Cognitive Moats

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