dramatically reshaping how firms co-create value in industrial markets. Successful AI integration relies on continuous, iterative loops: first, by co-creating customer-centric solutions through perceptive and responsive mechanisms, and second, by leveraging end-user knowledge to enhance daily operational practices. This means the true power of AI goes beyond traditional production metrics to what researchers call "value-in-interactional creation". Ultimately, the co-creation experiences themselves serve as the fundamental basis of value. To successfully implement AI @ Scale, leaders must stop viewing algorithms as isolated tools and start designing platforms that maximize engaging human-AI interactions.
Are you trying to "acquire knowledge" about AI, or are you "knowing in practice"? As Artificial Intelligence reshapes our workflows, many of us are scrambling to learn new tools. But there is a massive difference between reading about AI and actually having the competence to use it.Traditional views often treat knowledge as a static "thing" that can simply be captured, stored, or transferred. However, true competence is not a stable disposition or a discrete object you can just acquire. Instead, knowing is an ongoing social accomplishment that is continuously constituted and reconstituted as we engage with the world in practice.
Often described as the "grammar of action," organizational routines are the repeated, recognizable patterns of behavior that drive your daily operations. While adopting AI represents a massive macro-level strategic shift, the true transformation actually happens at the micro-level of these daily routines. Integrating tools like Large Language Models isn't merely about automating tasks; it fundamentally alters the division of labor and creates new dynamics for human-machine collaboration. For example, AI-assisted coding tools are currently transforming traditional software development routines into a collaborative "teammate" pairing between human engineers and AI. However, forcing new technology into old routines often triggers "cognitive inertia" —deeply ingrained assumptions and a resistance to letting go of legacy thinking. To successfully drive AI adoption, leaders must look beyond the technology. By aligning AI with existing work practices and actively empowering employees to co-create new digital routines, organizations can overcome resistance and build a culture of continuous adaptation. Don't just upgrade your technology—upgrade your organizational routines.
This scholarly article explores a strategic framework for integrating artificial intelligence into the service sector to improve customer engagement . The authors categorise technology into three progressive levels: mechanical , thinking , and feeling AI , each offering distinct advantages like standardisation , personalisation , and relationalisation . As machines become more sophisticated, they begin to replace or augment human labor depending on whether the task is routine, analytical, or emotional. The text suggests that while AI currently excels at efficiency and data analysis , human intelligence remains vital for empathetic interactions . Ultimately, service providers are encouraged to align these different forms of intelligence with their specific business strategies and process stages
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Observed September 13, 2026. Cached outside the daily freshness window; the positions keep the date they were taken on.
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