Since launching its Generative AI Innovation Center in 2023, Amazon Web Services Inc. has had one primary goal: help customers turn the potential of artificial intelligence into real business value. Now, the company has invested an additional $100 million in the center to enable customers to pioneer the new wave of autonomous agentic AI systems.
Post-announcement, I talked with Taimur Rashid, managing director of generative AI innovation and delivery, who oversees the center. He told me that education about AI continues to be a big part of the Center’s mission. “As new as generative AI is as a technology, one of the things that we can do to help our customers along that journey is educating them, showing them the art of the possible.”
To make that goal a reality, Rashid said AWS has been steadily expanding its gen AI capabilities. “We’ve added machine learning capabilities, gen AI capabilities and Bedrock, which is a foundational platform for building gen AI applications,” he said. “By also bringing human expertise, we can really help customers with that overall journey.”
As you would expect, the AWS Generative AI Innovation Center isn’t a building or campus. It’s a global organization of AWS experts that work closely with customers worldwide to help them successfully navigate, learn what AI can offer, and build AI capabilities at scale. Working with the center, customers can launch deployment-ready solutions in as little as 45 days. It’s this combination of collaboration, curated content and expert support that makes the center unique.
The human factor is key
AWS believes that there is an important role for people to enable gen AI to deliver on its promise to benefit a wide range of customers. “We are a multidisciplinary team of AI strategists, and forward-deployed engineers,” Rashid said. “We can really be very intentional about helping customers with how to look at gen AI, and then from there, productionizing systems so that they can ultimately get the business value out of it.”
He also noted that customers want to educate their teams. “They want to ensure that they can utilize the technology in the best way. What are the learnings? What are the best practices and approaches?” he added. “That’s where we help bridge that gap. Our most experienced customers in the enterprise space all the way to medium-size, even emerging startups have reached out to us saying ‘we need some unique help with how we look at model customization.”
One example he pointed out: “RobinAI, with its AI platform for the legal industry, is a great example of that. They specifically wanted to fine tune models to help lawyers and paralegals process hundreds of pages, and they got our expertise around that too.”
Another customer that’s working closely with the AWS team to ensure it gains the full benefits of gen AI is Jabil, a large manufacturing company. Rashid explained that in just three weeks, it deployed an intelligent shop-floor assistant using Amazon Q with more than 1,700 policies and specifications across multiple languages, reducing the average troubleshooting time while improving diagnostic accuracy. There’s technical help that AWS offers, but as Jabil started to adopt it, it required some guidance to optimize the cost and make it more efficient.
The center can help organizations kickstart their AI plans. Almost every business and information technology leader I have talked with has dozens, even hundreds of proposed AI projects. The technology is so new that most customer teams are not yet fully equipped with gen AI skills.
They have literacy around data and experience with classical machine-learning models, but when you look at gen AI, they are dealing with a plethora of large language models. Customers want help to determine which model to use. The AWS Generative AI Innovation Center helps customers better understand how gen AI can be used most effectively.
Not surprisingly, Rashid said the gen AI choices available to the typical company can be overwhelming. “A senior executive from a travel and hospitality company told me they had identified 300 use cases and needed help prioritizing them,” he said. “There’s a whole rubric of things that we help customers with, because either the technology is too new, or their teams have not been upskilled on it. We do it for them, which not only helps the customer navigate the space, but we teach them as we go so, they can be more self-sufficient over time.”
Past is prologue
When AWS opened the center in 2023, customers looked at chatbots as their best AI entry point. “As they gained experience and saw all the things they could accomplish with AI, we saw more use cases around content summarization or generation,” Rashid recalled. “It’s like how things quickly progressed at the advent of cloud computing.”
Like gen AI, he added, “cloud was a new emerging technology; a paradigm shift for many people. So, we invested quite heavily in teaching customers, enabling coursework through training and certification. We’re making very similar efforts with AI, too. In fact, I think with AI we must be a lot more intentional, because it’s not only a technical competency that we have to educate customers on. We have to show it in a more immersive way.”
Leveraging partners
Partners are a key part of the Innovation Center’s work. Last year AWS started a Partner Innovation Alliance that brings a subset of its gen AI competency partners closer to the center and teaches them the center’s methodologies and approaches. As a way of scaling, AWS is taking the best practices it has learned along the way and educating its partners. It currently has 19 partners in this Innovation Alliance, including Deloitte, Booz Allen Hamilton and Capgemini. There are also several boutique partners, these are ones that are born in the cloud or digital-native consulting partners, as well as regional coverage in markets such as Korea and Latin America.
AWS also has Innovation Center teams in various geographies around the world. “There’s a broad set of things that every region looks at from a gen AI perspective,” Rashid said. “In the Middle East and Africa — and even in Europe — we see a huge emphasis around sovereign AI. Customers are asking how they could use AI to advance many aspects of their society and their nations from health care and government services to education. What’s nice about how we’re structured is we have resources within those regions that can respond very quickly and in alignment with our regional sales teams to meet some of the unique needs that we see in different geos.”
Embracing startups
The AWS Generative AI Innovation Center team is also prioritizing working with startups. Though AWS has a long history, it has been more methodical of late.
Startups bring unique technology. By bringing this audience into the Innovation Center, AWS can help startups get enterprise-ready so they can jointly service customers. This is an obvious win for the startup but also AWS as it creates some consistency in experience.
Avoiding agent overload
As in most areas of life, there can be too much of a good thing in the world of agentic AI. Specifically, as agentic AI continues its explosive growth, how can organizations avoid having 100 applications that come with 100 agents all trying to chat at users and give advice on what to do?
That’s one of the goals of AWS’ recently announced preview release of Amazon Bedrock AgentCore, which enables customers to securely deploy and manage a large number of agents.
“During a recent trip to New York, every agent conversation I had was about ‘how should we think about this world of integration and permissions when it comes to agentic AI?’” said Rashid. “That’s why the launch of AgentCore is so timely. The primitives [foundational, reusable building blocks that enable AI systems to act autonomously and achieve complex goals] that are offered through AgentCore help establish not only integration, which is one aspect, but then the data permissions that must go with it.”
Ultimately, he added, as companies get their agents to learn reason and then act, permissions become very important. “Right now, we have building blocks which are important — such as MCP [Model Context Protocol] and AgentCore,” he said. “It’s about how you put them together to integrate them into the existing fabric of the application without having to do a massive overhaul. Over time, companies and teams will get data better integrated. They’ll get a more specific application strategy, but I do think you’ll see a lot of agents. We’re early in that cycle right now, but it’s very important for us to guide customers to avoid the problem.”
There isn’t a company I talk to that isn’t interested in gen AI, but new landscapes can be confusing and hold customers back. The AWS Generative AI Innovation Center is an excellent resource for AWS customers to understand all the technology, how to deploy and to ensure that as they look to scale up gen AI, they are maximizing benefits while reducing risk.
