By Anthony Lempelius, Chloe San Nicolas
Published on August 25, 2025
In the rapidly evolving landscape of GenAI and Agentic AI, enterprise leaders are discovering a sobering truth: AI is only as good as the data that powers it.
The age of AI has made data not just an asset, but a dependency. Without trust in that data, innovation quickly hits a wall.
Despite soaring investments and pilot projects, McKinsey’s latest report reveals a troubling paradox: More than 80% of enterprises report no meaningful impact from their GenAI initiatives. The technology is there. The talent is ready. So why the underwhelming results?
The answer lies in the silent multiplier few are prioritizing: data quality. Data quality is the foundational block that makes AI insightful, impactful, and relevant. It’s also the focus of an upcoming joint webinar with AWS, Tata Consultancy Services (TCS), and Alation, where you’ll hear how some of the largest enterprises in the world are solving this challenge to unlock scalable, trusted GenAI.
Read on to learn more!
As mentioned, most GenAI deployments are trapped in pilot mode. Horizontal copilots and chatbots might improve productivity on the surface—but their benefits are diffuse and hard to measure.
The true value lies in vertical, domain-specific use cases, where GenAI and AI agents can automate complex, end-to-end business processes. But less than 10% of these use cases escape pilot purgatory, blocked by a common culprit: inconsistent, untrustworthy, or inaccessible data.
It’s not a technology problem, but a trust problem. And trust starts with data quality.
GenAI systems, particularly Agentic AI models, don’t just need access to data. They need data that is:
Accurate and free from bias or duplication
Timely and reflective of real-world changes
Governed, traceable, and aligned with regulatory standards
Contextualized, so agents can reason across workflows with memory and intent
Without this foundation, even the most advanced LLMs can hallucinate, produce misleading insights, or fail to gain user trust – driving up costs and eroding ROI.
In the enterprise context, poor data quality doesn’t just slow things down; it also risks business outcomes, reputations, and compliance.
Alation is leading the way in solving this challenge. With the introduction of Native Data Quality capabilities within the Alation Data Intelligence Platform, enterprises can now:
Continuously monitor and score data quality at the source
Surface DQ insights directly in the data catalog, where data consumers make decisions
Embed trust signals and lineage into GenAI and Agent workflows
Accelerate the resolution of data issues across cross-functional teams
This isn’t about fixing broken data – it’s about empowering GenAI to make reliable, repeatable decisions at scale.
The result? AI outputs become more trustworthy, actionable, and cost-efficient—and organizations can scale from experimentation to industrialized GenAI and Agentic AI deployments. With native data quality, GenAI becomes less experimental and more operational.
To help data leaders put these insights into action, Alation is teaming up with AWS and TCS for a can't-miss 30-minute webinar:
In this session, you’ll hear:
AWS’s market vision for GenAI and Agentic AI—and why governed, AI-ready data is the linchpin
Alation’s product evolution, including how Native Data Quality empowers GenAI pipelines and AI agents
TCS’s field experience, delivering large-scale AI transformations using Alation as the system of value
A strategic framework to scale AI with less risk and greater trust
A look inside Alation’s newest Agentic DQ capabilities
Real-world case examples of data product marketplaces powering GenAI success
Actionable next steps to embed DQ in your AI architecture and workflows
Data quality isn't just an enabler. It's the multiplier that separates pilot fatigue from performance at scale.
Join us to learn how Alation, AWS, and TCS are unlocking the next era of AI transformation.
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