TDWI

How much is powering AI with poor-quality data costing your enterprise? Enterprise AI budgets are expanding at a pace that is making CFOs pay close attention. According to a 2026 survey of 2,360 senior executives, companies expect to spend approximately 1.7% of their revenue on AI this year, more than double the 0.8% average from 2025. Measuring ROI from AI is still a nascent practice.

Data Products for AI: Making Enterprise Data Trustworthy and Usable Webinar Speaker: Donald Farmer, Research Fellow Date: Thursday, June 25, 2026 Time: 12:00 p.m. PT / 3:00 p.m. ET Enterprises hold their most valuable operational data inside the ERP systems that run finance, supply chain, and manufacturing. As they turn to AI and advanced analytics, they want that data working in models for forecasting, planning, and day-to-day decisions.

TDWI Digital Dialogue | What Your Lakehouse Needs to Power Agentic AI Is your data truly ready to fuel agentic AI? If an autonomous agent consumed this data, would it understand what a given metric meant? Only about half of respondents to a recent TDWI survey agree that their data is easily accessible and integrated from diverse sources. For organizations that have not yet solved basic integration, agentic AI will compound the problem rather than solve it.

TDWI Blueprint Report | Building an AI-Ready Data Foundation As generative AI, copilots, and agentic AI systems move from experimentation into production, organizations are discovering that long-term AI success depends on the strength of the underlying data foundation. New research from TDWI finds that organizations achieving the greatest business impact from AI have stronger architectural, governance, and operational capabilities.

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Expert Panel: How to Make Enterprise Data Fit for Autonomous Decision-Making Webinar Speaker: Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics Date: Monday, June 8, 2026 Time: 9:00 a.m. PT / 12:00 p.m. ET As organizations move beyond generative AI toward agentic systems capable of autonomous decision-making, the demands on enterprise data are increasing significantly. Agentic AI requires data that is not only accessible, but trusted, connected, and rich in context.

TDWI Top Trends | Data Mesh and Cross-Cloud Collaboration: From Architecture Concept to Operating Model The scale and complexity of modern enterprise data have outgrown centralized data warehouses and lakes. This is the reality data mesh was conceived to address. By shifting data ownership from a central platform team to the business domains that produce and understand it, data mesh promises to untangle the bottlenecks that slow analytics and erode trust.

Poor training data does not just hurt model accuracy. It triggers a costly chain reaction. This article shows data leaders exactly where the money bleeds and what to do about it. Every failed generative AI initiative has a postmortem. And in almost every one, the blame lands on the model. But the model is rarely the problem. The real culprit is the training data. And the cost of getting it wrong is rarely contained to a single line item on a project budget.

TDWI Research Perspective | From Mandate to Motion: How AI Adoption Is Progressing—And Where It’s Stalling A year ago, the AI conversation inside most enterprises was being driven from the top down. Close to half of the organizations TDWI surveyed in early 2025 were under executive pressure to do something with AI. Most were still figuring out what. But at that time, only 24% of respondents said their organization had already deployed generative AI.

Readers will learn from new research into the current state of agentic AI readiness including common strengths and weaknesses. SEATTLE, WA, May 11, 2026—TDWI Research has released a new original research report that explores current levels of readiness for agentic AI. Based on assessment data as well as expert interviews, the new TDWI Benchmark Report, The State of Agentic AI Readiness Benchmark, includes insights that can help organizations assess and improve their own readiness.