ep. 98. Putting the “socio” back in “sociotechnical”
5 min read
In 1947, the British coal industry was nationalized, giving the state control over a critical but failing post-war industry. The newly formed National Coal Board led a massive modernization effort, introducing the longwall method of coal mining.
Longwalling replaced the traditional “hand-got” method, where small teams of miners worked in individual stalls to manage the entire coal-extraction cycle using handheld tools like pickaxes, wedges, and shovels.
This advanced machinery converted the coal face into a mechanized production line where cutters and drills worked in tandem to slice and blast the coal wall loose, while power-driven conveyors continuously carried the material away to the main transport tunnels.
Management expected that this new high-tech approach would yield increased coal output. Except, the opposite happened: productivity decreased.
In addition, absenteeism increased to nearly 20%. Labor disputes broke out. What was going on?
Enter the Researchers
That question was also something asked by Ken Bamforth, a newly-minted research fellow at the Tavistock Institute, a British non-profit research and consulting organization.
He had learned of this productivity paradox from catching up with his old colleagues at a South Yorkshire coalfield, where he had previously worked for 18 years.
As part of his training in industrial fieldwork, Bamforth returned to visit his old mine. The goal of the assignment was to apply his newfound research skills and report back any new perceptions or practices he observed.
Bamforth noticed that, despite the decreased output reported elsewhere, one part of the mine, the Haigh Moor seam, was thriving. The workers were highly productive and seemed satisfied with their jobs.
Back at Tavistock, Bamforth reported his findings, where they caught the attention of Eric Trist, a psychologist and one of the institute’s founders. One thing led to another, and the two men were off to study work at the Heigh Moor seam to see if the success of this one group could be replicated.
Unearthing a Theory
When Trist and Bamforth descended into the mine to observe what was happening, they discovered that miners had rejected the rigid, assembly-line layout required by the new machinery. Instead, they restructured their workflow into self-managing, multi-skilled teams.
This social structure was the missing link needed to understand why the introduction of the allegedly-superior longwall method was failing elsewhere.
Under the old hand-got method, small groups of miners operated with independence, managing the entire extraction cycle themselves and relying on close kinship ties for safety and emotional support. Trist and Bamforth called this responsible autonomy.
The longwall method fragmented these groups, isolating workers into three distinct shifts (cutting, ripping, and filling) that rarely, if ever, communicated. A culture of mutual scapegoating emerged.
For example, workers on the filling shift frequently inherited bad work from the previous shift, such as “gummings” (debris) left in the undercut or “sticky tops” (coal clinging to the roof). This bred resentment between miners and often led to skipping work altogether.
In contrast, the miners in the Haigh Moor seam spontaneously adapted the old ways of working in a way that complemented the new technology. This joint optimization resulted in the originally-expected productivity increases.
Trist and Bamforth wrote up their findings in a 1951 paper, which would form the foundation of Sociotechnical Systems Theory, which states an organization succeeds only when its people (the “socio”) and its technology (the “technical”) are designed to work in harmony.
If you optimize the “technical” without the “socio,” the technology works on paper but underdelivers in adoption, workflows, and outcomes.
The AI Longwall
Modern AI adoption looks a lot like the longwall approach: a new, powerful technology is introduced into organizations, but expected returns don’t follow.
Forbes reported on exactly this issue last week, spotlighting the manufacturing sector; zero of the manufacturing business leaders surveyed in a recent Grant Thorton study reported significant revenue uplift from AI initiatives. While they were seeing some efficiency gains, 48% were stuck in pilot mode.
Forbes concludes that the fix is better procurement discipline, not a better AI model.
What is “procurement discipline”? It’s the intentional design of an organization governance system that ensures technical tools are deployed alongside a clear social structure of accountability. In short, it’s part of the “socio” in sociotechnical systems.
Similarly, in June, Bain & Company reported that nearly 40% of companies measuring their AI outcomes landed in the 0-10% cost-savings bucket, versus the 11-20% they had targeted. (Fun fact: 90% of the companies that missed their AI cost-savings targets are increasing their budgets again for the next wave).
AI keeps underdelivering on its promise. The fix is organizational, not technological.” -Bain & Co, June 2026
The companies that were delivering on their AI targets did so by “treating data access, governance, and process redesign as CEO-level problems rather than IT problems”.
Sidenote: If my book manuscript wasn’t already locked, I would have loved to include this quote from the Bain article: “Ask most executives about AI agents, and they’ll describe a near future of autonomous systems handling complex decisions end to end”.
This overpivot on the “technical” at the expense of the “socio” is also on full display with OpenAI spending $4BB, and Anthropic, $1.5BB, on ventures that send in forward deployed engineers to diagnose and attempt to fix why powerful AI systems aren’t reaping promised benefits. (Why we’re not sending in experts whose deep well of knowledge is focused on the “socio” is the topic of another post).
Why This Keeps Happening
Seventy-five years later, the challenges of AI adoption echo those seen with the introduction of longwalling. Why do organizations keep favoring technical subsystems over social subsystems?
A Formal Rationality Mindset
Organizations continue to view the workplace through an industrial age lens, treating it as an organizational machine where both processes and humans are expected to behave deterministically.
This mindset is driven by formal rationality, a concept rooted in sociologist Max Weber’s work, which designs bureaucracies to replace human judgment with the dictates of rules, regulations, and structures. The mindset prioritizes quantification and control, mistakenly assuming that a technical system’s success is a repeatable chain of cause and effect where the whole will always equal the sum of its parts
The Governance Gap: Procurement and Ownership
Modern technology adoption is frequently driven by competitive pressure and the fear of falling behind rather than a disciplined analysis of specific operational needs. Because the motivation is reactive, projects often lack a named executive owner or clear financial metrics, leading to “pilot purgatory” where technical activity is mistaken for measurable P&L results.
Habituation to “Bad Systems”
When a technical system is socially dysfunctional, workers develop psychological defenses, such as the mutual scapegoating that emerged after the introduction of longwalling, to survive the environment. Over time, groups become habituated to these “bad systems” because the dysfunction offers a stable, albeit broken, status quo where individuals can remain anonymous and avoid personal accountability for system failures.
A Path Forward
Ultimately, moving towards joint optimization requires us to stop treating technology as a standalone fix and start designing the social systems to support it. A few ways to do this include:
Implement Procurement Discipline: Treat technical investments as capital requests that target a pre-costed business problem and a specific financial metric. The probability of success increases by assigning a named executive owner to the outcome and enforcing a firm “kill date” if measurable value is not achieved.
Empower Experts in Human Behavior and Organizational Systems: Elevate specialists with deep wells of knowledge in the “socio”, such as design researchers, to bridge the gap between technical tools and organizational needs. For design researchers reading this, your role needs to adapt too, such as taking a strong POV about what to build and taking an active role in implementation.
Encourage Responsible Autonomy: Grant teams the authority to manage their own internal regulations and task interchangeability, allowing them to experience a complete cycle of operations. This approach helps ensure that human actors are meaningfully integrated with the technical process rather than fragmented by it.
📖 If you liked this episode, you’ll love my upcoming book, What Your Machines Should Do: The Science and Strategy of Human-Centered Automation (Rosenfeld Media, Fall 2026). Sign up to be the first to know about book release details.
⏪ Recent Episodes
ep. 97. How an Embodiment Architect Designs for Physical AI
ep. 96. How a Creative-Technologist-Turned-Product-Leader Meets People Where They Are with AI
ep. 95. How an Interdisciplinary PM Owns the Output of AI
🚀 Sendfull in the Wild
QRCA Keynote: July 21
I’m excited to be giving the keynote for the Qualitative Research Consultants Association’s Qual Tech Days 2026 conference on July 21.
As AI automates more tasks, expert judgment is becoming an increasingly scarce and valuable resource. In my talk, 𝗚𝗼 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂’𝗿𝗲 𝘀𝗰𝗮𝗿𝗰𝗲: 𝘄𝗵𝘆 𝗾𝘂𝗮𝗹’𝘀 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀 𝗮𝗿𝗲 𝘄𝗵𝗮𝘁 𝗔𝗜 𝗺𝗶𝘀𝘀𝗲𝘀, I’ll cover why qualitative researchers are uniquely positioned to thrive in an automated future, and how the work we do best (reframing problems, making non-obvious connections, and uncovering tacit human knowledge) may become even more important in the age of AI.
✍️ Register for the conference here.
ShiftUX: Sept 23-25
What’s happening with UX? Come join us at ShiftUX to find out. I’m delighted to be a curator of the human-centered tooling track, alongside these amazingly talented humans.
✍️ Register for the conference here (virtual and IRL options available). Use HUTKA-shift26 for $75 off.
📖 Good Reads
Ford Rehires Human Engineers After AI Fails to Match Quality Checks (BBC): History repeating itself (Klarna, Boeing, Tesla…).
Context Architecture (Nielsen-Norman Group): A framework that applies information architecture principles to the “ecosystem” of AI context (instructions, memory, tools) to help systems generate outputs that are accurate and aligned with human mental models.
HCII AI Starter Pack (Dan Saffer): Access to a collection of Carnegie Mellon University’s Human-Computer Interaction Institute’s prominent AI papers of the last several years.
That’s a wrap 🌯 . More on UX, automation, and strategy from Sendfull in two weeks!
An essay after my own heart. An engineering and management design that seemed rational to some people. A collision with reality. A description of the strange gap. Catnip!
This is really helpful framing, Stef. Shared it with my team!