TDWI White Paper Library

Building Agentic and Generative AI: Enterprise Data Foundations and Applications

Mon, 11 May 2026 07:00:00 GMT

Generative and agentic AI are reshaping how enterprises work, but widespread adoption hasn't translated into widespread value. According to new TDWI research sponsored by ZoomInfo, only about 40% of organizations report measurable or consistent business impact from their AI initiatives, even as the majority are already experimenting with the technology.

The gap between experimentation and scaled deployment comes down to one critical factor: enterprise data. Organizations that treat company data as foundational to their AI applications are far more likely to achieve real, measurable outcomes, while those relying solely on off-the-shelf tools continue to see inconsistent results.

Download this free TDWI research report, Building Agentic and Generative AI: Enterprise Data Foundations and Applications, to understand what separates AI leaders from laggards and get a practical blueprint for building enterprise-grade AI that actually delivers, including:

Why most AI deployments stall at the pilot stage and what it takes to move into production
A maturity progression from generative AI tools to multi-agent systems, and where your organization fits
The six-layer enterprise AI capability blueprint required to scale agentic AI safely and reliably

Achieving the Agentic AI Advantage: Closing the Data Integrity Gap with Agentic-Ready Data

Mon, 11 May 2026 07:00:00 GMT

As AI evolves from analysis to action, enterprises are entering the era of agentic AI—systems that reason, decide, and execute autonomously.

Yet most organizations are constrained by a growing data integrity gap, where fragmented, outdated, and context‑poor data limits AI’s ability to operate at scale.

This guide explores how agentic‑ready data enables trusted, real‑time decisioning and unlocks the full value of autonomous AI.

The ROI of Gen AI and Agents

Mon, 11 May 2026 07:00:00 GMT

Most organizations have moved past the generative AI “hype” and are now facing the challenge of proving that these investments actually pay off.

A new global research report, “The ROI of Gen AI and Agents,” reveals that 92% of early adopters are seeing a positive return on their generative AI investments.

This research offers a strategic blueprint for scaling AI initiatives by highlighting:

A deep dive into the business areas where gen AI is moving the needle most, specifically in enhancing customer interactions and accelerating innovation
Why organizations are still hitting data quality bottlenecks and the specific steps taken to bypass them
How leaders are moving from basic chatbots toward autonomous AI agents to drive material efficiency

Turn Trusted Data Into AI Outcomes Finance Can Measure

Thu, 23 Apr 2026 07:00:00 GMT

Many AI initiatives fail not because of vision, but because the data does not have the right business context required for AI. IDC research shows that trusted data is a primary driver of AI success and a common reason projects never reach production.

Discover how finance leaders are helping establish the governance, consistency, and trust required to make AI deliver real business value in this IDC InfoBrief, sponsored by SAP. You’ll see how organizations with strong data practices see two to three times greater AI ROI, along with gains in retention, profit, and operational efficiency.

Turn Your Data Into an Ai-ready Strategic Asset

Thu, 23 Apr 2026 07:00:00 GMT

AI-driven innovation starts with reliable, business-ready data.

When organizations treat data as a product, they can empower teams, accelerate AI adoption, streamline data operations, and drive innovation across every business function.

Discover the characteristics and benefits of data products, as well as essential guidance, in this latest report.

AI/ML Within a Data Lakehouse Architecture

Tue, 14 Apr 2026 07:00:00 GMT

Is your data infrastructure ready to support agentic AI, generative AI, and traditional AI?

These different types of AI have many things in common, but there are also significant differences that you must consider when building your AI data infrastructure. At the same time, you can’t neglect your other workloads. It’s still imperative for your solution to support BI, data analytics, and data science.

A modern data lakehouse can be the foundation that supports all your data needs. Download this paper today for an in-depth explanation of data lakehouse reference architecture and its capabilities for supporting AI/ML workloads.

Migrating from Hadoop to an Iceberg-Powered Lakehouse

Tue, 14 Apr 2026 07:00:00 GMT

When organizations began to collect large volumes of data, Hadoop was the backbone of big data workloads. But today’s AI and modern analytics demand agility, speed, and efficiency, qualities Hadoop struggles to deliver.

Modern workloads require modern architectures.

For an AI-ready infrastructure, you need a data lakehouse powered by high-performance object storage. Data lakehouses are built to handle AI tasks such as model creation, training, inference, and generative AI. At the same time, they can also handle traditional data workflows like ad hoc analysis, analytics, and BI.

But saying you need to modernize your infrastructure is the easy part. How do you move forward? Download this guide for a pathway to success that takes you off of Hadoop and sets you on the road to successful AI.

Extending Databricks AI and Analytics to Your On-Premises Data

Tue, 14 Apr 2026 07:00:00 GMT

Most modern enterprises have data distributed across a hybrid environment, with some on premises and some in the cloud. But what happens when you try to implement AI or advanced analytics, but your platform for these modern workloads doesn’t work with on-premises data?

Some data cannot move to the cloud because of specific regulations or technical challenges. The question is not whether your data will remain distributed across on-premises and cloud environments. It will.

The question is whether your analytics architecture will treat that separation as a constraint to work around or a reality to design for.

Running successful AI isn’t dependent on having the most data in the cloud. You need to be able to reach all your data today. Download this resource to learn more.

Better Fraud Detection Starts with the Data Layer

Tue, 14 Apr 2026 07:00:00 GMT

Financial institutions are losing more than $190B to credit card fraud annually. Half a billion personal records were compromised in 2025 alone.

Fraud is increasing at an exponential rate, but legacy data infrastructures can’t respond fast enough. The fraud models themselves aren’t the problem; you need a data foundation that can run fraud analysis at enormous scale, in real time, across modern hybrid ecosystems.

If you’re ready to build a high-performance, hybrid-enabled data foundation that can keep pace with modern fraud rates and transform your fraud detection and risk analytics operations, download this resource today.

Scaling AI & Analytics in Digital Manufacturing

Tue, 14 Apr 2026 07:00:00 GMT

Digital manufacturing is flooded with data. Sensors and monitoring systems are constantly streaming information. Many organizations are turning to AI to make sense of it all and turn the surplus of data into market advantage.

But across the industry, full implementations of AI programs are failing. The model might work as a pilot project, but the established data architecture cannot support scaling it up.

Industrial data architecture wasn’t built to support AI workloads.

You must fix your data foundation before you invest in AI. If you’re ready to start, download this playbook today.

Close the Gap Between Strategy and Execution: Driving Impactful Business Decisions with Intelligent Applications

Thu, 26 Mar 2026 07:00:00 GMT

Can your systems keep pace with today’s flood of data and turn it into real-time decisions that matter?

With SAP Business Data Cloud Intelligent Applications, you can close that gap by embedding real-time, AI-led insights into everyday workflows to ensure that decisions are informed and effective.

These prebuilt apps enable you to:

Improve liquidity visibility and optimize working capital
Anticipate disruptions and adapt with predictive analytics
Identify talent gaps and improve workforce retention

And since it’s built on a harmonized, trusted data foundation, these applications ensure AI outcomes are relevant, accurate, and actionable. That means no more disconnected dashboards or siloed insights.

Discover how to move from reactive decisions to proactive strategies in the “Driving impactful business decisions with intelligent applications” e-book.

The Trust Factor: Data, Resilience, and Innovation

Thu, 26 Mar 2026 07:00:00 GMT

The reliability of your organization’s data is the cornerstone upon which every business decision is based and every deal is struck. Without it, organizations struggle to make informed decisions, adapt to market changes, and ultimately remain competitive.

In this global survey of 1200 IT and business leaders, SAP found that only 34% of organizations believe their data is of a highly reliable standard. This means that the statistical majority cannot pivot emerging market developments confidently.
SAP Business Data Cloud exists to mitigate this problem. By unifying SAP and third-party data with fully managed capabilities for a business data fabric, industry leaders are now able to take advantage of a truly interconnected system that shares accurate information in real time.

Prioritize building trust in your data with our report, “The trust factor: Data, resilience, and innovation”.

In it, you’ll learn about:

Mending trust gaps between systems and decision-makers
Harmonizing your key programs seamlessly on one platform
Extending your data capabilities with AI