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The transformative potential of generative AI is reshaping business strategies, yet many organizations are struggling to turn pilot projects into production use cases that drive measurable business results. A key barrier is legacy IT infrastructure and data management capabilities, highlighting the urgent need for cloud modernization.
According to a McKinsey study, generative AI will drive significant productivity gains with the most significant impacts to the banking, high tech, and life sciences sectors. With generative AI expected to automate half of existing work activities between 2030 and 2060, McKinsey is projecting the technology to contribute between $2.6 trillion to $4.4 trillion annually to the global economy. McKinsey says there are additional productivity gains to be realized if generative AI is integrated in software and workflows beyond the 63 use cases analyzed in its research.
While there is plenty of upside and early optimism, generative AI adoption trends highlight the stark reality that activity is still mostly aimed at early experimentation. Gartner research showed the number of generative AI proofs of concept (PoCs) rising from 15% to 45% between April and October 2024. However, production generative AI use cases only increased from 4% to 10% in that same timeframe.
What are the barriers to production-scale generative AI implementations? An AWS IQ survey found at least 30% of generative AI projects will be abandoned after POC by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. More specifically:
The path to success starts with modernization
A robust data foundation. As the saying goes, “garbage in, garbage out,” and that is especially true with generative AI initiatives. To realize generative AI’s full potential, organizations must create a high-quality data pipeline to support the customization and training of models. A proper data foundation must encompass scalable, cloud-based data management infrastructure; comprehensive data services, including vector databases to store and process data across various formats; robust data governance and security; and advanced analytics, RAG (retrieval-augmented generation), and industry-specific services for building generative AI applications and experiences.
Modern infrastructure. The sheer volume of data required for generative AI large language models demands high performance compute, storage, and network infrastructure to run efficient training and inference workloads. Price-performant accelerator compute technologies like the latest GPUs and dedicated machine learning silicon are critical for powering generative AI workloads. High-performance, low-latency cloud storage combined with fast data transfer capabilities ensure high utilization. Advanced computing technologies such as exascale computing and next-generation virtualization technology enable faster innovation and optimal price/performance.
Development tools and applications. Services like Amazon Bedrock aid in building custom generative AI applications using various foundation models while Amazon SageMaker helps build, train, and deploy models at scale. Amazon Q serves as a generative AI-powered assistant for accelerating software development and boosting productivity. AWS partners expand the mix with an array of generative AI-enabled solutions as well as vertical industry offerings.
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Mastering generative AI: AWS and partners offer recipe for success
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The bottom line
To unlock generative AI’s full potential, organizations must embrace a strategic approach to cloud and data management modernization. With the right mix of scalable infrastructure, seamless integration, built-in security, and expert guidance, AWS and its partners lead organizations on the journey from generative AI experimentation to full-throttled implementations that deliver for the business.
• Immature data strategies and poor data quality limit the utility of generative AI. The technology is wholly dependent on the ability to effectively tap into structured and unstructured data, most of which is currently housed in dispersed data silos and diverse formats.
• Soaring deployment costs that Gartner estimates can cost organizations anywhere between $5 million to $20 million.
• Security and data privacy concerns given the size and scope of many models.
• Performance and latency issues due to varying model types, usage patterns, and large-scale data sets which can create performance bottlenecks.
• The evolving regulatory landscape raising concerns about data privacy, bias, lack of transparency in outputs, and legal risks.
Organizations enjoying the most success with generative AI implementations are focusing on three critical technology pillars as they accelerate AWS cloud modernization and propel enterprise use of generative AI.
They include:
AWS partners help advance generative AI development in many ways. The range of resources and services span every phase of generative AI development, including access to many of the leading foundation models through a single platform. Enterprise-grade security and privacy are at the core of all offerings. AWS partners provide flexibility and choice, delivering a range of products, services, and technologies that address general-purpose and industry-specific needs.