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.