Leadership and AI: Strategic Decision Making in the Age of Human-Analogue Machines

An MBA Masterclass on Human-Machine Collaboration

Neil D. Lawrence

LUISS Business School, Full-Time MBA Programme, Rome

Today’s Schedule

Morning Session: 10:00-13:00

  • 10:00-10:45: Part 1 Lecture
  • 10:45-11:15: Exercise 1 + Plenary
  • 11:15-11:45: Break
  • 11:45-12:30: Part 2 Lecture
  • 12:30-13:00: Exercise 2 + Plenary

Today’s Schedule

Lunch: 13:00-14:30

  • Homework: Read Horizon scandal materials

Today’s Schedule

Afternoon Session: 14:30-17:30

  • 14:30-15:15: Part 3 Lecture
  • 15:15-15:45: Exercise 3 + Plenary
  • 15:45-16:15: Break
  • 16:15-17:00: Part 4 Lecture
  • 17:00-17:30: Exercise 4 + Plenary

Part 1: Understanding Human vs Machine Intelligence

The Age of Human-Analogue Machines

  • What makes humans unique?
  • This is the foundation of the day.

The Embodied Nature of Human Intelligence

E S A R I N T U L
O M D P C F B V
H G J Q Z Y X K W

Bauby and Shannon
bits/min
billions
2000
6
billion
calculations/s
~100
a billion
a billion
embodiment
20 minutes
5 billion years
15 trillion years

Communication Bandwidth

  • Human communication: walking pace (2000 bits/minute)
  • Machine communication: light speed (billions of bits/second)
  • Our sharing walks, machine sharing …

Cultura animi

For sale: baby shoes, never worn

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Key Takeaways: Human vs Machine Intelligence

  • Atomic Human defined by limitations rather than capabilities
  • Human intelligence is fundamentally embodied
  • Human context is associated with limited lifespan
  • Conversation and social understanding are situated in our culture and context

Exercise 1: Mapping An Organisation’s Information Flows

Plenary Discussion

  • What surprised you about your organisation’s information flows?
  • Where are the critical human judgment points?
  • How might AI change these flows?

Part 2: Information Topography and Decision Making

The Information Revolution in Organisations

New Flow of Information

New Flow of Information

Evolved Relationship

Evolved Relationship

The Evolution of Organisational 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.)

Understanding Information Topography

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.

Trust, Autonomy and Embodiment

  • Trust is not a slogan
  • Trust is an infrastructure
  • Autonomy depends on Trust
  • Trust depends on shared culture

Balancing Centralised 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).

The Attention Economy Framework

  • Human attention is the scarcest organisational resource
  • AI changes who pays attention to what
  • Strategic allocation of attention determines competitive advantage
  • Organisations must design for attention deployment, not just task automation

Generative AI as Human-Analogue Machines

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: existing organisational models are redundant

The MONIAC

Donald MacKay

Fire Control Systems

Behind the Eye

Later in the 1940’s, when I was doing my Ph.D. work, there was much talk of the brain as a computer and of the early digital computers that were just making the headlines as “electronic brains.” As an analogue computer man I felt strongly convinced that the brain, whatever it was, was not a digital computer. I didn’t think it was an analogue computer either in the conventional sense.

Human Analogue Machine

Human Analogue Machine

  • A human-analogue machine is a machine that has created a feature space that is analagous to the “feature space” our brain uses to reason.

  • The latest generation of LLMs are exhibiting this charateristic, giving them ability to converse.

Heider and Simmel (1944)

Counterfeit People

  • Perils of this include counterfeit people.
  • Daniel Dennett has described the challenges these bring in an article in The Atlantic.

Psychological Representation of the Machine

  • But if correctly done, the machine can be appropriately “psychologically represented”

  • This might allow us to deal with the challenge of intellectual debt where we create machines we cannot explain.

In practice …

  • LLMs are already being used for robot planning Huang et al. (2023)

  • Ambiguities are reduced when the machine has had large scale access to human cultural understanding.

Inner Monologue and Chain of Thought

HAM

HAM

Bandwidth vs Complexity
bits/min \(100 \times 10^{-9}\) \(2,000\) \(600 \times 10^9\)

The Strategic Challenge

  • We know everything we’re doing now is inadequate/anachronistic
  • We don’t know exactly how it’s inadequate
  • Traditional “plan-then-execute” approaches won’t work
  • Need adaptive, learning-oriented strategies

Exercise 2: SWOT Analysis for AI Transformation

Plenary: SWOT Insights

  • What’s surprising about your institution type’s position?
  • Biggest strength for AI adoption?
  • Most concerning threat?
  • Most exciting opportunity?

Lunch Homework: The Horizon Scandal

Please read about the Horizon scandal over lunch:

This background will be essential for Exercise 3 this afternoon.

Part 3: Maintaining Human Judgment and Building Trust

When Algorithms Override Human Judgment: The Horizon Scandal

The Horizon Scandal

Judgement in the AI and Data Era

The Big Data Paradox

  • We collect more data, but we understand less.

Wood or Tree

Big Model Paradox

  • Add complexity to the model to make it realistic.
  • Move model “beyond human intuition”
  • But model still falls well short of mark in terms of representing reality

Complexity in Action

Data Selective Attention Bias

BMI Steps Data

BMI Steps Data Analysis

A Hypothesis as a Liability

“ ‘When someone seeks,’ said Siddhartha, ‘then it easily happens that his eyes see only the thing that he seeks, and he is able to find nothing, to take in nothing. […] Seeking means: having a goal. But finding means: being free, being open, having no goal.’ ”

Hermann Hesse

The Scientific Process

Number Theatre

Data Theatre

Sir David Spiegelhalter

David Spiegelhalter

The Art of Statistics

The Art of Uncertainty

Increasing Need for Human Judgment

Diane Coyle

The domain of human judgment is increasing.

How these firms use knowledge. How do they generate ideas?

Intellectual Debt

Technical Debt

  • Compare with technical debt.
  • Highlighted by Sculley et al. (2015).

Lean Startup Methodology

The Mythical Man-month

Separation of Concerns

  • Decompose your complex problem/task into parts.
  • Each part manageable (e.g. by a small team)
  • Recompose to solve total problem

Addresses Complex Challenge

  • Highly successful approach to complex tasks.
  • Tuned to the human bandwidth limitation.
  • But the whole system still hard to understand.

Intellectual Debt

  • Technical debt is the inability to maintain your complex software system.
  • Intellectual debt is the inability to explain your software system.

Dealing with Intellectual Debt

What we Did at Amazon

Are Right a Lot

Leaders are right a lot. They have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.

Dive Deep

Leaders operate at all levels, stay connected to the details, audit frequently, and are skeptical when metrics and anecdote differ. No task is beneath them.

Thoughtsday

  • Dealing with highly operational decision making.
  • A day of reflection choose e.g. Thursday.
  • Ensure meetings are longer-term thinking.

What we did in DELVE

  • Covid 19 Pandemic
  • Royal Society Community Response
  • Support Government SAGE Committee
  • Diversity of scientific and economic expertise
  • https://rs-delve.github.io/

Delve Timeline

  • First contact 3rd April 2020
  • First meeting 7th April
  • First working group 16th April

Data at the Heart

  • Use data to answer policy questions.
  • Make international comparisons for input.
  • Challenges: around getting data.

Data as a Convener

  • Data allows externalisation of cognition.
  • Even when not existing, can ask: What data would we want?

Intellectual Debt and Superficial 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

Maintaining Human Judgment: Key Principles

  • Algorithms should inform, not dictate, critical decisions
  • Human judgment must remain accessible and exercisable
  • Build in mechanisms for questioning algorithmic outputs
  • Maintain transparency about when humans vs machines decide
  • Develop “intelligent accountability” for AI-assisted decisions

Reverse Mentoring and Listening Culture

  • In disruptive times, learn from below as well as above.
  • Exploration meetings that take into account all opinions.

Conclusion

See the Gorilla don’t be the Gorilla.

Exercise 3: The Horizon Scandal - Judgment Failures and Power Asymmetries

Plenary: Preventing Horizon-Type Failures

  • Never treat algorithmic outputs as infallible
  • Maintain accessible human override mechanisms
  • Build power balance into AI governance
  • Ensure diverse voices can raise concerns
  • Create intelligent accountability frameworks

Part 4: Strategic Implementation and the Attention Economy

Human Attention as Strategic Resource

The Attention Economy

  • Human intelligence is locked-in
  • This makes it a bottleneck

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)

Revolution

The Future of Professions

From Philosopher’s Stone to AGI

  • Philosopher’s stone: sought to transform base metals to gold
  • Modern parallel: Artificial General Intelligence (AGI)
  • Both concepts promise magical transformation

Philosopher’s Stone

The AGI Misconception

  • AGI based on flawed notion of rankable intelligence
  • Like an “artificial general vehicle” for all journeys
  • Intelligence is context-specific, not universal

Human Capital Index

  • World Bank Human Capital Index 2020
  • Measures health and education

Ranking

  • UK 0.78 11th globally
  • Italy 0.73 30th globally
  • US 0.70 35th globally
  • China 0.65 45th globally

Productivity Flywheel

Inflation of Human Capital

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

AI cannot replace atomic human

The Atomic Human Approach for Business

  • Human attention is the differentiator
  • Focus on how your human capital needs to adapt
  • People-first approach, not AI-first
  • Culture becomes the competitive moat

The Attention Flywheel: Reinvesting Human Capital

Attention Reinvestment Cycle

Emulsion: Combining Human and Machine Intelligence

Example: Data Science Africa

Data Science Africa is a bottom up initiative for capacity building in data science, machine learning and AI on the African continent

Example: Cambridge Approach

ai@cam

How ai@cam is Addressing Innovation Challenges

  • A-Ideas (across 20 departments)
  • Policy lab (with Bennett, Minderoo)
  • HPC Pioneer projects (with RCS, C2D3)
  • Accelerate programme (Schmidt Sciences funded)

Innovation Economy Conclusion

  • Interact directly with micro-demand
  • Release quality attention
  • Reinvest human capital in more innovation

Developing Board-Level Digital Literacy

Board-Level AI Governance Questions

  • What decisions is AI making or influencing?
  • Where does human judgment remain essential?
  • How do we know when AI systems are failing?
  • Who is accountable for AI-assisted decisions?
  • How do we maintain organisational culture with AI?

Exercise 4: Developing Institutional AI Strategies

Plenary: Contrasting Strategies by Institution Type

  • How do strategies differ by institution type?
  • What’s common across all types?
  • What surprised you in building a concrete strategy?
  • What’s your biggest open question?

Conclusion: Architecting Human-Machine Collaboration

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Key Takeaways: Strategic Framework

  • AI reshapes information flows - understand your information topography
  • Human attention is your scarcest and most valuable resource
  • Balance centralised oversight with devolved decision-making
  • Recognise LLMs as interfaces, not substitutes for judgment
  • Build intelligent accountability into all AI deployments

Leadership Imperatives

  • Lead with organisational culture, not technology
  • Invest in human capital development alongside AI
  • Maintain human judgment in critical decisions
  • Build governance for AI systems from day one
  • Focus on attention allocation, not just task automation

The People-First AI Strategy

  • Domain expertise must lead AI implementation
  • Develop institutional character around AI use
  • Create the attention flywheel for your organisation
  • Build trust through transparency and accountability
  • Remember: In the long run, your differentiators are human

Wicked Problems

Further Reading and Resources

Thanks!

  • company: Trent AI

  • book: The Atomic Human

  • twitter: @lawrennd

  • The Atomic Human pages atomic human, the 13 , Le Scaphandre et le papillon (The Diving Bell and the Butterfly) 10–12, Bauby, Jean Dominique 9–11, 18, 90, 99-101, 133, 186, 212–218, 234, 240, 251–257, 318, 368–369, Shannon, Claude 10, 30, 61, 74, 98, 126, 134, 140, 143, 149, 260, 264, 269, 277, 315, 358, 363, telepathy 248-50, anthropomorphization (‘anthrox’) 30-31, 90-91, 93-4, 100, 132, 148, 153, 163, 216-17, 239, 276, 326, 342, Blake, William Newton 121–123, Blake, William Newton 121–123, 258, 260, 283, 284, 301, 306, Michelangelo, The Creation of Adam 7-9, 31, 91, 105–106, 121, 153, 206, 216, 350, Blake, William Elohim Creating Adam 121, 217–18, baby shoes 368, topography, information 34-9, 43-8, 57, 62, 104, 115-16, 127, 140, 192, 196, 199, 291, 334, 354-5, anthropomorphization (‘anthrox’) 30-31, 90-91, 93-4, 100, 132, 148, 153, 163, 216-17, 239, 276, 326, 342, trust 43, 79, 100, embodiment factor 13, 29, 35, 79, 87, 105, 197, 216-217, 249, 269, 327, 353, 363, 369, topography, information 34-9, 43-8, 57, 62, 104, 115-16, 127, 140, 192, 196, 199, 291, 334, 354-5, MONIAC 232-233, 266, 343, MacKay, Donald, Behind the Eye 268-270, 316, ignorance: HAMs 347, test pilot 163-8, 189, 190, 192-3, 196, 197, 200, 211, 245, psychological representation 326–329, 344–345, 353, 361, 367, human-analogue machine 343–5, 346–7, 358–9, 365–8, human-analogue machine (HAMs) 343-347, 359-359, 365-368, Human evolution rates 98-99, Psychological representation of Ecologies 323-327, Horizon scandal 371, intellectual debt 84, 85, 349, 365, intellectual debt 84-85, 349, 365, 376, separation of concerns 84-85, 103, 109, 199, 284, 371, Tyson, Mike 92–93, 130, 193, 217, 225, 328, 348, cuneiform 337, 360, 390.

  • Guardian article on How African can benefit from the data revolution

  • Guardian article on Data Science Africa

  • blog posts:

    Dan Andrews image of our reflective obsession with AI

    Art is Human

    Dan Andrews image from Chapter 3

    Dan Andrews image from Chapter 3

    the launch of Facebook’s AI lab

    Open Data Science

    Playing in People’s Backyards

    Who is Stepping Up?

References

Brooks, F., n.d. The mythical man-month. Addison-Wesley.
Conway, M.E., n.d. How do committees invent? Datamation 14, 28–31.
Heider, F., Simmel, M., 1944. An experimental study of apparent behavior. The American Journal of Psychology 57, 243–259. https://doi.org/10.2307/1416950
Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., Sermanet, P., Jackson, T., Brown, N., Luu, L., Levine, S., Hausman, K., ichter, brian, 2023. Inner monologue: Embodied reasoning through planning with language models, in: Liu, K., Kulic, D., Ichnowski, J. (Eds.), Proceedings of the 6th Conference on Robot Learning, Proceedings of Machine Learning Research. PMLR, pp. 1769–1782.
Lawrence, N.D., 2024. The atomic human: Understanding ourselves in the age of AI. Allen Lane.
Lawrence, N.D., 2010. Introduction to learning and inference in computational systems biology.
MacKay, D.M., 1991. Behind the eye. Basil Blackwell.
Scally, A., 2016. Mutation rates and the evolution of germline structure. Philosophical Transactions of the Royal Society B 371. https://doi.org/10.1098/rstb.2015.0137
Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., Dennison, D., 2015. Hidden technical debt in machine learning systems, in: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 28. Curran Associates, Inc., pp. 2503–2511.
Simon, H.A., 1971. Designing organizations for an information-rich world. Johns Hopkins University Press, Baltimore, MD.
Susskind, R.E., Susskind, D., 2015. The future of the professions: How technology will transform the work of human experts. Oxford University Press.
The Admiralty, 1945. The gunnery pocket book, b.r. 224/45.