📌 Our critical mistake
AI case studies: June 2024 | How AI is transforming the world?
New email: Our critical mistake
Hey there, Yael!
Automation evaluation often involves a critical mistake of solely considering the existing market size and projecting the impact of new technology based on that market. Economists often overlook the practical realities when estimating the impact of AI on the labor force, failing to consider how new tools could transform the work engineers deliver. This oversight is a common challenge, and predictions about the impact of AI on the labor force are frequently misguided.
▸ The concern is that AI automation might halve the number of engineers needed. While AI can handle some engineering tasks, it's unlikely to replace engineers entirely:
Civil engineers might use AI for traffic flow optimization, but they'll still be needed to design new infrastructure.
Software engineers might see AI automate parts of coding, but new skills will be required to collaborate with AI for complex projects.
What to expect in other domains?
In marketing, this could entail infusing every asset that a company produces with a high level of design creativity or tailoring assets for specific geographic regions in a way that would have been impractical before.
In the legal sector, this might involve the capability to analyze all contracts and their associated risks with a depth of understanding that was previously unfeasible.
Product and engineering would involve distilling insights from customer feedback that would have been disregarded in the past.
In media, it could mean enhancing the ability to pair an advertiser with specific content or talent that might have otherwise been overlooked, leading to increased revenue.
So, what is important now?
We can identify at least three major factors (and there are likely more) that will drive the majority of the value generated by AI:
1. The level of automation applied to the work
This ranges from simple tasks like autocomplete suggestions in a text field or chatbots to more complex automation, such as AI agents that can automate entire processes or conduct extensive research to provide information.
Both ends of this spectrum can be valuable, but the value depends largely on how AI is utilized and the return on investment generated through automation.
2. The "cost" of the work being automated
Every task has its own associated cost due to time consumption or the high level of specialization required, making labor more expensive.
Tasks like software coding, content generation, contract reviews, and equity research synthesis have different values.
Generally, the more AI is applied to costly or specialized tasks, the more valuable it becomes.
3. The volume or frequency of the work being automated
The value of AI greatly depends on the frequency and volume of automated tasks.
AI's value is limited for infrequent tasks, such as changing application settings through a chatbot.
However, if AI is applied to processes that are executed hundreds or thousands of times a day within an organization, like QA testing or invoice routing, the value is significant.
The things to know
AI's value for infrequent tasks can be a double-edged sword. Here's why:
Limitations of AI for infrequent tasks:
Learning overhead: Training an AI for a task done rarely might not be cost-effective. The AI might not "learn" well enough to be consistently helpful.
User unfamiliarity: If users rarely interact with the AI, they might be unfamiliar with its capabilities and find it frustrating to use.
However, AI can still be valuable for infrequent tasks in some cases:
Standardization: AI can ensure consistent execution of even rarely performed tasks, reducing errors or variations.
Guidance: An AI assistant might still be helpful for infrequent tasks by providing step-by-step instructions or relevant information.
So, AI's value for infrequent tasks depends on the specific situation. It might be better suited for tasks with standardized procedures or where consistent guidance is helpful.
For your example of changing application settings, a well-designed user interface or a simple instruction manual might be more efficient than an AI chatbot.
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