Fuel Generative AI Success: Building Robust Data Foundation with AWS Partners

By Sam Anand, WW Partner Lead, Data Foundations for AI – AWS
By Sudhir Gupta, WW Tech Lead, Data & AI – AWS
By Vishal Sharma, WW Tech Lead, Data & AI – AWS
By Martin Pomykala, WW Partner Lead, Databases – AWS

As the capabilities of generative AI models continue to advance rapidly, organizations are eager to use this transformative technology to create new products, services, and experiences. However, realizing the full potential of generative AI is accelerated by having a robust data foundation in place — one that can power the customization and training of these models with high-quality data.

This blog post highlights the value of building flexible, scalable data infrastructure, and data governance frameworks that can ingest and connect diverse data sources including third-party datasets, and support the iterative model refinement process central to generative AI development. By getting these data fundamentals right, companies can position themselves for lasting success in generative AI. This could involve creating hyper-personalized content, automating complex tasks, or reinventing customer experiences. AWS Specialization Partners bring deep expertise in AWS databases, analytics, and AI/ML services, designing custom solutions that can transform your data into generative experiences.

“We have seen that customers are the happiest with the outcomes of their Generative AI initiatives when they do a great job of tailoring the experience with their data. Our customers who were already modernized on AWS, and were able to move really fast to create differentiated experiences for their employees and customers with Generative AI, are even happier. Migrating and modernizing applications and data on cloud is a foundational step organizations can take to create the best possible outcomes with Generative AI. This is why we are excited to welcome this cohort of Partners, who have the experience, and demonstrated success, doing this across hundreds and thousands of organizations” says Tim Finley, Worldwide Director, Data Foundations for AI at AWS.

Importance of data for generative AI

Data is the foundational element that enables successful generative AI solutions. With everyone having access to the same foundation models, your data is the differentiator between a generic application and a generative AI application that knows your business and your customers. Many customers experimenting with generative AI often overlook the critical role of data. As a result, they get stuck in the proof-of-concept (POC) phase or face scaling challenges due to inadequate data preparation and management.

To scale successful generative AI outcomes, data must be prioritized as an integral part of the strategy, solution development, implementation, and ongoing operations. Organizations need to ensure they have the right data foundation in place. This includes:

• Identifying and aggregating relevant data sources, both internal and external
• Cleaning, organizing, and labeling the data to ensure high quality and consistency
• Implementing data governance and security measures
• Continuously monitoring and updating the data to keep it fresh and relevant

Without a strong data foundation, generative AI solutions will be limited in their capabilities and unable to reach their full potential. Customers may struggle with issues like poor output quality, lack of diversity, and an inability to scale.

Key challenges customers face with data and generative AI

Here are some of the key data-related challenges customers face when scaling generative AI solutions:

• Data Quality and Governance: Ensuring the training data used to customize and develop generative AI models is of high quality is a significant challenge. Issues like data inaccuracies and inconsistencies can lead to unreliable and biased outputs from the generative AI models.
• Data Quantity and Diversity: Obtaining sufficient quantities of high-quality training data can be a hurdle, particularly for niche or emerging use cases. Ensuring the training data represents diverse perspectives and real-world scenarios is essential to improve the inclusivity and generalization of generative AI models.
• Data Integration and Preprocessing: The fragmentation of data across legacy and modern systems makes it difficult to use generative AI effectively. Developing robust data cleaning, transformation, and feature engineering pipelines is essential to optimize the data for model performance.
• Data Versioning and Reproducibility: Implementing efficient data versioning and lineage tracking mechanisms is crucial to ensure the reproducibility of generative AI model training and outputs.
• Data Security and Privacy: Safeguarding sensitive or personal data used in generative AI training and deployment is a significant concern, especially in regulated industries.

A recent Gartner study predicts that at least 30% of generative AI 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. Most organizations lack a solid, centralized data foundation to effectively use generative AI across their enterprise. Establishing the right data infrastructure, data governance, and data quality processes is critical for successful generative AI production deployments.

Data foundation on AWS

A data foundation on AWS can help address the challenges customers face with data and generative AI. AWS offers the comprehensive capabilities customers require to kickstart their generative AI initiatives by leveraging their proprietary data and establish a resilient data infrastructure that can sustain their long-term generative AI aspirations.

AWS provides the most extensive suite of data services, including vector databases, to store and process all your data across various formats. Customers can quickly and easily connect to and act upon all of their data, no matter where it resides, with integrated data services and zero-ETL features. Customers can scale their data and generative AI workloads to meet performance requirements. Additionally, customers can implement robust end-to-end governance over their data — overseeing storage, access, and permissible actions in the data workflows.

By extending the proven data foundation capabilities, customers can rapidly innovate with accurate, trustworthy, and compliant AI applications, unlock new insights, and drive innovation across their operations, all while maintaining data integrity and compliance.

Figure 1: Data foundation on AWS

How AWS Partners can help 

Partners bring their specialized expertise in AWS services, proven methodologies, and pre-built accelerators to rapidly implement and deploy secure, scalable data and generative AI applications. Working with a validated partner reduces risk, accelerates time-to-value, and provides ongoing support and innovation tied to the latest AWS capabilities. Customers also benefit from the partner ecosystem, gaining access to complementary services, integrations, and the deep collaboration between the partner and AWS engineering teams. By tapping into the expertise of AWS Specialization Partners, organizations can confidently build their end-to-end data foundation and harness the power of generative AI to drive innovation and business impact.

AWS Partners have developed a comprehensive suite of solutions and offerings to support customers at every stage of their data and generative AI journey – from initial assessments and data strategy definition to rapid prototyping, scalable deployment, and ongoing optimization.

For example, Brainbox AI, an energy optimization company for commercial real estate, worked with Caylent, an AWS Partner, to develop ARIA. ARIA is an AWS-powered natural language interface that enhances users’ access to insights and enables conversational data analysis and command HVAC and other building systems.

“Our reputation as pioneers in autonomous AI solutions for the built environment is rooted in our ongoing pursuit of innovation and pushing boundaries. The pathway to our generative AI innovation was made possible by partnering with Caylent and using industry-leading models including Anthropic’s Claude on Amazon Bedrock which enabled the creation of the world’s first virtual building assistant. This industry-defining technology, together with our AI for HVAC solution will have momentous impact on building operations management, reducing HVAC energy costs by up to 25% and greenhouse gas emissions by up to 40%” says Jean-Simon Venne, CTO & Co-Founder at BrainBox AI.

Similarly, as the pioneers of direct sourcing, TalentNet’s talent acquisition platform has helped the world’s leading businesses revolutionize how they attract, engage, and power their workforce. By working with Quantiphi and utilizing the DataPathway Accelerator (QDAP), TalentNet adopted a data-first approach that significantly accelerated their Gen AI initiatives by 60%. This strategy reduced data preparation time from months to weeks, enabling faster implementation of AI solutions, improving efficiency and better decision-making, and ultimately driving substantial ROI through enhanced recruitment outcomes and operational cost savings.

“Quantiphi enabled us to effectively manage and consolidate our talent data while integrating an AI layer into our platform. This transformation has made data-driven decisions central to our customers’ talent acquisition processes and overall hiring strategies, allowing us to unlock new efficiencies and insights that significantly enhance our business outcomes” says Shawn Duggan, VP of Application Development and Platform Engineering at TalentNet.

Mantel Group’s AWS Data Capability Leader, Wade Weirman remarks “The Data Foundation for Generative AI partner initiative, and AWS’s comprehensive set of native data services and support for partner data platforms such as Databricks and Snowflake, has enabled Mantel Group to build more robust data infrastructure that support advanced AI applications. This collaboration has resulted in better outcomes for our customers, allowing them to harness the power of their data more effectively thereby leading to improved decision-making, innovation, and growth.”

We are excited to announce the first cohort of Data Foundation Partners who can help customers build end-to-end data foundations and AI solutions on AWS.

Figure 2: AWS Data Foundation Partners

Conclusion

The data foundation for generative AI is designed to empower customers with enterprise-grade security, privacy, and generative AI capabilities. It is recommended to take a data-centric approach, paired with high-performance and cost-effective infrastructure, for scaling generative AI applications.

By working with validated AWS Partners, customers can unlock the full potential of generative AI, reduce time to market, and seamlessly integrate transformative technologies into their operations.

Discover the partner-built solutions and connect with our Data Foundation Partners today. Take the first step towards realizing your generative AI vision and deliver innovative solutions that delight your end-users.