McKinsey's Generative AI Report: 10 Key Takeaways

Last week McKinsey released their generative AI report.

Here are the 10 key takeaways everyone must know:

— Alex Banks (@thealexbanks) June 22, 2023

1/ Value potential by industry

Generative AI has the potential to generate $4.4 trillion in value across industries.

• $460 billion in high tech. Predominant factor: software engineering
• $390 billion in retail. Predominant factor: marketing and sales pic.twitter.com/pjCyUCldwJ

— Alex Banks (@thealexbanks) June 22, 2023

2/ Value potential by function

4 business functions account for ~75% of the total annual value of AI:

• Customer operations
• Marketing and sales
• Software engineering
• Research and development pic.twitter.com/hTaGsmzJkj

— Alex Banks (@thealexbanks) June 22, 2023

3/ Key use cases

Banking→legacy code conversion
Retail→consumer research
Pharma→research and drug discovery

Example:

The University of Washington recently used machine learning for protein design.

It allowed them to tailor protein complexes to specific biological responses pic.twitter.com/oTas43ISOz

— Alex Banks (@thealexbanks) June 22, 2023

4/ Achieving human-level performance

AI is expected to match median human performance and reach top-quartile human performance earlier than expected.

McKinsey's estimate for AI's natural language understanding:

2017 estimate: 2027
2023 analysis: 2023

Timelines are shortening. pic.twitter.com/EIUk5HHoVx

— Alex Banks (@thealexbanks) June 22, 2023

5/ Automation is increasing

The total percentage of hours that could be automated by integrating technologies that exist today has increased from 50% to 60–70%.

The technical potential curve is steep because of the acceleration in generative AI’s natural-language capabilities. pic.twitter.com/CizF0gf7fX

— Alex Banks (@thealexbanks) June 22, 2023

6/ Automation adoption has accelerated by a decade

McKinsey modelled adoption scenarios for the spent time on work activities reaching 50% automation:

2016 estimated midpoint was 2053
2023 estimated midpoint is 2045

This is an acceleration of almost a decade. pic.twitter.com/qYfmSOIsvV

— Alex Banks (@thealexbanks) June 22, 2023

7/ Generative AI is likely to have the biggest impact on knowledge work

Particularly activities involving decision-making and collaboration.

This previously had the lowest potential for automation. pic.twitter.com/xZgI0hytFk

— Alex Banks (@thealexbanks) June 22, 2023

8/ Automation has the greatest impact on higher-educated workers

Labour economists often note that the deployment of automation technologies has the most impact on low-skilled workers.

Generative AI has the opposite pattern—the greatest impact is with more-educated workers. pic.twitter.com/riu7aHdvxo

— Alex Banks (@thealexbanks) June 22, 2023

9/ Displacing college degrees

High-wage knowledge work activities were previously considered immune from automation.

AI will challenge the attainment of a multiyear degree as an indicator of skill.

This could lead to a more skills-based approach to workforce development. pic.twitter.com/vRGUWLdIKL

— Alex Banks (@thealexbanks) June 22, 2023

10/ Propelling higher productivity growth

Global economic growth was slower from 2012 to 2022 than in the two preceding decades.

Generative AI helps accelerate productivity growth and compensates for declining employment growth by automating individual work activities. pic.twitter.com/VVGelA5spF

— Alex Banks (@thealexbanks) June 22, 2023

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— Alex Banks (@thealexbanks) June 22, 2023

Full report by McKinsey:https://t.co/tnOyZRQ2Vn

You can read more about AI protein design here:https://t.co/8tT9Qjg42b

— Alex Banks (@thealexbanks) June 22, 2023