Amazon Q Expands AI Capabilities for Developers and Business Users

Amazon Q unlocks new generative AI capabilities for business users - SiliconANGLE

Amazon Q unlocks new generative AI capabilities for business users Amazon Web Services Inc. continues to expand the reach of its generative artificial intelligence assistant Amazon Q by bringing it to more applications for business users. At the company's re:Invent 2024 conference today, AWS announced a new capability for Amazon Q in QuickSight that will help business users perform lengthy scenario analysis to find answers to complex problems quickly. Amazon also announced that Q Developer is now available in SageMaker Canvas, a tool for building large-scale machine learning and artificial intelligence models, using a visual interface without needing code or expertise. AWS launched Amazon Q earlier this year for developers and business users to act as a generative AI assistant that can help with coding and business tasks. It consists of multiple formats including Amazon Q Developer, which works alongside developers and IT professionals where it acts as a coding assistant within code editors. Business users get access to Amazon Q Business, which can securely use an organization's enterprise data, helping employees stay prepared and productive. QuickSight is a cloud-based business intelligence tool that allows business users to analyze data, create visualizations and share insights. It supports various data sources, including databases, data warehouses, software-as-a-service applications and files. Users can create dashboards, reports and charts to visualize data. "The convergence of business intelligence and generative AI with Amazon Q will continue to unlock new possibilities for our customers, but as these models became more and more powerful, we know we could do more to accelerate data-driven decision-making," said Dr. Swami Sivasubramanian, vice president of AI and data at AWS. "Today many business users are faced with questions that cannot be answered by a simple Q&A on their data." Analysis tasks often lead to complex approaches to develop charts or visualizations from data sources, which can be a laborious amount of knowledge work. This new Amazon Q capability can take in complex questions and analyze multiple data sources simultaneously to suggest an analytical approach to address a business goal. For example, a business user could ask the AI assistant, "How can I help our store perform as well as the flagship store in Phoenix, AZ?" Using an agent-based approach, Amazon Q would then automatically analyze the data, present results complete with visualizations in QuickSight and suggest actions. It would do so across the software's canvas, which would enable the user to make adjustments to the plan, explore different approaches and adapt their ideas. According to Sivasubramanian, using Q's new capabilities in QuickSight business users can perform complex analysis ten times faster than using spreadsheets. Each step of the way, Amazon Q remains on hand with a conversational interface allowing the user to ask more questions and provide answers about the analysis. If the answer changes the projections or visualizations, the assistant can update the canvas and act as a collaborator alongside the business user to help them get where they're going faster. Amazon also announced that Q Developer is coming to SageMaker Canvas, a no-code machine learning model-building platform that will make collaborating on building, customizing and deploying new models easier for less technical users. Through bringing Q Developer to SageMaker Canvas business users with expert knowledge in their particular industry, can quickly build accurate, production-quality machine learning models using natural language interactions. Q Developer guides users through a conversational interface by breaking down business problems and data using step-by-step guidance for building custom machine-learning models using SageMaker Canvas. It will also clean their data to fix anomalies, build and evaluate their models to recommend the best one to fit their goals and guide them through a workflow. For example, a user could ask Q, "I want to build a model that will help me predict the number of passengers that will take rideshares across certain days given historical patterns of past usage, weather data, pricing, holidays and events." Q Developer would then take that plan and analyze the given data to build multiple models to provide an approach.

AWS expands Q Business gen AI assistant features and integrates with its business intelligence platform - SiliconANGLE

AWS expands Q Business gen AI assistant features and integrates with its business intelligence platform Amazon Web Services Inc. is using its annual re:Invent conference in Las Vegas today to broaden the scope of its Q Business generative artificial intelligence assistant with new features and integration with its QuickSight business intelligence tool. Q Business, which AWS introduced at last year's re:Invent, can answer questions, provide summaries, generate content and perform tasks based on an organization's data. It integrates with over 40 enterprise data sources - such as Microsoft Corp.'s Microsoft 365, Amazon's S3 Storage, Google LLC's Drive, Salesforce Inc.'s customer relationship management suite and Asana Inc.'s workflow project management product - to provide conversational search and answer retrieval that spans all of an organization's data. Q Business delivers contextually relevant answers that factor in company policies, organization and structure. The assistant creates an index that serves as a canonical - or definitive- source of content and data across an organization. It also maintains the index and applies security controls that comply with existing user-level access permissions. Integration with QuickSight allows users to tap Q Business capabilities from within the BI platform to get answers that include visuals like charts and graphs. Q Business and Q in QuickSight now work from the same index of enterprise data. In addition to third-party applications, users can now access data contained in documents, emails, data lakes and other unstructured sources within the business and combine it with data from business applications. For example, Amazon said people can now use Q Business or Q in QuickSight to generate a monthly business review that combines information from emails and help desk tickets with bar charts and other visuals from QuickSight showing usage metrics, trends and outliers. Customers can now expand and enhance quality of Q Business responses by granting independent software vendors access to data from multiple applications via a single application program interface. The result is more personal experiences with greater context while enabling organizations to retain full control of their data. AWS can manage a single index on their behalf to eliminate the need for each application to make a copy. Extended third-party integration allows users on a videoconference, for example, to use Zoom Communications Inc.'s AI Companion to transcribe and summarize the meeting while Q index retrieves relevant documents from places like Google Docs, Slack or Microsoft Outlook emails. Documents are only visible to users who already have access to them. Q users can now access a library more than 50 actions covering Amazon and third-party applications for tasks like processing invoices, managing customer support tickets and onboarding new employees. Another new capability due next year will use generative AI to discover and automate complex workflows without programming. That means a business user can choose to describe a workflow using natural language, upload a document detailing a process or use a browser plugin to capture on-screen interactions. Q Business uses a series of agents to create, edit and maintain the workflow. Workflows can be configured to run at set intervals or triggered by specific requests. QuickSight integration, the cross-application index and new actions are generally available today. The ability to access QuickSight data from Q Business is in preview.

Amazon's Q Business AI agent gets smarter | TechCrunch

A year ago, AWS announced Q, its AI assistant platform for business users and developers. Q Developer is getting a wide range of updates today and so is Q Business. The focus for Q Business is on new integrations that can help businesses bring in more data from third-party tools, the ability for third-party platforms to integrate Q into their own services, and new actions that will allow Q to perform tasks on behalf of its users across third-party applications like Google Workspace, Microsoft 365 and Smartsheet, among others. Previously, Q was already able to pull in data from about 40 enterprise tools ranging from data stores like Amazon's own S3 to services like Google Drive, SharePoint, Zendesk, Box and Jira. Q then creates a canonical index of all of this data (keeping access permissions and other settings intact). The idea now is to expand the types of data the service can index and then use that to provide ever more personalized results. This index, after all, is at the core of Q's capabilities. Now, business will be able to take the data they have stored in databases, data warehouses, and data lakes and combine it with the rest of their business data, be that documents, wikis or emails -- and they can now do so in QuickSight, AWS' business intelligence service. Amazon Q in QuickSight, the company says, will allow employees to query this data and quickly generate charts and graphs with the help of Q (or augment existing charts with content from a wider variety of sources). These new features are now in preview. The feature that is maybe the most interesting from a business perspective is that third-party services like Zoom, Asana, Miro, PagerDuty, Smartsheet and others will now be able to integrate Amazon Q Business into their own services. These services will get access to an API that will allow their generative AI-powered experiences to access the same index that is also used by Q. Asana, for example, is integrating Q Business and Asana AI to help its customers find information from other third-party applications (that are indexed by Q) without having to leave Asana. And from there, they can then also kick off Q workflows and take actions in these third-party tools as well. Similary, Zoom will use the Q index to enhance its own AI assistant so that, for example, the Zoom AI Companion can transcribe and summarize a meeting while Q looks for relevant documents, email or wiki entries related to the call. AWS stresses that all of these features will only surface information that the users have permission to access. In this context, it is worth noting that others, including Atlassian's Rovo, also heavily focus on third-party data integrations (Rovo offers about 80 or so connectors at this point). For many of them, including Atlassian, the idea is to keep users on their own platforms, though, not to have third-party services integrate their assistants and indexes. That's an interesting play on AWS' part. The dream of productivity nerds has long been to automate more of the repetitive but hard to automate processes that are part and parcel of running a business. With this update, Q Business will now feature a library of more than 50 actions that Q can perform for them, but more importantly, AWS is going beyond the workflow automation tools it already offered with Q. The service now uses generative AI so that users can simply describe a workflow using natural language or upload a document that describes a given process. They can also use a browser plugin to let Q capture how they perform an action step-by-step. Q Business then creates the agents that can perform and maintain this workflow. These workflows can run at specific intervals or triggered by specific actions. The market for workflow automation is getting crowded with startups and incumbents like UIPath and Microsoft's Power Automate. But it seems like the advent of generative AI may finally allow some of these products to live up to the promises of what was once called 'robotic process automation.' Those systems were often too brittle in real-world usage, but generative AI now allows for a bit more flexibility in how these tools interact with third-party platforms.

Transforming how business works: Dilip Kumar on Q for Business, gen AI and AWS' vision for productivity - SiliconANGLE

Transforming how business works: Dilip Kumar on Q for Business, gen AI and AWS' vision for productivity Generative artificial intelligence is ushering in a new era of productivity, transforming how businesses access, analyze and act on their data. At the forefront of this revolution is Dilip Kumar, head of Q for Business at Amazon Web Services Inc. In an exclusive conversation at AWS headquarters in Seattle, Dilip revealed how Q for Business empowers organizations to streamline workflows, harmonize data and unlock untapped potential through generative AI. "Businesses have treasure troves of data, but they're often siloed and hard to access," Dilip explained. "Q App Studio and for Q Business bridges that gap, enabling individuals and teams to work smarter, faster and more collaboratively." AWS Q for Business is a solution for modern challenges, where business users often face roadblocks in accessing data and creating actionable insights. Dilip highlighted how Q for Business is transforming workflows across organizations by: Generative AI is reshaping the dynamics between IT and business teams, fostering collaboration rather than friction. Historically, IT was tasked with provisioning technology for business users. Now, business users are leveraging tools like Q for Business to directly drive value. "The business people are the new IT, Business users are no longer just consuming technology -- they're using it to be productive and to serve their organizations better," Dilip explained. This shift is creating a more harmonious workplace, where technology eliminates mundane tasks and enables employees to focus on strategic goals. At the heart of Q for Business is Q Apps, a low-code solution that empowers users to create and automate tasks with ease. "Q Apps allow users to automate repetitive workflows and share them across teams," Dilip said. "It's a simple, intuitive process that transforms how businesses operate." Popular use cases for Q Apps include: One of the most significant challenges enterprises face is integrating data across multiple systems and formats. Q for Business addresses this by harmonizing structured, semi-structured, and unstructured data into a unified, actionable platform. "Q for Business doesn't just deliver search results -- it completes the discovery loop," Dilip said. "It provides context, memory, and actionable insights that drive workflows forward." This capability transforms enterprise search into enterprise action, enabling users to go beyond finding answers to solving problems. As businesses adopt generative AI, one value proposition stands out: productivity. From automating workflows to reducing friction between teams, Q for Business is delivering step-function improvements in efficiency. "When you remove undifferentiated heavy lifting, you give people the freedom to focus on meaningful work," Dilip said. "It's incredibly liberating and harmonizing." This is especially evident in how generative AI transforms interactions between developers and business users. Friction is replaced by collaboration, with AI enabling each group to focus on their strengths. Implementing Q for Business is designed to be simple and accessible: "You can start with basic search and quickly progress to building apps that automate and scale workflows across your organization," Dilip explained. Q for Business represents the first step toward more advanced agentic systems -- AI tools that can autonomously decompose complex queries, execute tasks, and deliver orchestrated solutions. "The key is orchestration," Dilip said. "It's about breaking down problems into manageable pieces and composing solutions that work seamlessly for the user." As AWS continues to innovate with Q for Business, the potential for generative AI to transform industries is clear. From harmonizing data to automating workflows, Q for Business is not just a tool -- it's a catalyst for change. "We're in a golden age of productivity," Dilip concluded. "The combination of generative AI, data harmonization and actionable insights is redefining what's possible in the workplace."

AWS enhances Q Developer AI assistant to reduce tedium, accelerate work - SiliconANGLE

AWS enhances Q Developer AI assistant to reduce tedium, accelerate work To make the lives of developers easier and reduce their workloads, Amazon Web Services Inc. released several updates to the company's artificial intelligence software development assistant, Amazon Q Developer. New capabilities announced today at AWS re:Invent included AI agents to automate unit testing, writing documentation and producing code reviews to assist developers in building software faster throughout the entire development lifecycle. Q Developer will also receive the capability to assist with operational issues by assisting with investigating and fixing issues. The AI assistant is also gaining capabilities to autonomously help with the heavy lifting during application migration projects. Modernizing applications and operating systems can be time-consuming and labor-intensive, which involves code analysis, documentation, planning and major changes to entire systems taking time and energy away from development. "Amazon Q Developer is fundamentally transforming how developers work and can speed up a variety of software development tasks by up to 80%, providing the highest reported code acceptance rate of any coding assistant that suggests multi-line code, code security scanning that outperforms leading publicly benchmarkable tools, and high-performing AI agents that autonomously reason and iterate to achieve complex goals," said Deepak Singh, vice president of next-generation developer experience at AWS. Making reliable code depends on having solid tests that can catch potential issues early. However, writing tests is a labor-intensive process that involves going back over already written code. Amazon said generating unit tests for code is now as simple as using the generative AI assistant to produce it after they complete their code segments so they can move on and focus on coding instead of preparing tests. Developers spend a certain amount of time "proofreading" each other's code by reviewing it to make sure that it fits standards for quality, style and security. After preparing a code revision or functionality, a developer can wait hours or days for another developer to become available to review their code and then wait more time for feedback on a revision. To help break this cycle, Q Developer now helps automate the detection of code-quality issues earlier to help save developers time on future reviews. With the assistant running, developers get feedback sooner about their code standards when they need it, helping them maintain better code quality based on best practices. The assistant can supply recommendations on how to fix the code to maintain standards after every line of code and with every merge request. This helps produce better code before peer review and reduces the number of rollbacks or revisions. Q Developer now automatically keeps up with documenting code, something that often breaks developers out of the flow of producing code. After writing and preparing code, developers often have to stop and explain how it works. However, the longer a project goes on, the more complex it gets and the more pieces are interconnected. Now, Q can update disparate pieces of documentation so that they remain up-to-date without the developer having to hunt them down and correct them. With correct and understandable documentation, updated as coders go about their work, this means that it becomes easier to understand code when scanning it for the first time or refreshing knowledge of it. Q Developer presents its proposed changes to documentation so developers can see that updates are accurate as they go, allowing them to control the maintenance of documentation. After all the code writing is completed and an application is in production, problems can still crop up that need to be fixed in code. When issues happen, development and operations teams need to move as quickly as possible to get the application working as it should so that customers can get back to using it as it was intended. Traditionally, if something goes wrong in an application, the operations team investigates the issue and passes along the errors and telemetry to development. The development team then attempts to discover and repeat the issue, or bug, on a virtual server so that the problem can be fixed and pushed out to production. This is a process that can involve sifting through many lines of data and code to determine what went wrong and how to fix it. With access to the codebase and information about the health of the application and monitoring, Q can now quickly sift through hundreds of thousands of data points between services to begin investigating the moment an issue is discovered. The AI assistant can then use this to help form an analysis of a potential root cause for both operations and development to issue guidance on how to fix it. Amazon added that where possible, Q will access routine procedures such as runbooks and, if permitted, will automatically execute them. The potential benefit is that Q Developer will take on the hard, tedious work of the investigation so that operations can get to the cause faster and the development team can address a fix sooner. Many organizations have legacy applications that exist on old hardware or operating systems that need to be moved to more modern systems, but this can be laborious and tedious work. Today, Amazon announced a new capability for Q Developer that will provide transformation capabilities to make these projects easier by autonomously analyzing source code, generating new code, testing it and executing the change for the customer once approved. "We are combining Amazon Q Developer with our nearly two decades of experience helping organizations migrate and modernize their legacy workloads on AWS to accelerate and simplify large-scale transformations," said Mai-Lan Tomsen Bukovec, vice president of technology at AWS. "This is a game-changer for customers and partners looking to move off Windows .NET, VMware and mainframes." Amazon initially integrated the Java transformation capability of Q Developer internally to migrate tens of thousands of production applications from an older version of Java to Java 17. The company said the effort saved more than 4,500 years of development work and $260 million in annual cost savings. The company added that customers using Q can modernize Windows .NET applications to the Linux operating system up to four times faster than traditional methods and reduce licensing costs by up to 40%. Amazon said using the new capability, customers can shift VMware workloads from data centers to AWS faster. Amazon Q's agents can automatically identify dependencies to accomplish what Amazon said can take weeks of manual work to convert on-premises networking configurations into AWS configurations in a matter of hours. Amazon Q can also assist with moving off mainframes, starting with IBM z/OS mainframe systems. Now Amazon partners and customers can collaborate using Q to reduce costs by having an expert assist them with a range of tasks including generating documentation and preparing applications. For example, Amazon Q can work with developers to document thousands of COBOL programs, a truly tedious and nearly impossible task, and prepare their logic and business rules for the move to AWS. The new capabilities to assist with migrating large-scale Windows .NET, VMware and mainframe projects will be available through a new Amazon Q Developer web application. Amazon said this will be designed to help customers collaborate on hundreds of complex transformation projects simultaneously by giving them a place to review them together easily. The VMware and mainframe modernization capabilities are only accessible through the new web interface, while developers can also perform Windows .NET transformations in their development editors.

Revolutionizing software development: Deepak Singh on gen AI, AWS Q Developer and the future of coding - SiliconANGLE

Revolutionizing software development: Deepak Singh on gen AI, AWS Q Developer and the future of coding Generative AI isn't just a technological leap -- it's a transformative force reshaping the way software is developed and deployed. At the forefront of this revolution is Deepak Singh, vice president of the Next Generation Developer Experience at Amazon Web Services Inc. In an exclusive conversation at AWS headquarters ahead of its re:Invent conference next week in Las Vegas, Deepak shared his insights into how generative AI and tools such as AWS Q Developer are empowering developers, streamlining workflows and accelerating innovation across industries. "Using gen AI is going to change the way all of us build software," Deepak remarked. "It's about building faster, being more creative and spending less time on the tasks we don't like doing." The software development landscape is undergoing what Deepak describes as a "before and after moment" akin to the launch of AWS EC2 or serverless computing with Lambda. Gen AI-powered tools such as Q Developer are at the heart of this transformation, enabling developers to address longstanding pain points. Deepak highlighted two groundbreaking features of Q Developer: These tools don't just enhance efficiency -- they fundamentally change the development process by removing bottlenecks and empowering teams to focus on creativity and innovation. Deepak's role centers on reimagining how developers work, blending tools, environments and AI to meet developers where they are. This includes integrating Q Developer into commonly used platforms such as VS Code, IntelliJ and even Slack. Our goal is to eliminate friction. Whether you're coding in an IDE, debugging in Slack, or querying in a shell, Q Developer integrates seamlessly into your workflow. This flexibility extends beyond AWS environments. Recent partnerships with companies such as Wiz and Datadog enable customers to use Q Developer to analyze security issues and operational metrics directly within the AWS console. One of the most striking examples of Q Developer's potential is its role in legacy modernization. Deepak shared how Amazon used Q Developer to upgrade its Java codebase from JDK 8 to JDK 17, saving 4,500 developer-years and reducing costs by $260 million annually. Tasks like version upgrades or legacy modernization, which used to be tedious and time-intensive, can now be handled autonomously. This frees developers to focus on building innovative solutions. This shift reflects a broader trend toward systems thinking, where developers focus on architecture and high-level design while gen AI handles execution. According to Deepak, this mindset will define the next wave of innovation in software development. AWS' gen AI tools aren't just for seasoned developers -- they're democratizing software development for all builders. With tools such as Q Developer, even nondevelopers can create and manage applications. Deepak shared an example of data center technicians using gen AI to troubleshoot HVAC systems: They uploaded their documentation, and with a few prompts, built an app that identified and solved error codes. These technicians weren't developers, but gen AI enabled them to create a solution that might never have been built otherwise. This democratization extends to how Q Developer is accessed. A freemium model allows users to experiment with the tool using a free-tier Builder ID, while enterprises can scale up with advanced features through Pro licenses. The recent redesign of Q Developer includes enhanced reasoning capabilities and a new inline chat feature, which allows users to seamlessly switch between typing and complex prompts within their workflow. These updates reflect AWS' commitment to building goal-seeking agents that adapt to evolving customer needs. This is just the beginning," Deepak said. "As LLMs improve and tools like text editors become more integrated with AI, the possibilities are endless. Beyond coding, gen AI is driving a broader productivity boom across industries. Whether it's enabling faster software development, modernizing legacy systems, or unlocking creativity, the impact is profound. Deepak noted that customers like the BT Group and National Australia Bank are already seeing 37% to 50% acceptance rates for AI-generated code. Gen AI isn't just about doing more -- it's about doing it better," Deepak emphasized. "Whether you're a bank launching new products or a startup scaling quickly, these tools help deliver higher-quality results at unprecedented speed. As AWS prepares for re:Invent, the excitement around Q Developer and gen AI continues to grow. Deepak hinted at upcoming announcements that will expand the tool's capabilities and deepen its integration with third-party platforms. We're just scratching the surface of what's possible. As gen AI evolves, so will Q Developer -- helping builders, developers, and business users achieve their goals faster and more effectively. Deepak Singh's insights highlight how AWS is redefining the developer experience with gen AI, setting the stage for a future where software development is faster, smarter and more inclusive. With tools such as Q Developer, AWS is not only empowering developers but also enabling a new generation of builders to thrive in an AI-driven world. For those ready to embrace this revolution, the message is clear: The future of software development is here, and it's powered by generative AI.

AWS wants Amazon Q to become your buddy for the entire software development lifecycle

At its re:Invent conference, AWS today announced a series of updates to Q Developer, its coding assistant platform that competes with the likes of GitHub Copilot. The focus here is on going beyond code completion and to help developers with a wider range of routine tasks involved in the end-to-end software lifecycle. The service, which you may still remember under its previous name of 'CodeWhisperer,' is part of AWS's overall Amazon Q generative AI platform, which also includes Q Business (and which is also getting a slew of updates today). "What developers need is they want to actually have Q be the buddy to solve some of the undifferentiated heavy lifting so that they can actually have more freedom to innovate," Swaminathan 'Swami' Sivasubramanian, AWS' VP of AI and Data, told me. "So that's why having an assistant -- or buddy -- that helps them do things faster, more streamlined, is such an important thing, and that's why we're focused on it in a big way." Managing the end-to-end software lifecycle Sivasubramanian told me that he believes what differentiates Q Developer from competing platforms is its focus on the entire software development lifecycle. So far that meant helping developers troubleshoot issues and perform multi-step tasks to fix them (or built entirely new apps), as well as scan the code for security vulnerabilities. At this re:Invent, the company is taking this a step further. Q can now, for example, automatically generate unit tests, for example. But what's maybe even more important is that it can now also do the one thing that many developers hate the most: write and maintain the documentation for that code. To complete this cycle, Q can now generate a first code review when developers check in their code. "In Amazon, we have this rule that no code ever gets checked in without a code review," Sivasubramanian said. "So if you don't do a code review, then you cannot check in code. But not many enterprises actually have either enough senior engineers to review or the senior engineer says: 'I can't deal with so many reviews. Can somebody first review it before we do so?' Q we will streamline the code review process by being the first line of reviewer and takes care of the automatically checking code quality, security vulnerabilities and so forth." Then, once the code is in production, a new operations agent for Q can now automatically pull in data from AWS CloudWatch, the company's monitoring service, and immediatly start investigating when an alarm goes off. "It utilizes the [knowlege it has about an] organization's AWS resources and then it sifts through hundreds of data points across various resources sitting in CloudWatch. Then, after analyzing it, Q comes up with potential hypothesis for the root cause and then it guides the users through how to fix it," Sivasubramanian explained. All you wanted for Christmas was help with your Cobol and .NET migrations, right? For those enterprises with older codebases, transitioning to the cloud often involves rewriting a lot of their existing code. One of the earliest differentating features of Amazon Q Developer was its agent for code transformation. At the time, the focus of this agent was to on moderizing older Java apps. Today, the team is expanding this by also helping developer update their older .NET-based applications from Windows to Linux. And while this may at first seem like a curiosity, AWS is also launching an agent for modernizing COBOL mainframe applications. A lot of large enterprises still rely on this old code, after all, which few developers know to work with today. These are very complex migrations, Sivasubramanian stressed, and so the goal here is not to simply translate the existing code 1:1. "Our goal is not to actually just like fully COBOL project in, code out," he said. "The reality is, these projects are inherently extremely complex. You need to have a human in the loop to leverage it, but I've heard customers say, 'Hey, this takes multiple years and customers have explicitly told us this is a game changer and would significantly drop that timeline." Sivasubramanian noted that while there is less COBOL code to train models to automate the code migration, the team was able to leverage AWS' overall experience in modernizing mainframe applications, as well as more traditional methods for code translation. "Taking code from one language to another one arguably is the easy part," he said. "But the harder part is: how do you know you got it right? And how do you even know what the code does? And then the challenge in these [codebases] is they are usually poorly documented and dependencies are not well understood. So what we have built is really extremely innovative, and [the system] also understands, at a project level, what are the objectives of each of the module, and then plans out and creates a migration planning timeline to actually generate the code, and then generate the test -- and bringing humans in the loop to see how you validate it."

Inside the AI revolution: Swami Sivasubramanian on generative AI, agentic systems and AWS' vision - SiliconANGLE

Inside the AI revolution: Swami Sivasubramanian on generative AI, agentic systems and AWS' vision Amazon Web Services Inc. is betting big on artificial intelligence as it scrambles to gain mindshare from perceived leaders such as Microsoft Corp., OpenAI and Google LLC, and AWS Vice President of AI and Data Swami Sivasubramanian is the key executive driving those effort. In an exclusive video podcast conversation with Sivasubramanian, who was one of the pioneering engineers behind DynamoDB and RDS, we dove deep into the transformative power of generative AI, agentic systems and the critical role of data in shaping the future of enterprise applications. As we approach AWS re:Invent next week, this discussion offers unique insights into the innovations redefining productivity, data analytics and AI-driven business processes. Swami highlights how innovations such as the enterprise AI agent Q and gen AI app creation platform Bedrock are not only saving Amazon developers thousands of hours but also empowering business users to achieve "10X productivity" by simplifying data access and automating workflows. By democratizing data for nontechnical users and integrating diverse datasets, AWS is enabling enterprises to make faster, smarter decisions. Swami underscores that productivity is the true "killer app" of generative AI, setting the stage for a new era of efficiency and innovation across industries. Swami reflects on the journey that brought us to this moment in AI history: We are having this moment because a lot of things came together -- deep learning neural nets from 30 years ago, transformers unlocking unsupervised learning, and the cloud enabling seamless storage and computation. This convergence, he notes, created the foundation for large language models, which are now transforming industries by learning from massive datasets. But 2023, he says, was just the starting line: 2023 was about proof of concepts. In 2024, it's about real ROI -- deploying gen AI systems and agents that save money or increase revenue. A defining example of AWS' innovation in action is Q, an AI-powered agent that Swami uses personally: Q transformed how we upgraded Java packages. It analyzed dependencies, suggested changes, and automated 90% of code reviews. This saved 4,500 developer-years and $250 million in capex. These savings go beyond coding. Enterprises like Pfizer and Toyota have leveraged Q to streamline processes, saving billions. Swami emphasizes that the real power of gen AI lies in mapping business problems to the right data and leveraging cutting-edge LLMs: The efficiency of productivity is going to be 10X. Hundreds of thousands of customers are already realizing this with AWS. One of the most exciting shifts Swami highlights is the democratization of data for business users. Traditionally, technical expertise was a bottleneck in unlocking insights. Now, tools like Q and QuickSight are enabling business professionals to harness the power of data without needing a Ph.D. in SQL: With Q, you can ask for insights in natural language and get dashboards or data stories in minutes. Tasks that used to take weeks now happen in seconds. This shift, he explains, is redefining productivity: Cloud made developers 10X productive by eliminating undifferentiated heavy lifting. GenAI is now doing the same for business users. Swami predicts a seismic shift as enterprises move beyond unstructured data (text, images) to integrate multi-modal datasets: Imagine querying relational databases, streaming real-time data, and blending this with unstructured data. That's the future. He cites BrainBox AI's ARIA assistant, which uses building schematics and energy consumption data to optimize efficiency: ARIA identifies patterns like increased energy use on a specific floor and provides actionable recommendations. This used to take months -- it now happens in minutes. This ability to fuse diverse data types opens the door to unprecedented innovation across industries -- from transportation to marketing to supply chain optimization. As generative AI becomes a critical business tool, concerns around security and accuracy grow. Swami underscores the importance of resilience in AI applications: Contextual grounding is key. Guardrails ensure AI responses are accurate, secure, and actionable. At AWS, we're investing heavily in these areas. He envisions a future where AI systems seamlessly integrate with enterprise data, offering not just accuracy but confidence: The ability to map data to LLM responses and refine contextual grounding will be a game-changer. Echoing AWS Chief Executive Matt Garman's sentiment, Swami reaffirms that productivity is the killer app for gen AI: From automating mundane tasks to enabling strategic decision-making, gen AI is empowering enterprises to achieve more with less effort. The result? A shift in the labor equation: Developers focus on innovation, not debugging. Business users gain instant insights without needing technical expertise. And enterprises achieve a step-function increase in efficiency. The cloud has changed the equation and made developers like 10X productive so what is happening with the gen AI world, the business users are now actually 10X productive. Our conversation with Swami highlights the pivotal moment in technology and business history. As AWS continues to invest and drive more viable generative AI, the opportunities for transformation are limitless. From streamlining code upgrades to revolutionizing enterprise processes, the innovations discussed here signal a new era where data, AI, and cloud infrastructure converge to unlock unprecedented value. This is just the beginning. The ability to turn months into minutes will redefine how we work, innovate, and solve problems. As AWS continues to shape the future of generative AI, the challenge will be balancing innovation with resilience -- ensuring these systems remain reliable, secure and transformative for businesses worldwide.