I was impressed by the case where Moderna has adopted ChatGPT in their internal daily tasks at every business process. Below is the short video snippet introduction.
During the beginning of 2023, I believe that tons of people have stunned by how ChatGPT demonstrates its amazing capabilities to us. Now we pass half of 2024, I was wondering what on earth this kind of tool has been adopted in the real world. Don’t get me wrong that I never doubt the power of LLMs. I strongly believe one day people will get familiar with it just like how we use Power Point or Google Search. But as a Product Designer, I always keep curiosity of how people use it, where they use it, or even why or why not they don’t use it.
If you look into job boards (I mean the Taiwan market) online, you might be surprised that the amount of AI-related jobs, or the industries that they claim they do embrace AI are not quite wildly proliferated; just a few, and it doesn’t surprise me though. It’s kind of like everyone says that the Bible is a huge classic, you must read it, but when you ask one more questions further, no one really has it or read it through.
That’s why it push me to go back to check how those tops catipalize it, or monitize it in this wave of AI. OpenAI is definitely one of them, needless to say lots of rumor around it (the investment, the cost, the barrier, the privacy and security issues … ). In the OpenAI News Stories, there are totally 43 cases illustrating how their clients take advantage of ChatGPT in their respective domains.
In all here, I’ll try to use the framework of AI use cases clearly pointed out in one of th excellent courses that I took - AI for Business Leaders, which is hosted by Allie K. Miller (the course link), and plus my little bit of little thought.
By industry
Based on the research released by McKinsey, in 2024 looking by industry, the biggest increase in adoption, compared with 2023, can be found in professional services (such as HR, Legal, consulting, marketing, R&D, etc).
Among the stories of OpenAI, 15 cases are around Technology industry, for example like Ada, Upwork, Salesforce, Wix, and Typeform. They adopt ChatGPT in the areas from internally use only to customer-facing services. But the industry here I mention is not quite reflected back to the applications that those companies use the AI for. Take Upwork for example. Upwork is the world’s largest work marketplace, connecting businesses and freelance professionals worldwide. They launch several features powered by ChatGPT, such as Upwork Chat Pro, Proposal tips and Best Match insights, as well as enterprise daily productivity like coding and QA.
Another sector is focused around healthcare industry. Take Color Health as an example. They work with OpenAI to pioneer a new way of accelerating cancer patients’ access to treatment. The copilot application’s output is analyzed by a clinician at every step and, if need be, modified before being presented to the patient. Screening, diagnosis, and treatment for cancer is notoriously complex and time-consuming. Their next step is to use the copilot application to provide AI-generated personalized care plans, with physician oversight, for over 200,000 patients.
The last example of the industry is education. Take ASU as an example. ASU is enhancing educational outcomes by integrating ChatGPT Edu into projects across teaching, research, and operations. President Crow envisions a future where AI will continue transforming higher education, making it more personalized and inclusive. In under a semester, ChatGPT is guiding students through personalized learning experiences and giving time back to faculty.
Finance, legal and retail are also listed on the top sectors that gradually embrace AI in decision-making and streamlining in their workflow among OpenAI’s stories.
If you ask me that for those cases, is it necessary or urgent right now to adopt some kind of AI tools in the workflow or daily tasks? My answer is definitely no, and the simple and no brainer reason is that we have already been here for decades without those AI tools; why bother to have one right now, right?! But if we truely face those details in more nuanced granularity, you will find out how much efforts and resources have been wasted in dealing with those devils. There are so many unstructured data and labor costs are ignored or spent no where, and we just get used to it or not willing to pick it up and re-assemble it into valuable pieces. That’s a twofold waste. In the theory of Jobs to be Done, aren’t those ChatGPTs just act like the agents that we hire to clean up the mess in our working pipelines?! We are not necessarily hungry for the flowers, but we definitely want to be a Super Mario.
Let’s move on to the second dimension.
By role
Again let’s take a look at how different roles adopting AI (or specifically Gen-AI) in their given function.
Based on this chart, Marketing and Sales, Product and IT are the highest adopters; corporate functions and operations are the second, and Software, HR and Risk are in the middle. However the interesting thing is that, if we don’t count the internal function teams inside of the corporation, the OpenAI’s cases that aim for marketing and sales people are pretty low (but so many companies are trying to increase their sales). Probably because they use the AI tools not that much heavy or only limited to a sort of side project to experiment, where afterall the AI tool is pretty convenient to try on even like amateur.
In Allie’s course where she mentions the tools exist for certain kind of roles is the first heads up. Some roles are industry agnostic (like project managers), while some roles are industry specific (like doctors). Different roles have different tools to use when it comes to adopting AI in their work pipelines. Some of the jobs are time-consuming or low-value but we need some people to get it done efficiently but manually.
In OpenAI’s stories, if we focus on Technology sector, most of the cases are around the linkage of two categories:
1) Relationship between the people from business operation or provider side and the people who use it. Imagine how much data has been created or generated from the end users in terms of customer service and support, content creation like multi-medias and feedbacks.
2) Relationship between employees and the jobs to do at large scale. Imagine the amount of Emails, site content, documents to process or game materials. Those are the areas that AI could enhance, integrate, match, enrich, automate, interpret or infer, in the blink of an eye.
Another important thing to notice is that we need to be aware of what the other side of the roles are using. For example, if job seekers are using some AI tool to help them build resumes, maybe recruiting company could consider adopting the similar tool in their process at service.
Take Stripe as an example. They develop a FAQ chatbot in a 100-person hackathon for the external developers to help them identify user questions, read detailed documents, identify relevant section and summarize the solution.
Stripe DOCS (Source: OpenAI Stories)
Let’s move on to the last dimension.
By task
Tasks are closely coupled with the roles in an organization to conduct specific functional activities. According to McKinsey’ The state of AI in 2022, Some of the top use cases of functional activities are Service operation, Creation of new AI-based products, Customer service analytics, and New AI-based enhancements of products.
Look back to OpenAI’s stories, some of the top cases are pretty close to McKinsey’ chart. The areas like productivity, more personalized experiences, information retrieval, feedback analysis and lead generation, are benefit from ChatGPT’s capabilities.
Why is this important? The answer behind this dimension is pretty straight forward: How many people hours can I save? AI infrastructure implementation costs money, and if the tasks that AI is going to solve cannot be paid off, people might be hesitate to adopt it.
Mapping the outcome that AI is helping to achieve with the business metrics would be a high priority so that we can clearly know if these efficiency enhancement, customer satisfaction, or tool performance, diagnostic accuracy, or customer engagement are well addressed in the business scope and worth investment.
One of the key points mentioned in Allie’s course is that when a company uses those AI-powered services to its hundreds of clients to be able to access to the source of orchestrated database, it would make clients feel respected and also save their time significantly. One of the typical examples is contract management, or like patient care in healthcare industries. We will save lots of valuable resources from stucking in the long and tedious process, and also raise our customers success and satisfaction rates.
Closing thoughts
See how ChatGPT has been adopted can give me clear picture of how business solve the real problems. We can jump in a future task from a very simple mindset of how we can get it done by AI; kind of like AI-first mindset. Of course not everything has to be with AI at all, but if you know someone or somewhere, the case where people solve the problem better and more efficient than you do, you should think about it and learn from it too.
Real use case is highly related to the experience, and the experience is inherent into the problems we have long been facing and dealing for years. Designers know that. What is the next area to move on? We’ll see.
By the way you can check the link of the file on Google Drive where I put 43 stories together in a table to be easily scan through.
To be continued.