Dilip Kumar offered a breathtaking proposition during his remarks at AWS re:Invent, the event that Amazon Web Services (AWS) is holding for developers, customers, analysts and others in Las Vegas.
“Imagine a future, not so distant, where 80% of the work that you’re doing is automated,” Kumar said. “And the other 20% is at 100x quality.”
That’s the future AWS is striving to achieve with Amazon Q, the artificial intelligence assistant that AWS claims “transforms how work gets done in your organization.” Kumar, vice president of Amazon Q Business, explored various ways businesses are leveraging Amazon Q just one year after the company launched it.
For example, Jabil, a global manufacturing company, uses it to help its engineers retrieve information for daily tasks. “The deployment provided our employees with access to over 1,700 policies, manufacturing specifications and troubleshooting documents in various languages,” said May Yap, Jabil’s CIO.
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How Amazon Q Works
Originally envisioned as a tool that would primarily benefit software developers, AWS quickly realized its potential as a tool for all kinds of workers. Still, even AWS has been surprised by the speed with which businesses have deployed it for diverse use cases.
“And that’s largely due to the ingenuity of customers, trying to figure out how to make generative AI work to transform their businesses,” Kumar said.
Amazon Q makes it relatively easy for nontechnologists to build small applications, called “skills” in Amazon parlance, to make their work easier. They can do so writing instructions in natural language; no coding knowledge is required. The process is becoming familiar to more people as they use publicly available tools such as ChatGPT.
When AWS set out to build Amazon Q, it had three goals in mind. First, it needed to be secure. Second, it needed to deliver to AWS customers’ authorized users “access to data in a much more unfettered way,” then enable them to act on it, Kumar said. “And third, we wanted to provide this assistance the way people naturally work, rather than inventing a whole new way of working.”
Businesses that have had the most success with Amazon Q are those that started with a clearly defined application, then expanded from there, Kumar said: “They can do a very quick before-and-after comparison to figure out whether they’re getting ROI. Once they establish that, they’re able to expand to many other use cases.”
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How Nasdaq and Asana Are Using Amazon Q
One business that’s had great success already with Amazon Q is the stock exchange Nasdaq. The company’s head of AI, Michael O’Rourke, said the organization wanted to create an internal AI platform that all employees could use, which Amazon Q made possible. It chose to build one platform for its developers requiring high proficiency with code writing and a separate, no-code platform for everyone else.
“We launched in August and the response has been overwhelming,” O’Rourke said. “Most users chose to use the no-code version of the platform. Employees like the fact that they can just click, create a skill, upload the documents, connect it to the data repositories and be using it within just a few minutes. There’s a lot of demand from nonengineers to have these capabilities.”
Asana used Amazon Q to build AI into its well-known project management platform, according Chief Product Officer Alex Hood. “We find that the best-performing teams have three tools: A place where their content lives, like Microsoft 365; a place where they communicate, like chat and email; and then there’s the coordination layer — who’s doing what by when? That’s Asana.”
Asana works “because there’s a graph database on who’s doing what and when that’s a single source of truth,” he said. “But then, when you add Amazon Q, that graph database can be greatly enhanced if it’s supplemented with the unstructured data that lives in the other two tool sets. When we get that data tied into the graph, then the answers and the context they get from Asana is much more holistic.”
Amazon Q acts a bridge between Asana and other work tools, allowing employees to easily create their own skills within the project management platform, Hood explained: “The way we talk about AI at Asana is in terms of AI teammates. These teammates live inside workflows and they do very specific tasks, you give them very specific instructions and put the human in the loop. That’s the recipe for success. Breaking down the work and having AI do very specific work instead of just asking it to do everything.”
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Managers and employees within specific lines of business are the most adept at figuring out how put generative AI tools to the best use for them, Kumar said: “Lines of businesses are very familiar with their pain points, and you don’t have to grin and bear it anymore. There are ways and means out of it, to be able to do something. The barrier to getting started is a lot lower.”
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