MAIO

Split T into T1, T2 and assign one of them to A

Create a new task for AI, human uses the AI prediction + data to predict on T

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• Even if AI is accurate – Trust own judgement
• When AI makes an error – Penalize algorithms more than humans

Use less energy to make decisions
Little bit of stress followed by reward is addictive
Transfer learning
Social conformance & power

NO

• Feedback may have negative feedback for driving
• Only people seeing very low scores improve their performance
• So how to design better feedback?

Trust
Blackbox nature
Accuracy
Threat
Lack of control
Bias

• “BCG consultants using AI completed 12.2% more tasks while doing it 25.1% faster. They also
produced over 40% higher quality results compared to those not using AI. That seems quite
incredible.”
• BUT
• “Despite these productivity gains, the study found a significant increase in errors when tasks
fell outside the AI enhancement. Consultants using AI were 19 percentage points more likely to
produce incorrect solutions in such scenarios.”

• “Consistent with prior work, our results show that access to GPT-4 significantly improves performance
(48% improvement for GPT Base and 127% for GPT Tutor). However, we additionally find that when
access is subsequently taken away, students actually perform worse than those who never had access
(17% reduction for GPT Base). ”
• “ Our results suggest that students attempt to use GPT-4 as a "crutch" during practice problem
sessions, and when successful, perform worse on their own.

Let’s say human can choose how much weight to put on AI decision
With full autonomy (people can choose any weight from 0 to 100% on AI)
• People put Naïve weight – close to 50% and does not change it even if the performance goes down

• People put better weights and improve performance of the system
• Due to bounded autonomy humans are more sensitive to AI accuracy, put more weight to
“accurate” agent

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Automation

If human has tacit knowledge (that cannot be tabulated
nor guessed) → use human for simpler problems, use an
ensemble of the two in a complex problem

We have to deeply understand how to use Al in a business context
Consider how it will interact with 'people'
Test in the field, and iterate

1 Representation: Digital representation of information, (e.g: image)
2 Replication and Connectivity: Copy can be accessed through a digital network under zero marginal cost (e.g: replicaitron in networks)
3 Aggregation and Interaction: Data aggregation and the interaction between produces and consumers (e.g: sharing eco-system; creation -> publishing->promoting->receiving likes)

Yes, example airbnb

Mainframes / Corporate Networks

PC and Web

Cloud and Mobile

Networks and AI

Pre 1995

▪ Corporate Networks
▪ Tools, applications
▪ Mainframes/minis
▪ Email, early EDI

Products, Industries
▪ IT ecosystem begins
▪ Software licensing
▪ Direct monetization of
customer

IBM, digital

1995 –2005

▪ Networks explode
▪ PCs, browsers
▪ Static content

Early Software Ecosystems
▪ IT ecosystems scale
▪ Localized networks within
industries

Micosoft, Yahoo, ebay

2005 – 2015

▪ Mobile explodes
▪ Cloud emerges
▪ AI returns

Ecosystems Network Across Industries
▪ Digital APIs begin to connect industries
▪ Value capture starts to tip

Google, facebook

2015 – ???

▪ Cloud dominates
▪ IOT, AR emerge
▪ “AI first

“Hub Economy”
▪ All industries connected into a giant ecosystem
▪ AI driving transformation
▪ Collisions between digital and traditional firms

Alphabet, alibaba, amazon

Differentiation and Cost leadership

Differentation, they have a lot of different firms and servivies like meal, books etc 

A business model is defined by how it creates and captures value from its customers

Value Creation is the reason the customers choose to use a company’s products or services, and the particular problem the company is solving for customers

Value Capture is the value a company captures from the customers purchase

• Price
• License
• Promotion

The goal of an operating model is to deliver value at scale, to achieve sufficient scope and to respond to changes by engaging in sufficient learning

Scale means designing an operating model to deliver as much value to as many customers as possible at the lowest cost

• Volume
• Complexity
• Customers