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From the boardroom to the frontline workforce and beyond, the potential and transformative power of generative AI looms large. Yet for all the enthusiasm and pace of experimentation, an estimated a sizeable number of enterprises are struggling to make the leap from pilot to production. The key roadblock: IT, data, and application infrastructure in need of serious cloud modernization to successfully execute AI priorities. There is no singular route to GenAI success. However, data—and more significantly, a data management backbone—are perhaps the biggest differentiators for effectively turning GenAI projects into substantive business outcomes. Simply put, organizations need to modernize infrastructure and applications on a proven cloud platform from which they can seamlessly collect, manage, and scale data while unleashing the power of GenAI-enabled workflows.

Turning potential into reality

GenAI continues to dominate corporate strategy discussions. The technology offers significant productivity potential across most industries; banking, high tech, and life sciences expect the biggest boosts in revenue generation, according to a recent McKinsey study. GenAI is elevating customer interactions, generating personalized content for marketing and sales campaigns, and assisting with the labor-intensive work associated with writing software code and other technology-oriented tasks. McKinsey estimates that half of today’s work activities could be automated between 2030 and 2060 using GenAI—a scenario it says could boost labor productivity growth by 0.1 to 0.6 percent annually through 2040. Based on an analysis of 63 use cases, the McKinsey study projects GenAI will add between $2.6 trillion to $4.4 trillion annually to the global economy. What’s more, those numbers could roughly double if GenAI is embedded into software currently used for tasks beyond the initial 63 use cases, McKinsey found. Given the breadth of possibilities for disruptive innovation, it’s no wonder GenAI has become a strategic enterprise imperative. Among the top use cases on the corporate agenda:

Infrastructure and data remain key barriers to GenAI adoption

There’s been a flurry of activity surrounding proof of concept (PoC) pilots and individual experimentation since GenAI’s meteoric rise. But organizations have been slower to move on production applications and enterprise use cases. Many customers simply aren’t ready to traverse the PoC to production chasm. While the number of organizations experimenting with GenAI PoCs rose from 15% to 45% between April through October of last year, there was far less ground gained for production GenAI use cases, according to Gartner research. Specifically, Gartner found the number of firms successfully transitioning GenAI pilot projects into production only increased from 4% to 10% over the same time. One of the biggest factors curtailing GenAI use is immature and ineffective data strategies. An AWS IQ survey found at least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. Those successfully building and deploying GenAI applications with real business value are making use of their own data, underscoring the importance of mature enterprise data strategies. These firms are tapping into enterprise data stores to customize GenAI results, making them relevant for specific business requirements and outcomes. To do so requires a modern, cloud-enabled platform on which to collect, manage, and scale all this data, bolstered by a robust ecosystem of related tools and services.

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Data readiness for generative AI

“To get real value out of foundational models, companies need to feed them with their own data, which varies depending on the use case,” says Amit Singh, Global Head of GTM, Generative AI and Machine Learning Partners at AWS. “Those companies that have invested over the years in a robust data strategy and a solid data quality and provenance foundation are further ahead in terms of adopting GenAI.”

A modern, cloud-based AWS ecosystem paves the way

1. Enhancing customer experience/retention: Chatbots, virtual assistants, AI-powered contact centers, and personalization 2. Fueling growth initiatives: Gen AI-enabled marketing, customer service delivery, enhanced product prototyping, and development 3. Improving business operations: Cybersecurity, process optimization, document processing, and data augmentation 4. Aiding creativity and content creation: Automating and elevating writing, media, design, and modeling applications

Many current GenAI implementations are producing lacklustre results. Enterprises that may have made the leap to modern cloud infrastructure still lack a solid data foundation, which puts them at a disadvantage. Others are struggling because they haven’t yet executed a cloud migration and modernization strategy, opting to maintain a significant portion of their data and application estate on premise. This leaves GenAI efforts disconnected from the scalable resources of a modern cloud platform and ecosystem—a true recipe for failure. By modernizing with AWS, companies can seamlessly integrate their existing applications and data with new AI services from both AWS and its broader partner network. They can also tap into infrastructure primed to easily scale next-generation GenAI applications. A GenAI-ready modern foundation needs to encompass the following:

• Robust data foundation: A comprehensive set of database services, data integration tools, data governance, data warehouse, advanced analytics, and Retrieval Augmented Generation (RAG) capabilities are essential for building secure GenAI applications. • Compute, storage, and network infrastructure: Organizations need cost-effective infrastructure that meets performance, sustainability, and ease of use requirements. This includes price-performant accelerated computing, high-performance cloud storage, and fast data transfer capabilities. Advanced computing technologies like exascale computing and next-generation virtualization will enable faster innovation and optimal price/performance. • Builder tools and applications: Organizations need tools and services to fuel GenAI-enabled application development. Services like Amazon Bedrock, Amazon SageMaker, and Amazon Q assist in building, training, and deploying custom GenAI applications at scale. AWS partners also provide access to a variety of GenAI-enabled solutions for general business use and industry-specific needs.

AWS partners offer several other advantages to help enterprises advance their GenAI development journey while avoiding the usual twists and turns along the way. They include:

• Flexibility and choice. AWS partners have resources spanning every phase and function for GenAI development. The AWS partner portfolio is leveraging GenAI in their own offerings, from tools focused on marketing and sales use cases to those oriented to customer support functions. • A robust partner network: The AWS partner network is 150,000-strong, giving customers the ability to select from collaborators that fit their exact needs. Partners offer a range of products, services, and technologies, including specialized consulting services, domain expertise.

With the AI/GenAI era upon us, companies sit at the crossroads of an unprecedented opportunity to drive innovation and transform how work is done. AWS and AWS partners deliver access to all the tools and resources required to turn GenAI experimentation into full-blown competitive advantage.

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