Most enterprises have invested heavily in AI, but real ROI still feels just out of reach. The reason isn’t the technology, but the data behind it.
That’s the challenge we’ll be exploring at Snowflake Summit 26 in San Francisco, June 1–4, where Precisely is proud to exhibit as part of our ongoing partnership with Snowflake. This year’s Summit theme – “Making AI Real for Business” – couldn’t be more aligned with what we’re hearing from data leaders every day.
Organizations are increasingly adopting Agentic AI: systems that reason, decide, and take action autonomously across core business processes. But before you can drive measurable ROI from your AI agents and autonomous workflows, you have to make your data ready.
As a Snowflake partner, Precisely helps enterprises
Why is Data Integrity Critical for Agentic AI?
A reported 95% percent of AI initiatives are failing to deliver a positive return on investment. Bad data is at the root of this issue.
When AI systems were primarily supporting human decisions, imperfect data was manageable. People could intervene, validate, and course correct.
Agentic AI removes that safety net. These systems act on the data they’re given, autonomously and at scale. When the data is flawed, the outcomes are too – and there’s no human in the loop to catch it.
This is what we call the Agentic AI Data Integrity Gap: the widening divide between what autonomous AI systems are capable of delivering and what enterprise data can reliably support today. Rather than a single failure or missing capability, this is a set of persistent conditions that, together quickly compound to limit accuracy, context, trust, and scale, creating significant risk to the business.
I’ll be discussing this in more detail during my speaking session at Snowflake Summit, but want to give you a preview here.
The Six Dimensions of the Agentic AI Data Integrity Gap
The Agentic AI Data Integrity Gap shows up across six interconnected dimensions. Organizations rarely face just one – they tend to coexist and reinforce each other. As a result, there’s a barrier to accurate, confident autonomous decisions.
- Trapped. Critical data is scattered across hybrid and multi-generational IT environments. When it can’t be easily discovered, understood, or connected, it becomes effectively inaccessible to AI systems that depend on timely, reliable inputs.
- Incomplete. Internal data alone rarely tells the full story. Without enrichment from authoritative third-party sources – location data, risk signals, demand indicators, real-world context – AI systems are forced to reason with blind spots.
- Outdated. Data refreshed periodically, rather than continuously, forces AI to act on what was true hours or days ago. In dynamic environments, that lag erodes confidence in outcomes quickly.
- Inconsistent. Inaccurate, duplicated, non-standardized, or misaligned data creates ambiguity that autonomous systems can’t resolve through judgment or escalation. Quality issues that humans once filtered out now flow directly into decisions.
- Non-compliant. As AI takes on greater autonomy, governance gaps become critical risks. Without traceability, verification, and consistent policy enforcement, you can’t explain how AI-driven decisions were made – or demonstrate alignment with regulatory requirements.
- Expensive. Historically, data integrity has depended on manual processes and specialized skills. That model doesn’t scale. When AI demands continuous confidence rather than point-in-time validation, reliance on human intervention becomes a barrier to broader adoption.
Individually, each of these conditions constrains AI outcomes. Collectively, they prevent your organization from moving beyond experimentation to autonomous execution.
What is Agentic-Ready Data? How Does it Close the Data Integrity Gap?
Closing the gap doesn’t require starting over. It requires elevating data integrity to meet the demands of autonomous decisioning, and applying it continuously, not selectively.
Agentic-Ready Data is data that consistently demonstrates accuracy, consistency, and context at the point where decisions are executed. It’s the highest-quality data that is integrated, governed, and enriched for AI, automation, and analytics initiatives across the enterprise – and available in the form and timeframe AI systems need to act with confidence.
Achieving Agentic-Ready Data means meeting six requirements that directly address the elements of the Agentic AI Data Integrity Gap: unifying trapped data, enriching incomplete data, refreshing outdated data, shaping inconsistent data, governing non-compliant data, and automating away the cost of manual processes.
- Unify your data. Connect data across your IT landscape through a common catalog so it can be discovered, classified, and understood – and integrate it where required to support decisioning.
- Gain an enrichment edge. Close gaps in internal data with authoritative third-party and location-based context, so AI systems can reason beyond what operational systems capture.
- Operate in the now. Continuously refresh and maintain data so autonomous systems are working from current conditions, not backward-looking snapshots.
- Shape data for purpose. Ensure data meets the quality standard required for its specific use – completeness, validity, and consistency where they matter most.
- Elevate governance. Put guardrails in place so AI decisions are traceable, verifiable, and aligned with internal controls and evolving regulatory requirements.
- Lower the cost structure. Leverage AI-driven automation and interoperable capabilities to reduce reliance on manual processes and specialized skills, so integrity can scale alongside your AI ambitions.
Find Out More with Precisely at Snowflake Summit 26
Precisely and Snowflake share a common goal: helping enterprises get more value from their data.
Through our partnership, you’re able to bring Precisely data integrity capabilities to the data that lives in and flows through Snowflake’s platform – ensuring it’s accurate, enriched, governed, and genuinely ready to power autonomous AI.
That’s the story we’re looking forward to telling at Snowflake Summit 26. If you’re attending, we’d love to connect.
- Join my speaking session on June 2 at 11:30 AM – Achieving Agentic AI ROI with Snowflake: Is Your Data Ready? You’ll find out what it takes to prepare your Snowflake data for agent-driven outcomes.
- Visit our team at booth 1212 to start mapping your path to Agentic-Ready Data and to learn more about how Precisely and Snowflake are working together to help enterprises make a real impact with AI across the business.
