Over the past few years, companies have significantly increased their experimentation with artificial intelligence (AI). This phase has evolved into a movement to scale AI across organizations, offering immense potential but also posing challenges, particularly in achieving a return on investment (ROI). A key contributor to the struggle for ROI in these AI initiatives is data.

The volume of data has surged astronomically—between 100,000 to 150,000 times in the last 25 years, with 90% of it created within the last few years alone. Despite this gigantic amount, only about 1% of unstructured data is actively utilized, highlighting a substantial opportunity that remains largely untapped. Generative AI (Gen AI) has the capability to leverage this data effectively, yet numerous hurdles must be overcome in the endeavor of data management.

Common challenges in managing data include the lack of seamless integration between business operations and data systems, data quality issues, and existing silos within data structures and organizational practices. Additionally, with the rise of Gen AI, three new obstacles emerge:

• Contextualization: Standard generative AI models often yield only 40-60% effectiveness. To improve performance to the desired 90%, large language models (LLMs) require contextualization by incorporating deeply specialized enterprise data.
• Operationalization: Current data and machine learning operations frameworks struggle to manage the data lifecycle needed for LLMs, leading to gaps in the effective deployment of AI at scale.
• Security: The two-way data flow of LLMs introduces risks related to data leakage, exposing sensitive information like personally identifiable information (PII) or proprietary data.

To successfully navigate these challenges and operationalize AI, businesses must rethink their approach to data management. Implementing a five-part framework can aid in this process:

1. Narrowing Down the Business Problem: It is critical to identify the specific business issue and the necessary data. This precision helps limit the scope of data challenges and ensures that only relevant data is processed within the system.

2. Building AI and Data Products: These products provide targeted data solutions anchored in specific business outcomes, facilitating efficient problem-solving through increased reusability and repeatability.

3. Gen AI Enablement: Organizations should enable a mix of LLMs and specialized language models (SLMs) that balance cost, efficiency, performance, and accuracy, ensuring data fed into these models is secure and optimized for specific use cases.

4. Building a Knowledge Engineering Layer: This layer focuses on the ability to search and retrieve the right data for contextualizing Gen AI outputs, ensuring models are trained on relevant, high-quality enterprise data.

5. Modernizing the Data Stack: Data architecture must evolve to accommodate vector databases and multi-modal data models, which are necessary for Gen AI effectiveness.

While businesses have invested heavily into developing their existing data infrastructure, creating a new AI-specific stack could hamper ROI realization. Instead, the focus should be on narrowing data scope, scaling Gen AI effectively, contextualizing it with relevant data, and selectively modernizing existing infrastructure. This balanced strategy promotes a path toward achieving meaningful ROI from AI initiatives across the enterprise.

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