On 4 December 2025, Columbia University’s School of Professional Studies hosted the latest installment of its IKNS Conversations That Matter series online, presenting “Business Anthropology at Work: Making AI a Human Multiplier.” Moderated by Jessica Malloy, alum of the MS in Information & Knowledge Strategy program, the session featured anthropologist Dr. Drew Jones of Culture Consultancy and organizational change expert Dr. Matthew Hill, who explored how human-centered perspectives can guide AI-enabled organizations toward clearer understanding, faster learning, and better decisions grounded in human judgment.

Opening Remarks and Introduction

Jessica Malloy opened by situating the series within the School of Professional Studies and explaining its mission: to ignite discussion on timely ideas aligned with the IKNS curriculum. She described the new business anthropology elective and framed the session’s focus on examining the human side of AI, starting with the foundations of business anthropology and then applying its principles to today’s generative AI moment.

Defining Business Anthropology

Dr. Drew Jones defined business anthropology as the application of ethnographic research methods—centered on deep empathy and contextual inquiry—to understand experiences from the point of view of employees, customers, and communities. He stressed that anthropology strives to keep humans at the center of business, counterbalancing the drive toward mechanistic solutions that promise predictability but often marginalize the human voice.

Historical Overview of the Field

Tracing nearly a century of practice, Dr. Jones highlighted early workplace studies in the 1930s and ’40s, where anthropologists participated in the Hawthorne experiments; Edward Hall’s cultural management work for government in the 1950s; and the pioneering ethnographic research on human–computer interaction at Xerox PARC in the 1970s and ’80s, which laid the groundwork for UX design. He noted that today’s AI era brings us full circle: as in those early days of HCI, we again need ethnographic insight to guide technological innovation.

The Anthropologist’s Lens on Organizational Psychology

When asked about the relationship between business anthropology and industrial-organizational psychology, Dr. Matthew Hill observed that IO psychology often focuses on individual cognition and mental models, whereas anthropology takes a broader view of culture as everything socially learned and transmitted—from formal vision statements to informal social norms. Dr. Jones added that anthropology’s emphasis on context, power dynamics, and collective practices can enrich organizational effectiveness in ways that purely psychological approaches may overlook.

Anthropological Insight in Action

Illustrating ethnography in practice, Dr. Hill described a post-COVID process-reengineering project at a large metropolitan credit union. By mapping every step of a customer-service request system—from the frontline teller through multiple back-office departments—researchers uncovered silos, misrouted tickets, and unnecessary handoffs. They then convened cross-departmental workshops to co-create a shared process map, aligning teams around a unified understanding and ultimately streamlining the entire workflow.

Ethical Considerations and Human-Centered AI

Audience questions turned to the ethics of AI deployment. Dr. Hill warned that unchecked automation can cost human safety engineers their roles—a trend seen in recent layoffs at X’s AI division—and can exacerbate environmental impacts from data centers. Dr. Jones observed that investors seeking higher earnings per share are often the primary beneficiaries of today’s AI investments, not front-line workers. Both urged the adoption of clear regulations—such as the EU’s opt-in requirements for workplace AI—to safeguard employee data, agency, and trust.

Keeping Humans in the Loop

To prevent AI from flattening human judgment, the panel recommended “ensembling” approaches in which algorithms perform large-scale pattern recognition while humans frame problems, interpret meaning, and make the final decisions. Dr. Jones pointed to McKinsey’s recent retraining of consultants in interpretation, discernment, and judgment—skills that AI cannot replicate—as a positive example of hybrid workflows that balance machine efficiency with human insight.

Looking Ahead: A Practical Checklist in Prose

As a final takeaway, the speakers urged leaders to frame AI investments around empathy and contextual research, ensuring that initiatives begin with a deep understanding of how people actually work and interact. They encouraged organizations to design workflows that pair machine-scale data analysis with human judgment at critical junctures, rather than allowing algorithms to make unilateral decisions. Strengthening cross-functional trust was highlighted as essential; creating shared artifacts such as process maps can align stakeholders and foster collaboration. The panel also emphasized the importance of empowering front-line teams to apply AI insights in real time, pushing decision-making authority closer to those doing the work. Finally, they called on leaders to advocate for ethical guardrails and data-privacy standards that respect individual agency and environmental sustainability. By grounding AI adoption in anthropological methods—empathy, contextual inquiry, and collaborative sense-making—organizations can transform technology into a true human multiplier and build workplaces where collective intelligence thrives.

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