Dataiku

If the person reviewing an agent’s work can’t keep up, what exactly are they approving? Mike Anderson’s point about human fatigue in a recent InformationWeek piece stood out to me. Someone can be formally responsible for an agent and still spend their day clicking “approve” to clear the queue. Dataiku’s recent report with The Harris Poll, The Global AI Confessions Report: CIO Edition 2026, which surveyed 685 global CIOs, found that 84% say employees are building agents and applications faster than IT can govern them. And just 21% of CIOs have full visibility into AI agent and AI workload costs across different use cases and business units. Trying to close that gap through manual review alone puts an unrealistic burden on the people responsible. What companies need is a platform that supports ongoing monitoring, sets clear boundaries and provides a workable path for intervention. This is part of why we built Dataiku Agent Management: to bring agent ownership, usage, cost, quality and risk information together across platforms. It gives teams a shared basis for deciding where attention is needed. As we give agents more autonomy, we need to be just as deliberate about how people supervise them. Otherwise, approval becomes a formality. Great article by Carrie Pallardy that's definitely worth a read: https://lnkd.in/eN7xksMu I’ve added more on Agent Management in the comments.

My keynote at Dataiku Succeed came down to one point: Vibe coding is becoming the next generation of Excel workbooks. Fast. Useful. Hard to inspect. The answer isn’t stopping business teams from building. It’s giving them a governed way to build under IT oversight. At Succeed, we expanded Cobuild to support that: • Dataiku Headless: Build and manage Dataiku projects from tools like Claude Code, Codex, and Cursor - coming soon. • Agent Management: Discover, monitor, and improve agents across the enterprise, wherever they were built - available now. More people building is inevitable. The real question is whether they’re building in a system you can trust. More on what we shared at Succeed: https://lnkd.in/eprXtXXN #DataikuSucceed26

The countdown is on for the Dataiku Summit London! Here's what I'm looking forward to most: Vodafone's AI Opportunity: Paul Blair reveals how rapid prototyping uncovered a single IT testing use case projected to save £8 million. Scaling GenAI at Aviva: Shantanu Deo and Chris Butler explore how AI agents are transforming internal auditors into data analysts. Platform Migration at L&G: Sara Budd shares practical lessons on driving user adoption and managing change when moving to Dataiku. Beyond the tech, it’s going to be a great space for high-level industry conversations and sharing market insights on how companies are truly scaling AI today. If you're in London, it would be great to see you there! Register via link here: https://lnkd.in/egMYjTF2

The board asks you, “So what did you get out of your investment in artificial intelligence?” ⠀ If you were the CIO in that meeting, how would you answer? ⠀ Support uses an agent to write answers for customers. Finance uses an app to help reconcile accounts. Operations uses an agent to identify shipping delays. ⠀ The teams say these tools are helping, but some agents were built outside of IT. And each team has a different definition of "success." ⠀ Since there's no way for the CIO to combine the local results of these initiatives to give a complete picture of the total cost/risk/value to the company, she'll have to say, "I believe we're getting value, but I don't know exactly how much." This accountability gap is something I've seen in Dataiku's Global AI Confessions Report: CIO Edition 2026. The report includes results from a Harris Poll survey of 685 global CIOs. In the survey, the CIOs confessed that: ⠀ - 84% say employees are creating AI agents and applications faster than IT can govern them. - 86% believe their compensation will be linked to measurable AI outcomes. - 76% believe their CIO position may be at risk if their company fails to generate measurable business benefits from AI by the end of 2027. - 88% believe their success with AI will affect their professional reputation or career. ⠀ 𝗪𝗵𝗮𝘁 𝗲𝗮𝗰𝗵 𝗮𝗴𝗲𝗻𝘁 𝗻𝗲𝗲𝗱𝘀 For architecture teams, this starts with three things: - A named owner. - Clear access limits. - A clear way to measure success. When an agent goes into production, these three elements need to remain linked. ⠀ 𝗪𝗵𝗮𝘁 𝗜 𝘄𝗼𝘂𝗹𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 I would track the following for the support agent mentioned earlier: - The number of cases resolved correctly. - The amount of time it takes to review cases. - The cost per resolution of each case. Those numbers would help me decide whether to give the agent more responsibility. ⠀ 𝗪𝗵𝗲𝗿𝗲 𝗗𝗮𝘁𝗮𝗶𝗸𝘂 𝗳𝗶𝘁𝘀 Business units should continue to create new apps while embedding ownership and performance measurement into that creation process. Dataiku Cobuild offers teams the opportunity to convert business objectives into project plans that can be reviewed and approved. ⠀ Dataiku Cobuild helps teams turn business goals into AI projects they can inspect and approve. ⠀ The CIO needs evidence she can explain: what improved, what it cost, and where to invest next. ⠀ You can download the full Global AI Confessions Report , CIO Edition 2026 here: https://lnkd.in/dy5S9hQ2 ⠀ If your board asked you what AI produced for your organization last year, what is the single number you would want to show them? Thanks to Dataiku for supporting this post.

Recognized by Gartner®, we believe Dataiku helps organizations embed AI into decisions and operations to drive meaningful business outcomes and AI success. Explore the report to learn more.

Recognized by Gartner®, we believe Dataiku helps teams move from models to production faster and deliver AI success across real-world workflows. Explore the report to learn more.