In previous sessions, you saw the rapid pace at which consumers and companies have adopted generative AI technologies. The fact that ChatGPT received 1 million sign ups within 5 days and a plethora of generative AI companies have emerged in different niches is a testament to the interest shown in these technologies. In coming years, this interest will translate into a major impact across different industries and functions.
In this segment, you will learn about the impact of generative AI through three different lenses:
- Impact on the whole economy.
- Impact on different sectors of the economy.
- Impact on different functions within companies.
In the next video, Kshitij will talk about the impact of generative AI on the economy.
Kshitij outlined two ways in which generative AI can generate value in the economy:
- Novel use cases where generative AI can solve challenges faced by organisations.
- Improved productivity of workers in enterprises that adopt generative AI tools.
In session1, you saw various examples of new generative AI tools that are already solving problems. Those products, along with other applications of generative AI models, can improve workers’ productivity as well. Let’s review the generative AI product Adobe Fir that we saw before and analyse the different ways in which it can generate value.
The improved productivity of professionals using Adobe Firefly is clear. A task such as recolouring an image using a different colour palette, which may require multiple hours of work if done manually, can be completed in seconds using Adobe Firefly’s built-in recolouring tool. One novel use case arising from its use could be generating personalised marketing creatives for different demographics. Personalised marketing campaigns elicit more emotional responses from viewers and, consequently, may increase the impact of the campaign in terms of revenue.
Before moving forward, answer the following question to check your understanding of what we have covered so far.
Next, Kshitij cited a report by McKinsey & Company to quantify this economic impact. You can read more about this here. The graph from the McKinsey report is given below.
The values given in the graph are in trillions of dollars. As you can see, new generative AI use cases can have an impact with a monetary value of anywhere from $2.6 to $4.4 trillion. Whereas, if you include the impact from additional worker productivity, the number rises to anywhere from $6.1 to $7.9 trillion. To make sense of these numbers, you can compare them to more tangible values. The estimated annual GDP of the United Kingdom in 2023, the 6th biggest economy in the world, is roughly $3.15 trillion. Another tangible number is the market capitalisation of Apple in early 2023, the biggest company in the world, which is roughly $3.o3 trillion.
In the next video, let’s zoom in further and understand the impact of generative AI across industries and function.
Kshitij started by listing the industries that generative AI will impact. According to research, technology, retail and banking are the three industries that will be impacted the most. Manufacturing, healthcare, logistics and education are some other industries that will also be significantly impacted. You can read more about this in the McKinsey report shared above.
Before moving forward, answer the following question to check your understanding of what we have covered so far.
Next, Kshitij discussed the impact of generative AI across different functions using a graph from a report by Goldman Sachs.
You can find the report from where this graph is sourced here. According to this report, different functions can be impacted by generative AI in two different ways:
- It can complement the work done by workers in that function.
- It can replace the workers in that function.
As you can see in the graph, almost all the listed functions are getting impacted, while the depth and kind of impact varies from one function to another. Before moving forward, answer the following question to check your understanding of what we just covered.
In the next video, Kshitij will elaborate further on the impact of generative AI on different functions.
The graph given below illustrates the impact generative AI will have on different functional spends.
Functional spend refers to the amount of money spent on the different vertical functions within a company. As you can see from the graph, software engineering and customer operations will be the most impacted, whereas corporate IT and strategy will be the least impacted. You can read more about the impact of generative AI on customer operations in this article by Boston Consulting Group and this article by Salesforce.
This concludes our discussion on generative AI’s impact across industries and functions. Before moving forward, answer the following questions to evaluate your understanding of the topics taught in the segment.
In the next segment, you will learn about the use cases of generative AI in different industries.