Unlocking AI ROI from enterprise data (Part 1)

When AI can act on enterprise data

Generative AI has become part of everyday work. It helps draft emails, summarize meetings and calls, and assist with writing code. As individuals and organizations, we’ve adopted these capabilities quickly and found some real efficiency gains.

But when it comes to working with enterprise data, AI largely stops short, and that represents a significant missed opportunity. You can’t reliably ask AI a basic business question like “What was our average DAU (Daily Active User) last quarter?” because it doesn’t know which definition of DAU is the correct one. It can’t tell whether a model is built on top of a healthy pipeline, or whether the data it’s using is fresh, trusted or who is using it and for what purpose.

Enterprise data is where critical decisions are made, risks are managed, and value is created. Enabling AI to operate reliably on this data would unlock faster insights, better decisions, and more effective strategy across the business. For example: 

• A business user asks an AI agent a question about performance or risk and receives an immediate, reliable analysis based on trusted data.
• AI assesses the reliability of a dashboard or analytics model based on the quality, security, and lineage of the underlying data, and recommends corrective actions.
• An agent generates a new ML model with full awareness of existing definitions, dependencies, and downstream usage.
• An AI system identifies that a dashboard contains sensitive information, understands who has access to it, evaluates that access against their permissions, and recommends or initiates corrective action.  

Research supports this shift. An MIT study shows that enterprises that embed AI deeply within their data and operational workflows are extracting millions of dollars in value, while those that treat AI as an assistive layer see far more limited impact.

That is the promise of AI for enterprise data.

Introducing the enterprise data workloads

Enterprise data is continuously processed across the organization. It is created, transformed, analyzed, governed, and acted on through four main enterprise data workloads:

• Analytics
• Data operations
• Data-powered applications 
• Security

Each of these workloads processes and uses enterprise data in a different way. Today, these workloads are becoming increasingly AI-native. When AI operates directly within these workloads, it can reason over context, recommend actions based on business impact, and support decisions and execution where data work actually happens.

In the sections that follow, we’ll explore how. 

1. Analytics: providing instant insights

The goal is simple: a world where any stakeholder can ask a complex business question and receive an accurate, real-time answer without waiting for a report to be manually built. In this model, AI agents become active consumers of enterprise data. They continuously explore metrics, detect changes, and surface risks or opportunities at a scale and frequency no analytics team can match. Gartner estimates that by 2027, 50% of business decisions will be augmented or automated by AI agents for decision intelligence. 

2. Data management and observability: making enterprise data reliable for AI

Operating AI agents at scale places new demands on the data layer. Meeting those demands requires strong data operations that ensure pipelines are built quickly and remain reliable over time. Today, AI can assist with pipeline creation through natural language. But if AI is embedded more deeply in the data layer, it can understand lineage and trust signals, recommend updates, and evolve pipelines as data changes.

Beyond creation, AI improves ongoing reliability by detecting behavioral patterns in the data and suggesting corrective actions autonomously. 

3. Data-powered applications: continuously refining predictive models

Data-powered applications rely on predictive models to drive decisions across pricing, forecasting, personalization, and more. When AI is embedded directly into enterprise data, it can monitor how data feeding these models evolves, detect shifts in behavior, and recommend refinements before model performance or accuracy degrade.

4. Security and compliance: protecting data at AI scale

As enterprise data is created and accessed more frequently, protecting sensitive information becomes critical. In fact, 66% of organizations have caught AI tools over-accessing data users were not authorized to see (Cyera). When AI operates directly on enterprise data, it can detect when sensitive information flows downstream to reports or dashboards, understand who can access it, and automatically take the appropriate corrective steps.

We’ve seen how AI can enhance each of these enterprise data workloads when it operates directly on enterprise data. The question now is what this means for the business.

The measurable impact of AI for enterprise data

By providing AI with a deep understanding of meaning, usage, lineage, intent, and governance, enterprises unlock measurable ROI across every data workflow.

Research by Gartner, Forrester and Accenture shows: 

Up to 60% lower total cost of ownership
As AI becomes capable of operating within enterprise data workflows, large portions of analytics, governance, data operations, and security activities shift from manual effort to AI-driven execution. Tasks such as dashboards building, monitoring, validation, classification, and remediation require less ongoing human intervention, significantly reducing the cost of operating data at scale.

Up to 10× faster decision and execution cycles
When AI can access and reason over enterprise data directly, decisions are no longer gated by manual analysis or report production. AI can assess current conditions, evaluate impact, and surface recommended actions in near real time, compressing decision and execution cycles from days or weeks to minutes within operational workflows.

2–4× scale in data operations without growing headcount
As AI becomes embedded into enterprise data workflows, agents can take on a growing share of routine data work. By handling work at machine speed, AI allows data teams to support more data consumers, more AI-driven use cases, and higher data volumes without proportional increases in headcount. The result is a meaningful increase in operational scale while keeping teams lean and focused on higher-value work.

Up to 50% lower risk of sensitive data exposure
With awareness of data sensitivity, usage, and intent, AI can apply access controls and governance policies more consistently across platforms. Continuous detection of risky data flows, combined with timely intervention, reduces both the likelihood and impact of sensitive data exposure.

Conclusion

AI delivers its greatest value in the enterprise when it can operate directly on enterprise data with full context. When AI understands meaning, usage, lineage, intent, and governance, it can be leveraged at scale and begins to drive real, measurable impact.

Across analytics, data operations, data-powered applications, and security, this shift enables faster execution, lower costs, greater scale, and reduced risk. This is the opportunity in front of data leaders today.

In Part 2, we’ll explore what it takes to make this possible, and how enterprises can build the context AI needs to reason, recommend, and act reliably on enterprise data.

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