Why data readiness defines GenAI success: Krish Vitaldevara, Informatica

The success of GenAI initiatives is increasingly tied to the strength of enterprise data foundations. In this exclusive conversation, Krish Vitaldevara, Chief product Officer, EVP at Informatica, discusses the realities of data readiness, the evolution of metadata-driven intelligence through CLAIRE, the value of multi-cloud neutrality, and the growing importance of integrated governance for scalable, trustworthy AI.

He also outlines how enterprises can quantify ROI from Intelligent Data Management Cloud (IDMC), what the Salesforce acquisition means for the product roadmap, and how Informatica is addressing the industry-wide talent gap through a deliberate, innovation-centric talent strategy anchored in India’s iLabs.

How do you evaluate the current state of enterprise data readiness for scalable GenAI deployment? Are enterprises truly ready?

This is a critical question. A recent MIT study highlights that 95% of enterprises, despite investing nearly USD 40 billion, are failing to realise full return on investment from AI initiatives. Additionally, in our conversations with Chief Data Officers, about 70% cite lack of data readiness as the primary impediment to achieving success in their AI programmes.

Enterprises are at varying stages of maturity. Many do not yet have the strong data foundation required to support scaling AI, especially GenAI. Our Intelligent Data Management Cloud (IDMC) addresses this gap by enabling enterprises to prepare, activate, manage, and secure their data. It ensures that data is intelligent, contextual, trusted, compliant, and secure.

Interestingly, organisations in regulated industries tend to be more prepared because they have historically invested heavily in data hygiene. But overall, readiness is a journey, and we support enterprises across all stages.

Informatica’s CLAIRE AI engine is widely recognised for moving beyond basic automation toward a metadata-driven system of intelligence. How does CLAIRE translate into measurable business value?

CLAIRE has evolved significantly. We began with CLAIRE GPT, then introduced copilots embedded into every product across data quality, data integration, master data management, and more. Each copilot supports data engineers in automating tasks.

The next phase is CLAIRE Agents, purpose-built data agents that move beyond automation toward reasoning-driven workflows. In our fall launch, we released agents that support data engineers in managing data quality. These agents allow users to analyse data quality through natural language, review results, map 2,000+ quality rules to specific datasets, and execute or automate these rules as needed. Similar agents exist for master data management and data integration.

Enterprises can also use our AI Agent Engineering product to compose these purpose-built agents alongside custom or third-party agents to execute tasks end-to-end. This enables a shift from heuristics to reasoning.

All of this is tied to the metadata system of intelligence. Informatica has been a pioneer in metadata, and we lead Gartner’s Metadata Magic Quadrant. Metadata becomes the foundation for preparing, managing, activating, and governing data. Semantic data models, policies, and governance guardrails all operate on metadata rather than raw data. This approach is foundational for scaled AI and agentic automation.

Data integration has long been Informatica’s core strength. In the current environment, where data gravity is consolidating around hyperscalers, how do you maintain multi-cloud and hybrid neutrality?

This is an industry-wide challenge with multiple layers. Every hyperscaler now offers basic integration. Application providers also offer integration capabilities. Data platforms like Snowflake and Databricks do the same. The question then becomes: why choose Informatica?

There are three reasons.

First, we offer one of the most comprehensive integration ecosystems, with more than 50,000 connectors. Enterprises rarely operate in a single-cloud world. They need reliable, performant, enterprise-grade connectivity across Azure, AWS, Google Cloud, SAP, Oracle, Snowflake, and beyond. Building this breadth and depth takes years.

Second, our connectors are metadata-aware. They understand both sides of the pipeline and optimise the integration based on schema, context, and policies. This also enables applying quality and transformation at source, making the integration compute-efficient, processing-efficient, and storage-efficient. Working through metadata rather than moving raw data reduces complexity and cost. Metadata also allows tracking lineage, which is essential for responsible AI and explainability.

Third, we act as the “Switzerland of data.” We are not tied to a persistent storage layer. Our job is to help enterprises manage, activate, and prepare data regardless of where it resides. Most ecosystem providers offer connectivity primarily to pull data into their own environment, which aligns with data gravity. But enterprises still operate in siloed environments. Our neutrality enables us to support hybrid and multi-cloud architectures without locking customers into walled gardens.

This neutrality extends to AI as well. Our agent engineering environment supports not only Informatica agents but also third-party and custom agents, allowing them to interoperate seamlessly.

As enterprises adopt GenAI, the complexity of data governance increases, especially around lineage, hallucinations, and compliance. How does Informatica enable responsible and trustworthy AI?

The rapid adoption of agents and AI models has dramatically increased governance complexity. Many enterprises already manage tens of thousands of data tasks. In the AI era, this scales to tens of thousands of agents as well.

The solution lies in a unified metadata-driven foundation. An enterprise catalog that understands entities, relationships, policies, and lineage becomes the single source of truth. This catalog does not require enterprises to consolidate immediately; it can operate across heterogeneous catalogs, but the more an enterprise consolidates, the more complexity shifts from people and processes into the catalog itself.

Auto-cataloging is critical. Automatically detecting relationships, lineage, governance rules, compliance requirements, and quality constraints reduces manual overhead and ensures consistency. Enterprises can also create a trusted data marketplace where verified, compliant assets are published for reuse. This simplifies explainability, reduces risk, and accelerates AI development.

The Intelligent Data Management Cloud integrates cataloging, quality, policy enforcement, lineage, privacy, and governance into a single brain. Point solutions may compete on price, but the integrated platform delivers the value required for enterprise-scale AI.

Can you share a framework or KPIs that customers using IDMC can track to quantify business value from your platforms?

I will start with a customer quote because it makes this very real, and then I will touch on the KPIs. Even though for many of us who live and breathe data these capabilities appear mandatory, many enterprises have not invested in them. The reason is that historically enterprises viewed these areas as a tax, compliance was a tax, quality was a tax. Some invested, some did not, which is why we see such a wide spectrum of data readiness.

Many enterprises approached these areas as a tax. Today, one customer I met recently in Asia Pacific said it is no longer a tax. They are being told that this is now part of the value proposition. AI has changed the landscape. The expectation is no longer, “Give me high-quality data because I need to meet compliance.” It is now, “Give me high-quality data so I can produce high-quality insights.”

“Give me data that is compliant so that I can explain why my AI agents make certain decisions.”

This shifts the focus from the cost part of the curve to the value part of the curve, and we are seeing that shift across enterprises. What used to be, “I have to do this because I must,” has now become, “I am doing this because my business extracts value from it.” That shift has been incredibly useful.

When we think about KPIs, each customer has a slightly nuanced view depending on their workflows. Some track the percentage of their data that is high quality. Others focus on the percentage of data assets and AI assets that are part of their governed marketplace. Another important metric is the percentage of data in any agentic workflow for which they have proper lineage.

These metrics help enterprises avoid garbage in, garbage out, and help them stand behind their data. This is crucial when data becomes central to operational agentic workflows, AI workflows, and operational data flows. These metrics have now become very important.

Salesforce will complete the acquisition of Informatica by the third quarter. There is a valuation of USD 8 billion in equity value that Salesforce is offering. Post-acquisition, what are the immediate and non-negotiable integration points for IDMC within the Salesforce ecosystem? And how will you ensure continued standalone development?

A few points here. First, I must emphasise that until the deal closes in Salesforce’s Q4, we are still two independent companies. Our roadmaps are still independent. There is excitement, but we cannot start building joint roadmaps until we are a single entity.

Second, a major part of Salesforce’s acquisition thesis is that Informatica completes their portfolio, specifically their Data 360 portfolio. At Dreamforce, they announced how they are bringing together Data Cloud, MuleSoft, Tableau, and now Informatica, along with Agentforce. When you combine these services, enterprises gain a complete view of their data, where it resides, how it flows, how it is consumed, and how it is analysed and visualised. This becomes a full portfolio with strong products across every layer.

The acquisition thesis is that Informatica completes this portfolio. To do this effectively, we must keep our roadmap. From the perspective of existing customers, we are keeping our roadmap. For joint customers, things will become even better as the Data 360 vision matures.

Also, Salesforce has a significantly larger customer base. For us, this represents incredible growth and roadmap acceleration rather than trade-offs. We view this positively, but again, we are still two independent companies. Much of this is current acquisition theory, and we are waiting for the deal to close before we can materialise these plans for customers.

My last question is about talent acquisition. The success of any AI platform depends significantly on the operating model, and we see a substantial skill gap in the industry. How is Informatica addressing this talent gap?

I will break this into two parts. For us, the approach to talent management and the work we do with iLabs in India are related and important.

First, we have a holistic approach to talent management. This begins with being very explicit about how the organisation is designed, the structure of the pyramid, the ratio of junior to senior engineers, and whether teams are well-distributed.

Second, we are intentional about the kind of talent we bring in. It is not only about skills and closing skill gaps; cultural fit matters. We work closely with many universities. In India, for example, we participate in the National Apprenticeship Program and collaborate with IITs and other universities to identify, onboard, and coach talent.

We are very focused on diversity, not just diversity for its own sake, but diversity of thought, skills, and culture. When combined intentionally, this creates an innovation flywheel.

A strong example is iLabs India. We have more than 2,500 employees in India who have contributed to over 100 patents in recent years. Because of our explicit organisational design, many of the teams I mentioned, such as the CLAIRE AI team and the Master Data Management team, have origins in India. These teams are not siloed; they own missions and visions. We believe such centres can serve as hubs for product innovation. This is our intentional approach to talent management.