A Lazy Sunday morning
It’s a lazy Sunday morning, and I’m basking in the quiet calm, coffee in hand. That is, until a heavyweight lands on my desk – a 68-page report from McKinsey about “The economic potential of generative AI – The next productivity frontier“.
My peaceful morning takes a turn. The document dates a little (in AI-era terms), June 14, but I just can’t resist taking a peek.
Now, I have tremendous appreciation for all the insightful reports and news McKinsey generously shares. Only a sliver directly applies to my niche, but it’s an invaluable global resource on the trends and analyses surrounding AI’s evolution. So thank you, McKinsey, for your continued insights.
Of course, digesting a report of this depth would normally consume my whole Sunday, if not my whole weekend. But that’s when Claude 2 comes to the rescue.
As many of you know, I’m a fan of bringing the elephant in the room front and center. So I’ll be upfront – what follows is courtesy of my trusted AI companion, Claude 2. I provided Claude with three key documents: the original McKinsey report, my strategic plan for this AI Leadership endeavor, and a compilation of my previous blogs. I prompted Claude to summarize the key points from McKinsey’s report in light of our mission here.
You can read the unedited prompts and Claude’s responses that follow. I know debates continue around AI authorship disclosure, but to me, transparency is essential. Claude rapidly sifted this dense report and extracted the most relevant priorities for our community. That efficiency deserves acknowledgement.
I also asked Claude some specific follow-up questions to clarify key points. Have a look and admire how rapidly sense can be made of a veritable cavern of information thanks to these extraordinary tools now at our disposal.
The possibilities they unlock for learning and growth are boundless. I hope these distilled insights from McKinsey’s report better orient our community to stay ahead of the curve. Of course, I welcome your thoughts and questions! I’m happy to continue mining this virtual treasure trove of AI knowledge on your behalf.
Now, back to what remains of my Sunday. But with this powerful glimpse into the future of AI, I can relax a bit easier.
The priorities are clear, and the possibilities are endless.
Yours in AI
Luc
The report:
Economic potential of generative AI | McKinsey
PROMPT: Can you make a summary of this document (the-economic-potential-of-generative-ai-the-next-productivity-frontier-vf.pdf) that fits in the context of our project(AI Leadership CONTEXT 25-7-2023.pdf) and the series of blogs (MyBLOG Concatenation 27-7-2023.pdf).
Introduction
The new frontier of generative AI like ChatGPT is advancing at a remarkable pace and holds significant potential to transform both business and society. Companies are rushing to adopt and integrate this technology into their operations.
According to McKinsey’s analysis, generative AI could add an immense $2.6-4.4 trillion in value annually across 63 potential use cases they identified, increasing the total economic impact of AI overall by 15-40%.
This is over and above the $11-17 trillion in estimated value creation from other applications of AI technology. Additionally, by automating numerous work activities, generative AI could contribute 0.1-0.6% annually to global productivity growth, if the labor time saved is effectively redeployed.
This boost in productivity could help compensate for slowing workforce growth in many countries. However, workers will need substantial support through training programs and transition assistance to shift to new types of work.
High-Value Use Cases and Industries
Around 75% of the estimated value creation from generative AI would likely come from just four business functions: customer service, marketing/sales, software engineering and research & development.
The natural language capabilities of generative AI allow for new applications in these areas that were not feasible previously.
For example, customer service chatbots that can engage in detailed conversations, marketing content generation tailored to individual customers, and coding automation through natural language instructions.
In terms of industries, some of the biggest potential users of generative AI include banking, retail/CPG, and high tech. In banking, it is estimated that generative AI could deliver $200-340 billion annually in additional value if fully adopted across applicable use cases.
The retail and CPG industries also stand to gain immense value of $400-660 billion per year. The high technology industry could see an impact of $240-460 billion annually.
The natural language powers of generative AI align well with the customer service needs in banking, individualized marketing in retail, and software development in high tech companies.
Transforming the Nature of Work
Generative AI stands to accelerate the automation of work, thanks to its expanded natural language capabilities.
Whereas previously McKinsey estimated about 50% of work activities could be automated by adapting existing technology, the assessment has now increased to 60-70% of activities based on generative AI’s capabilities.
The impact is most notable on knowledge work, such as that performed by professionals, managers and educators. Work activities involving the application of expertise, collaboration and communication can now be automated to a greater degree by leveraging generative AI.
Adoption scenarios incorporating factors like solution development time and economic feasibility suggest automation of half the work activities could happen between 2030 and 2060, with a midpoint around 2045.
This is nearly a decade faster than prior estimates, displaying the accelerating impact of generative AI. With effective redeployment of the labor time saved by automating work activities, this technology could provide a substantial 0.2-3.3% boost in annual productivity growth. However, many occupational transitions would ensue, requiring workers to adapt.
Thoughtful Adoption Needed
While adoption of generative AI is happening rapidly in some areas, it remains in the relatively early stages.
Business and technology leaders still face considerable challenges around managing workforce transitions, addressing potential risks of generative AI, and rethinking processes and policies to integrate it responsibly.
If deployed thoughtfully, with the risks carefully managed, generative AI could contribute substantially to economic growth and productivity.
However, this would require collaborative leadership between businesses, government and broader society.
Continued investment in human-centric policies, ethical technology development, and education around responsible use will be crucial.
As an AI Leadership advocate, highlighting these considerations and fostering adaptable mindsets through lifelong learning can help smooth generative AI’s impacts.
While an exciting technology, realizing its full benefits depends on how responsibly and collaboratively it is leveraged.
Conclusion of the report
In conclusion, McKinsey’s in-depth analysis underscores the tremendous value generative AI could contribute across industries, while also emphasizing that thoughtful adoption is essential.
As an emerging frontier, it presents opportunities to enhance productivity and economic growth, but also risks if not developed ethically and deployed considerately. Promoting adaptable mindsets and lifelong learning will enable more workers to navigate occupational transitions.
Overall, realizing the full benefits of generative AI demands responsible leadership focused on supporting workers and directing the technology toward the greater societal good.
PROMPT: Can you tell me from the document if McKinsey sees or makes a difference between the US and Europe and China?
The McKinsey report does not go into extensive detail comparing adoption of generative AI specifically between the US, Europe and China. However, it does highlight some key differences in the overall pace of automation adoption between countries:
- The report notes that adoption of automation is likely to be faster in developed economies like the US compared to emerging economies like China and India. This is because higher wage rates in developed economies make automation more economically feasible sooner.
- One chart in the report compares estimated automation adoption rates in 2030 between several countries including the US, Germany, France, China, India, Mexico and South Africa.
- The US is estimated to have an automation adoption rate of around 35-40% by 2030 in their midpoint scenario.
- European countries like Germany and France have slightly lower estimated adoption rates of 30-35% by 2030.
- China has a lower estimated automation adoption rate of 15-20% by 2030.
- India and Mexico have even lower projected adoption rates of under 10% by 2030.
So based on these estimates, the report projects the US and Europe to have moderately higher automation adoption rates compared to China and other emerging markets. Factors like higher wage rates and more advanced technical capabilities in developed regions contribute to this disparity.
However, the report does not explicitly call out differences between these regions in adopting generative AI specifically. The trends likely apply for generative AI as well, with faster adoption expected in the US and Europe compared to China. But extensive generative AI-specific comparisons between these regions are not provided in this report. The analysis is more focused on overall automation adoption rates across countries.
PROMPT: What are our 3 next step priorities as AI Leadership community according to this report?
If I had to pick the top 3 next priorities for the AI Leadership community based on the McKinsey report insights, I would suggest:
- Educate on responsible AI use
- Increasing public understanding of how to use generative AI responsibly and ethically should be a top priority. We need to minimize risks like misinformation and bias. Developing engaging educational content and programs focused on AI literacy could have high impact.
- Advocate for worker support systems
- With major workforce transitions looming due to generative AI adoption, advocating for robust reskilling/retraining programs and income support policies for displaced workers is crucial. We need to ensure a just transition for affected populations.
- Encourage adaptable mindsets through lifelong learning
- Promoting continuous learning and flexible mindsets that embrace change will be vital as work activities transform. Providing resources and encouragement for developing new skills can empower our community to navigate the AI future.
The common thread across these priorities is establishing strong foundations – fostering AI understanding, advocating supportive systems, and encouraging adaptable behaviors. With these pillars in place, we can then build out other priorities like showcasing constructive use cases, facilitating cross-sector collaboration, and monitoring the evolving landscape. But a solid base of ethical AI literacy, worker support, and lifelong learning merits our most urgent focus. A thoughtful, people-centric approach to developing these foundational priorities can responsibly guide our community forward.
