Humans in the AI World

Reimagining the Organisation in the Age of Human-Analogue Machines

Neil D. Lawrence

The Transformational CHRO Programme

Introduction: The Age of Human-Analogue Machines

Henry Ford’s Faster Horse

What is Machine Learning?

What is Machine Learning?

\[ \text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

  • data : observations, could be actively or passively acquired (meta-data).
  • model : assumptions, based on previous experience (other data! transfer learning etc), or beliefs about the regularities of the universe. Inductive bias.
  • prediction : an action to be taken or a categorization or a quality score.

What is Machine Learning?

\[\text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

  • To combine data with a model need:
  • a prediction function \(f(\cdot)\) includes our beliefs about the regularities of the universe
  • an objective function \(E(\cdot)\) defines the cost of misprediction.

Beyond Automation to Augmentation

Human Communication

Artificial General Vehicle

Embodiment Factors: Fundamental Differences Between Humans and Machines

Information and Embodiment

Claude Shannon

Embodiment Factors

bits/min billions 2,000
billion
calculations/s 		~100 a billion
embodiment 20 minutes 5 billion years

For sale: baby shoes, never worn

The Atomic Human Concept

Homo Atomicus

  • Not in A-level results
  • Not in hospital waiting lists
  • In the quality of human interaction

Table Discussion

  • What is the indivisible essence of human contribution to organizations?

The Trust Imperative

Complexity in Action

Techno-Inattention Bias

  • Organizations develop “techno-inattention bias” - focusing on AI details while missing human dynamics
  • The “gorilla” of human relationships, culture, and ethics goes unnoticed
  • Institutional inattentional blindness develops when leadership fixates on technical aspects

The Danger

  • AI fascination distracts from nurturing irreplaceable human elements
  • CHRO role critical as “gorilla spotters” - keeping human essentials in focus

Human Attention as the Scarce Resource

The Attention Economy

Herbert Simon on Information

What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention …

Simon (1971)

New Flow of Information

Evolved Relationship

Evolved Relationship

Emulsion: Combining Human and Machine Intelligence

New Attention Flywheel

Conclusion

See the Gorilla don’t be the Gorilla.

Maintaining Human Judgment in Critical Decisions

The Horizon Scandal

Table Discussion

  • How do we develop feedback systems that capture both algorithmic outputs and essential human judgment?

Cultural Architecture

The Atomic Human

The Uncertainty Principle of Human Capital Quantification

Inflation of Human Capital

  • Strength in Human Capital double edged sword.
  • Automation creates efficiency.
  • But skills risk becoming redundant.

Organizational Culture as Competitive Differentiator

Balancing Centralization and Distribution of Authority

An Attention Economy

  1. Human attention will always be a “scarce resource” (See Simon, 1971)
  2. Humans will never stop being interested in other humans.
  3. Organisations will keep trying to “capture” the attention economy.

Creating Environments for Reflexive and Reflective Decision-Making

Example: Amazon’s “Thoughtsday”

Future-Ready Talent Strategy

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Beyond Traditional Competencies: The Uncertainty Problem

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The MONIAC

-1

HAM

Building Integrated Learning Systems

Organizations need to develop learning systems that:

  1. Capture insights from both human and algorithmic sources
  2. Distribute knowledge efficiently across the organization
  3. Adapt rapidly to changing conditions
  4. Preserve essential human judgment

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Practical Implications for CHROs

Ethical Frameworks for Personal Data

Example: Business Development at Amazon
When acquiring companies, we often encountered “difficult to place individuals who were irreplaceable in the acquired company” - these individuals defied algorithmic categorization but were essential to value creation. This required new frameworks for evaluation.

New Metrics for Human-Machine Collaboration

Traditional metrics focused on efficiency must be complemented by measures of:

  • Innovation adaptation rate
  • Decision quality (not just speed)
  • Human-machine collaboration effectiveness
  • Knowledge creation and distribution: Attention Reinvestment

Maintaining Human Agency While Leveraging Automation

Superficial Automation

  • AI enables automation of surface-level tasks
  • Examples: Email writing, document summarization
  • Risk of losing deeper value in the process

Hidden Value

  • Email writing builds relationships
  • Documentation creates institutional memory
  • Human pauses enable reflection

The Automation Challenge

Good Process Drives Purpose

Developing Digital Literacy at Board Level

Conclusion: Architecting the Future Organization

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Thanks!

References

Lawrence, N.D., 2024. The atomic human: Understanding ourselves in the age of AI. Allen Lane.
Simon, H.A., 1971. Designing organizations for an information-rich world. Johns Hopkins University Press, Baltimore, MD.