As enterprises integrate artificial intelligence into their business operations, the focus is shifting from automating routine tasks to enabling agentic AI—AI systems that can act autonomously, make decisions, and drive business outcomes. However, achieving this transformation depends on a critical but often overlooked resource: AI-ready data. Proprietary data isn’t just a supporting element in the AI ecosystem—it’s a strategic asset that determines how effectively agentic AI systems can operate.
Why Data Quality is the Foundation of Agentic AI
Unlike traditional AI applications, which may operate on general datasets, agentic AI thrives on enterprise-specific, contextual data. According to the recent CIO article What to Expect from AI in the Enterprise in 2025, foundational models (FMs) trained on broad, public datasets are excellent for general-purpose tasks but often fall short when applied to enterprise-specific workflows. “To benefit from this wider range of [retrieval-augmented generation] services, organizations need to ensure their data is AI-ready,” the article notes.
AI-ready data requires robust information management practices such as:
- Data Cleaning & Validation: Ensuring that enterprise data is accurate, relevant, and free from duplicates.
- Data Structuring & Enrichment: Organizing data into formats that AI systems can easily understand and enriching datasets with contextual metadata.
- Data Ownership & Compliance: Clearly defining who owns the data and ensuring compliance with privacy and governance standards.
Not sure where to start? Check out this comprehensive guideline for creating AI-friendly documents.
From Data Silos to Competitive Advantage
Enterprises that treat their proprietary data as a competitive differentiator will be well-positioned in the coming years. As AI in the Enterprise in 2025 explains, “The sooner enterprises identify data assets from across the business, adopt a creative approach to how they might be used, and get them in an AI-ready state, the sooner they’ll be able to take advantage of new RAG services coming down the line in 2025.”
This preparation enables businesses to generate deeper insights, unlock previously untapped opportunities, and create a sustainable competitive advantage through AI-powered innovations.
Data Strategy: The CIO’s Next Business Imperative
While CIOs have long recognized the value of data, many still struggle with aligning data strategies to tangible business outcomes. The CIO article 5 Tips for Better Business Value from Gen AI highlights how forward-thinking enterprises are linking data quality initiatives directly to revenue-generating outcomes:
- Sales Enablement: AI-powered CRM tools that provide predictive insights by analyzing customer interaction data.
- Marketing Personalization: Gen AI-driven marketing platforms that generate tailored content based on enriched customer data.
- Service Optimization: AI-driven service teams that resolve customer issues faster by leveraging structured service records.
“Improving data quality and integrating new data sources…are vital for applying AI in marketing and sales,” said Jacqueline Woods, CMO of Teradata, in the article. She emphasized how combining structured and unstructured data can unlock new opportunities for customer engagement and retention.
Looking Ahead: AI-Ready Data as a Long-Term Asset
Preparing proprietary data isn’t a one-time project—it’s an ongoing strategic investment. Forrester predicts that AI governance software spending will quadruple by 2030, reaching nearly $16 billion. This surge underscores the growing recognition that high-quality enterprise data is essential for building advanced AI capabilities.
As businesses face intensifying competition and rising customer expectations, agentic AI fueled by proprietary data will be the defining factor between market leaders and laggards. The enterprises that invest today in making their data AI-ready will unlock capabilities that extend far beyond automation—enabling AI systems that drive innovation, enhance decision-making, and transform entire industries.
Is your enterprise ready to harness the full potential of its proprietary data?
About the Author
Assaf Melochna, President and CoFounder, Aquant
Assaf Melochna is the President and co-founder of Aquant, where his blend of decisive leadership and technical expertise drives the company’s mission. An expert in service and enterprise software, Assaf’s comprehensive business and technical insight has been instrumental in shaping Aquant.
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