Inside the role: pioneering generative AI for business intelligence

Steve Ash’s first computer science love was database systems, which eventually blossomed into his master of science thesis on building database storage layouts and improving optimizer heuristics for new-at-the-time Solid State Hard Disks (SSDs). He went on to earn a Ph.D. focusing on machine learning and natural language processing.

Now, as a principal software developer for Amazon QuickSight, he’s working at the forefront of generative AI technology. He likens this time in generative AI tech to the early days of home internet, when customers paid for use by the hour.

“We are still at the very beginning of witnessing how generative AI will affect our software and day-to-day work,” Ash said.

In this Q&A, Ash shares insights from building Amazon Q in QuickSight's groundbreaking generative AI features. He discusses the value of rapid prototyping, the positive customer response to time-saving solutions, and the significance of integrating AI into existing user workflows. He also offers perspectives on overcoming challenges and maintaining a customer-focused approach while innovating with continuously evolving technology.

Amazon Q is a generative AI–powered assistant that can answer questions, provide summaries, generate content, and securely complete tasks based on data and information in your enterprise systems. Amazon QuickSight is the AWS unified business intelligence (BI) service built for the cloud.

With Amazon Q in QuickSight, customers get a generative AI assistant that allows business analysts to use natural language to build BI dashboards in minutes and easily create visualizations and complex calculations. Business users can also get AI-driven executive summaries of dashboards, ask questions of data beyond what is presented in the dashboards, and get auto-created narratives and presentations highlighting key insights, trends, and drivers.

After getting my undergraduate degree in computer science, I first went into industry as a programmer for a software vendor in banking tech before entering grad school to earn a M.S. and Ph.D. in computer science.

For my Ph.D. work, I switched focus toward machine learning (ML) and worked on applying natural language processing methods to data mining problems. As part of that work, I met Amazon Principal Scientist Andrew Borthwick at an academic conference, and then years later when I graduated, Andrew recruited me to his team at Amazon! I’ve been here almost seven years and have worked on several interesting projects that sit in between engineering and science work.

This is an interesting time. We’ve passed through an inflection point in terms of both technical capabilities (large foundational models) and the broader awareness and enthusiasm for folks to think big in applying this tech. QuickSight is such an exciting place right now, because we have a large, diverse set of customer personas who can benefit from applying AI in useful, thoughtful ways to improve their day-to-day work. Also, I think the fact that BI is the intersection of humans and data-based visual insights makes it a great place to invent with recent advances in multi-modal foundation models, which naturally combine information from language text and visual images.

I think we’re just scratching the surface here in terms of AI capabilities and how humans interact with AI. We finally invented useful chatbots and returned to the past of the text terminal as the primary modality of human computer interaction! As we did 50 years ago, we will surely graduate beyond the text terminal and invent new ways to combine text, visual information, and UI-based patterns for humans to work with AI capabilities.  

It’s neat that chatbots have captured so much public imagination about AI and hopefully inspired folks to think big about this technology. One part that excites me is how large foundation models change the calculus of building applied AI solutions.

Current large foundation models are good at following instructions given in natural language and for many use cases can extrapolate how to do a task from just a few well-picked, in-context examples. This makes rapid prototyping and changing model behavior a simpler “prompt engineering” task, which is much faster and more accessible to different skill sets.

Now I ask product managers to experiment in the Bedrock Playground directly to get hands-on experience with the art of the possible. This shift of making sophisticated AI accessible and understandable to a much larger population of builders will change how we build software, even in places that don’t seem chat-related at all!  

If you're passionate about cutting-edge technology and want to make a real impact, we'd love to hear from you. Check out our open roles in generative AI and apply today.