Artificial intelligence is rapidly moving from experimentation to large-scale adoption, and cloud providers are at the center of this transformation. Among them, Amazon Web Services (AWS) is helping organizations harness the power of generative AI with tools, infrastructure, and strategies that drive measurable business outcomes.
In a recent podcast conversation between Shu Sia Lukito, Partner Solutions Architect at AWS, and Fabrice Bagniakana, AI Director for Europe at TD SYNNEX, Shu Sia shared how AWS is shaping the AI landscape through its comprehensive AI stack and groundbreaking solutions like Amazon Q. The discussion explored AWS’s approach to empowering partners and customers, real-world use cases driving adoption, and how collaborations like the TD SYNNEX Cloud Lab are enabling partners to accelerate their AI journey with hands-on experiences.
Fabrice: Shu Sia, could you introduce yourself and tell us a bit about your role at AWS?
Shu Sia: I will be happy to, Fabrice. Hi everyone, my name is Shu Sia Lukito. I am a Partner Solution Architect at AWS. Think of me as a technical resource who provide architectural guidance, technical strategy, and enablement support to help partners build and scale their AWS practices.
Fabrice: AWS is playing a key role in shaping the AI landscape. Could you share a bit about AWS’s overall strategy in AI and how you’re supporting customers and partners?
Shu Sia: AWS’ approach was to design a comprehensive generative AI offering that provides:
- The broadest choice of foundation models for every use case
- Easy-to-use tools for every type of customer
- Purpose-built infrastructure to help you scale
- A comprehensive set of data services
This approach is manifested in the AWS AI Stack where we have Applications in the top layer with ready to use AI applications like Kiro, Amazon Q. The middle layer with flexible development platform like Bedrock. And bottom layer with complete ML infrastructure like SageMaker AI and purpose built AI chips for performance.
Fabrice: Many in our audience may not yet know Amazon Q. Could you explain in simple terms what it is, and how it helps organizations?
Shu Sia: Amazon Q is a generative AI-powered assistant for accelerating software development and leveraging companies’ internal data. Companies can use it to make generative AI securely accessible to everyone in their organization to streamline processes, enhance decision making, and boosting productivity.
Let’s talk about how different personas can use Amazon Q. Developers and IT professionals can use it for tasks like coding, testing, upgrading, troubleshooting, and security scanning. Business users can have tailored conversations, solve problems, generate content, build dashboards, take actions, streamline tasks, and more. Customer Service agents can detect customer issues and deliver real-time, personalized responses.
Fabrice: From your perspective, what trends are you seeing in AI adoption among customers, and how does Amazon Q fit into those needs?
Shu Sia: We’re seeing 65% of organizations already using generative AI in at least one business function, and half have moved beyond experiments to production. What’s most compelling is that 40% are already seeing real productivity gains. These aren’t just pilots anymore – organizations are getting tangible results!
Amazon Q can help organizations reduce time to deploy new features, complete tasks faster, and minimize repetitive actions. This results in efficiency improvements by up to 70%. Amazon Q has also led to increases in code quality and integrity, helping customers create better performing and more secure software.
Fabrice: Let’s talk about our collaboration through the Cloud Lab, which makes it easy for partners to explore and demo AI solutions. How do you see this environment helping partners accelerate their AI journey?
Shu Sia: For those who don’t know what TD SYNNEX Cloud Lab is, let’s use a car buying experience to illustrate. When you want to buy a car, you want to test drive it. If you SEE a demo of a luxury car at the mall, it could be interesting, but it’s not likely for it to result in a purchase. But, if you get to DRIVE a luxury car, you’ll want to find a way to buy it. Think of your customers in the same way; don’t give them a free trial… Give them an experience, with a reason to buy.
How exactly do Cloud Labs help accelerate opportunities? Well, customers are flooded with options and information, and they need more than just a sales pitch; they need to see how a solution or product can help to solve their specific challenges within their own environment. That’s where Cloud Labs comes in.
TD SYNNEX and AWS have collaborated in building the Amazon Q Cloud Lab. It comes pre-populated and trained with data for national parks and insurance provider scenarios. Additionally, the Amazon Q Cloud Lab can showcase other use-cases, like data extraction and summarization of financial data, technical manuals, drug trials, and human resources payroll documents, to name a few. Leveraging this service allows partners to quickly enable customers to interact with the model to get a better understanding about how to rapidly prototype and expand the data sets and use cases.
Fabrice: More broadly, AWS and TD SYNNEX are working closely together to bring Amazon Q to partners across Europe. Could you share what this collaboration means for partners and customers?
Shu Sia: Beyond Cloud Labs, AWS and TD SYNNEX have been collaboratively building programs that can help our partners and customers accelerate their AI journey. For example, AI Practice Accelerator program is designed to empower partners to enter the AI market in their area of proficiency, with personalized support throughout the go-to-market process – from technical enablement (on Amazon Q and other AWS AI solutions) to marketing and selling the solution to their customers. These are just a few of what came out of our collaboration. We will have many more to come, not to mention the ongoing incentive and funding programs. The takeaway here is that partners don’t have to go through building their AI journey alone. TD SYNNEX is here to help.
Fabrice: Where do you see the biggest opportunities for channel partners to leverage Amazon Q in their solutions or services?
Shu Sia: There are so many opportunities. Why don’t we talk through several real use cases to give an idea on how partners can help customers boost productivity with Amazon Q.
Bayer AG is one of the largest pharmaceutical and biomedical companies in the world. By implementing Amazon Q Business across their new Decision Science Ecosystem (DSE) platform, they enable data scientists to quickly build, train, and deploy machine learning models for virtually any use case. They expect to reduce onboarding time by approximately 70% and improving developer productivity by over 30%.
Orbit Irrigation is a manufacturer and supplier of home and commercial irrigation systems. To resolve customers’ questions, their agents spend two to three minutes per interaction searching through several different sources of knowledge, including Orbit product pages, customer account pages, and internal knowledge forums. This multistep process adds time to the interactions for agents and customers. The new responses automatically generated at each turn of the customer conversation by Amazon Q in Connect. These responses are tailored based on their own knowledge base articles. This will create 10% – 15% time savings on every contact, and the increased number of calls handled every hour is expected to translate directly into costs savings for Orbit – all done with improved customer sentiment.
Fabrice: Looking ahead, how do you see generative AI shaping the business landscape in the next 2–3 years?
Shu Sia: We discussed the market trend earlier where AI is shifting from experiments to production. And our data shows that Agentic AI is emerging as the next major wave of innovation. This isn’t just about single-purpose AI anymore; it’s about building intelligent systems that can handle complex workflows.
In customer service alone, agentic AI is driving 14% faster resolution rates while reducing handle time by 9%. Given these results, it’s no surprise that Gartner predicts agentic AI adoption will grow from 1% to 33% of enterprise apps by 2028. The question isn’t whether to implement anymore – it’s how to implement effectively.
