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The rise of generative and agentic AI is the most consequential technology innovation to materialize in decades, and it is arguably bigger than the advent of the public internet and cloud computing. As enterprises scramble to evaluate and deploy AI tech stacks and agentic frameworks that are evolving rapidly over weeks and months, traditional data intelligence platforms are struggling to keep pace. Realizing the true promise of AI requires a fundamental mindset change, and organizations must prioritize the business outcome first.

There is an immense opportunity for data intelligence companies to rise to the challenge, unlike the recent irrational pivot of Allbirds, redirecting its focus from footwear to AI. Data is the fuel that powers AI applications and workloads, and enriching it into a knowledge layer is a valuable endeavor. Subsequently, three solution providers are reimaging their architectures to capitalize.

Alation

Alation was founded nearly fifteen years ago with a mission to democratize data access and comprehension for non-technical users. By creating an enterprise data catalog to facilitate data search and discovery, Alation has evolved its platform to include governance and AI-ready capabilities. Proof of success lies in adoption, and the company boasts that over five hundred customers use its solutions today.

Alation’s AI pivot involves collaborating with its customers to identify impactful business outcomes realized through the deployment of AI agents. Once that critical milestone is established, the company offers deep insights gleaned through tailored metadata to ensure that agents execute important tasks, including cost optimization and revenue generation. Alation’s Knowledge Engine serves as its tip of the spear, providing a centralized hub that provides governed data, context delivery and agent creation and management.

I recently spent time with Alation’s co-founder and chief executive, Satyen Sangani, to understand the impact of its efforts. During our discussion, we examined the company’s efforts with Daimler Truck North America to help govern the automaker’s global manufacturing, supply chain and engineering data through trustworthy AI agents. DTNA leverages Alation’s AI and metadata management capabilities to quickly move from experimentation to production, while improving data quality and visibility across silos and legacy systems to ensure that outputs are accurate. It is a powerful example of how the company is extending its capabilities beyond traditional data intelligence services to unlock deeper AI value.

Collibra

Collibra aims to turn enterprise AI ambition into value through its Data Confidence platform. Confidence is a wise term to use, given the need to foster trust in agentic workflows that are often a black box to organizations from an observability standpoint. To deliver on this promise, the company offers the ability to actively deliver unstructured data into AI-ready assets. It also provides a framework that embeds business context, assurance and policy orchestration to improve business outcomes through the scaling of AI agents, models and use cases. Finally and most critically, Collibra automates risk reporting across multiple data sources to ensure data governance and regulatory compliance.

At Google Cloud Next this year, Collibra announced an expansion of its partnership with Google. The specific enhancements center on improving data governance within today’s data lakehouse environments. Through the bi-directional integration of Collibra with the Google Cloud Knowledge Catalog, customers can take advantage of enriching data with critical business context, including data lineage, ownership and quality metrics. It is a powerful set of capabilities that ingests Collibra-governed metadata directly into Google’s intelligent data fabric to strengthen agentic workflow management and automate discovery to ensure that enterprise systems of record are continuously updated. The integration is currently in public preview.

Informatica

Informatica markets its Intelligent Data Management Cloud to unify, connect and manage data across hybrid, multi-cloud environments. Like Alation and Collibra, it offers data cataloging to discover and map assets and ensure data quality and governance. Worth highlighting is Informatica’s Claire AI framework, which is designed to automate tasks, facilitate lifecycle management and improve security posture through data masking schemes.

The company identifies a host of broad agentic use cases and vertical industry applications, easing what is often the highest barrier to entry – simply getting started. In totality, Informatica offers a solid set of capabilities, and its $8 billion acquisition by Salesforce late last year sends a message to the broader market of its value.

The Lifeblood Of Modern AI

Data is the lifeblood of modern AI workflows and applications. To ensure the refinement and accuracy of agentic workflows across cloud, network edge and on-premises infrastructure, data intelligence requires a reimagination. The good news is that Alation, Collibra and Informatica are stepping up to the challenge. Consequently, enterprises can balance the need for deployment speed to maintain a competitive advantage while simultaneously maintaining critical data governance.