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Chapter 0.3 — AI as a sociotechnical system

🎯 Chapter objective

Establish that AI systems are not merely technical: they involve people, processes, politics and culture.

🧠 Core concept

A sociotechnical system operates under the interaction between:

  • Technical: data, model, code, infrastructure.
  • Social: users, operators, stakeholders, regulators.
  • Organizational: processes, roles, internal politics, culture.

Operational failures, lack of documentation, absence of governance, or conflict between teams are often as damaging as wrong hyperparameters.

🏗️ How this shows up in production

  • A good model is pulled because the compliance team was not consulted.
  • A useful agent is shut down because nobody takes ownership of operating it.
  • A correct pipeline produces wrong data because the upstream team changed the schema without notice.

⚖️ Trade-offs

Option Advantage Risk
Centralize governance Consistency Slowness
Decentralize to squads Speed Inconsistency
Hybrid (federated) model Balance Coordination cost

🚨 Failure modes

  • Technical team without organizational mandate to change data, rules or process.
  • No clear RACI: nobody owns it.
  • Technical success without user acceptance (overreliance or rejection).

🛡️ Controls and mitigations

  • Explicit RACI for each agent/model: technical owner, business owner, operations owner.
  • Governance committees with multidisciplinary participation.
  • User training on the system's capabilities and limits.
  • EX-FUND-03 — a RACI template for an enterprise AI system.

📌 Checklist

  • [ ] Are a technical owner and a business owner named?
  • [ ] Is there a communication plan with users about what the system is and is not?
  • [ ] Is there a multidisciplinary review process before go-live?

📚 References