Why a Clear AWS Data Strategy is Essential to AI Implementation

A company’s data has long been its most important asset—an adage that remains true in the era of gen AI.

As large language models (LLMs) and foundation models (FMs) become widely accessible via out-of-the-box apps, differentiation lies not in the model itself but in the quality, structure, and accessibility of the data powering it. That means organisations with a clear, reliable AWS data strategy to power gen AI gain a true competitive advantage.

However, challenges related to data quality, accessibility, governance, or lack of internal expertise hinder companies’ ability to leverage their own data. According to Harvard Business Review, 52% of Chief Data Officers (CDOs) view their data foundation as inadequate for AI implementation. If data is viewed solely as the purview of IT rather than a strategic enabler of business outcomes, these challenges will remain, limiting AI scaling into production. Without an AI-ready data foundation, the continuous advancement of AI, including the emergence of agentic AI, will only amplify these challenges.

This article explores the requirements for an effective AWS data strategy to help you address these challenges and lay a foundation for success in gen AI initiatives

Why traditional data strategies no longer apply

AI-supportive data strategies represent a fundamental shift from traditional approaches. Unlike conventional systems that rely primarily on structured data, gen AI demands comprehensive access to all data types—including unstructured and multimodal formats such as video, audio, text, and code—with real-time accessibility across the entire data ecosystem. These requirements drive corresponding shifts in architecture, governance priorities, and how organisations measure data quality.

Given these fundamental differences, companies should evaluate their data strategy before beginning any production-scaled implementation of a gen AI solution and should continuously refine it throughout the process.

Avoiding common data barriers to AI adoption

Even well-prepared organisations often encounter three common barriers that limit their ability to harness data effectively for AI: data quality and readiness, governance and compliance, and organisational structure.

To avoid these obstacles, organisations must establish the following:

• Clear data product ownership and storage aligned with the organisation’s standardised governance model.
• Guardrails for models and automated personally identifiable information (PII) scanning and masking before data ingestion.
• Lakehouse architecture to unify and store all data types

Attributes of a solid data foundation 

• Scalability and performance: Ensuring that the foundation can accommodate exponential growth in data volume while maintaining high performance.
• Data isolation and privacy: Encrypting customer data in transit and at rest and ensuring data remains within the customer’s VPC environment.
• Access controls: Enforcing granular least privilege access to ensure that only authorised applications can access sensitive data.
• Model guardrails: Actively filtering inputs and outputs for harmful, inappropriate, or sensitive content.
• Data lineage and audit: Tracking the origin and transformation of all data used to customise the models.

Five steps to adopting an AI-first AWS data strategy

When implementing a gen AI application for the first time, it’s critical to identify a few use cases that can deliver immediate productivity or efficiency gains, as well as an early return on investment (ROI). For example, reducing service-call handling time by 30% would be an ideal candidate for an AI solution.

Here are five steps to develop an AI-first data strategy to achieve these outcomes:

1 Conduct a data audit to identify one or two use cases with high business value and mature data. Unify the relevant data in a secure, scalable storage solution and implement appropriate guardrails immediately.

2 Modernise your data architecture, including breaking down silos and defining data product owners for key business units. Establish common governance structures.

3 Build internal capabilities by upskilling data teams in prompt engineering, vector databases, and responsible AI, while training teams across the organisation on AI fundamentals and responsible use to maximise adoption.

4 Implement human-in-the-loop and LLM feedback logging to continuously monitor and improve data quality and model performance.

5 Measure ROI by tracking business outcomes, operational performance metrics, and data and trust metrics such as retrieval precision rate, factual consistency score, and daily active users. Together, these steps establish the governance, culture, and technical infrastructure needed to operationalise AI at scale.

Turning data risk into AI advantage

Challenge: After losing its data team to a spin-off, French media group M6 faced employees uploading sensitive content to public AI tools, exposing proprietary data.

Solution: With Devoteam’s help, M6 built “Alfred,” a secure internal AI assistant running entirely within their AWS environment, leveraging M6’s proprietary data through RAG technology. 

Result: Around 100 employees now use Alfred daily. M6 closed a major security gap while gaining new AI capabilities—turning a serious risk into a powerful business tool

What AWS Data Strategy Success Looks Like

Clear patterns are emerging for identifying when a company’s data strategy is supportive of AI adoption. Successful implementations start by working backwards from specific business challenges rather than leading with technology. They prioritise data quality and ensure data is contextualised, treating it as a continuous strategic asset from which new use cases can be built and integrated into real-time pipelines.

In contrast, the warning signs of struggling implementations include focusing on the model instead of the data, rushing to pick an out-of-the-box LLM without understanding proprietary data sources and governance requirements, and cleaning up data once without integrating it into real-time pipelines.

Ultimately, with the right focus and guidance, establishing an AI-supportive data strategy is achievable for organisations of any size, and Devoteam experts are available to assist along the way.

Also read: Data Pipelines for AI on AWS: From Ingestion to Intelligence