Transformative Power of AI and its Challenges

Introduction: The Age of Human-Analogue Machines

Henry Ford’s Faster Horse

What is Machine Learning?

What is Machine Learning?

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

The Atomic Human Concept

Homo Atomicus

• Where we have homo economicus the machine can step in.
• Quantitative vs Qualitative gap
• Homo atomicus is …
  • 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

Attention Reinvestment Cycle

Conclusion

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

The Evolution of Decision Making

Networked Interactions

Bezos memo to Amazon in 2002

The API Mandate

• All teams will henceforth expose their data and functionality through service interfaces.
• Teams must communicate with each other through these interfaces.

• There will be no other form of inter-process communication allowed: no direct linking, no direct reads of another team’s data store, no shared-memory model, no back-doors whatsoever. The only communication allowed is via service interface calls over the network.

• It doesn’t matter what technology they use.
• All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions.

Duality of Corporation and Information

• What is less written about is corporate structure.
• This information infrastructure is reflected in the corporation.
• Two pizza teams with devolved autonomy.
• Bound together through corporate culture.

Conway’s Law

Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization’s communication structure.

Conway (n.d.)

Information Topography: How AI Reshapes Organizational Decision Making

Balancing Centralized Control with Devolved Authority

Question Mark Emails

Executive Sponsorship

• Direct sponsorship from the most senior executive.
  • This has a cultural effect as well as a direct effect.
• Bring about through involvement
  • develops understanding of capabilities of data science in exec team.

Pathfinder Projects

• In executive context: an important project that is interdepartmental.
• Should involve the CEO, CFO, CIO and data science team (or equivalents).

Generative AI as HAM

• Generative AI provides us with an “analogue human”
• An information amplifier with a multiplier of 300,000,000.
• Radically changes information infrastructure
• From Conway’s Law: All existing models redundant.

Human-Analogue Machines (HAMs) as Business Tools

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The MONIAC

Human Analogue Machine

HAM

The Business Challenge

What to do?

• We know weverything we’re doing now is wrong.
• We don’t know how it’s wrong.
• “Marconi approach” unlikely to work

Conclusion: The Business Imperative

AI cannot replace atomic human

Atomic Human Approach

• Human attention the differentiator.
• Focus on how your human capital needs to adapt.
• People first approach, not AI first.

Conclusion

• AI reshapes information flows - understand your information topography
• Balance centralised control and devolved decision-making
• Recognize LLMs as interfaces, not substitutes for human judgment

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

Table Discussion

• How does personal automation vs systems automation vary with Generative AI?

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

• Complex ≠ Complicated
• Speed isn’t always progress
• Augment humans, don’t replace them
  • See e.g. ai@cam public dialogues: https://ai.cam.ac.uk/projects/public-dialogues.html

Good Process Drives Purpose

Developing Digital Literacy at Board Level

Conclusion: Architecting the Future Organization

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