Dataiku

AI compliance isn't a checkbox. Here's the framework we use with regulated clients. The sharpest thing we've learned working with regulated clients: the rules were never the problem. Visibility was. Data science builds models in one place. Security and risk teams monitor from another. Executives end up accountable for systems they can't fully see into. That's usually where regulated teams get it wrong. They treat compliance as documentation that happens after a model ships, not something built into how it's built. According to "7 career-making AI decisions for CIOs in 2026," based on a Dataiku/Harris Poll survey of 600 enterprise CIOs, 92% have been asked at least once to defend AI outcomes they couldn't fully explain. Here's what embedded compliance looks like in practice: with Dataiku Govern, lineage, risk classification, review signoffs, and audit trails are captured as models move from development to production, not bolted on through spreadsheets and manual review after the fact. The pattern holds across every failure we've studied: disconnected teams, fragmented tooling, governance applied too late. Fixing it starts with giving everyone the same view of what's in production. What compliance requirement surprised your team most? Read the full breakdown → https://lnkd.in/digZicfk

What do CEOs across enterprise software, banking infrastructure, and cybersecurity see when they look at AI risk? A need to deliver value, maintain control, and be ready to explain what AI is doing. AI & Data Insider spoke with our CEO, Florian Douetteau, about the decisions that create lasting risk — from fragile foundations and vendor dependency, to AI adopted without enough discipline. Read the full article: https://lnkd.in/eSck3KuC

AI has moved from a boardroom ambition to a boardroom performance review. According to 7 career-making AI decisions for CIOs report, 98% of CIOs say pressure to demonstrate measurable AI ROI has increased since 2024. And for many, proving the value is only part of the challenge. They also need to explain how AI reached its decisions — across a growing mix of models, agents, data, and tools. The question is no longer simply how quickly an organization can build AI, but whether the CIO can operate it as a coherent, accountable system. Our CEO, Florian Douetteau, explores what that requires — and the fundamentals that separate AI activity from real business performance. Read the Forbes full article: https://lnkd.in/eyMKj2Pg

“A bit like pouring cement around the furniture, then learning the furniture is going to move four times before you finish the house.” That’s how our CEO, Florian Douetteau, describes some of the AI decisions enterprises made in the race to move quickly. Now, 65% of CEOs are more concerned about over-investing in AI than under-investing, and the risks they are confronting extend far beyond the original budget. What happens when a technology decision becomes difficult to unwind? Read the Forbes full article: https://lnkd.in/eyMKj2Pg

Heading to Ai4 2026? Come meet the Dataiku team in Las Vegas. From practical AI governance to building an adaptable AI architecture, our leaders and technical experts will be sharing how enterprises can move beyond experimentation and create AI systems that deliver lasting value. On the agenda: 🎤 𝗧𝗵𝗲 𝗢𝗻𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗧𝗵𝗮𝘁 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗲𝘀 𝗔𝗜 𝗪𝗶𝗻𝗻𝗲𝗿𝘀 𝗙𝗿𝗼𝗺 𝗔𝗜 𝗖𝗮𝘀𝘂𝗮𝗹𝘁𝗶𝗲𝘀 Keynote with Mark Abramowitz and Jed Dougherty August 4 at 8:59 AM Hall A 🎤 𝗧𝗵𝗲 𝗔𝗜 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗧𝗿𝗮𝗽: 𝗢𝘄𝗻 𝗬𝗼𝘂𝗿 𝗘𝗱𝗴𝗲 Solo talk with Catalina Herrera August 5 at 11:20 AM Palazzo Ballroom G Between sessions, stop by Booth #1001 for hands-on demos of AI agents and built-in governance, and explore how Dataiku fits into your technology ecosystem. View the full agenda and request time with the Dataiku team: https://lnkd.in/ee9Fr8mW #Ai42026 Ai4 - Artificial Intelligence Conferences

Enterprise AI’s biggest bottleneck isn’t building more agents, it’s coordinating them. Learn why orchestration is becoming the critical layer for scaling AI with governance, consistency, and control. #AIOrchestration #EnterpriseAI

We're 2 months away from Dataiku Succeed: The AI Success Conference! ⏰ At too many organizations, AI ambition is running headfirst into complexity, disconnected teams, and unclear ROI. We’re done with the hype — it’s time for AI that actually delivers. Join 1,000+ AI, IT, data, and business leaders to learn how People + Orchestration + Governance can turn AI chaos into real, scalable business impact. Here’s what you can look forward to: 📈 Real-World Success Stories: Hear directly from frontrunners at Air Canada, Perdue Farms, Snowflake, and more on how they moved from AI pilots to measurable ROI. 🎯 Actionable Strategy Tracks: Get battle-tested frameworks to empower your teams, unify agent workflows, and embed governance before scaling. 🤝 Peer Networking: Benchmark your strategy against leaders solving the exact same challenges you face. Seats are filling up fast. Reserve your spot early! 🗓️ September 24 📍 New York City 🔗 https://bit.ly/4w1kvPA #DataikuSucceed #EnterpriseAI

When an agent handles thousands of decisions a day, no team can realistically review each one. So the human approval step on the workflow diagram can quickly become a rubber stamp. We think that's why executive trust in fully autonomous agents fell from 43% to 27% in a year, according to Capgemini Research Institute. The models got better and people trusted them less, which says the real gap is governance. The way we've come to see it, human-in-the-loop at scale is an architecture question, not a review queue. You bound autonomy by risk: Agents run freely on low-stakes work, but anything touching customers, money, or compliance needs a human to sign off. You build in exits so an agent knows when to stop, ask, or escalate instead of guessing. And you keep every decision traceable — what did it do, and why. If you're running agents in a regulated industry, where does your human sit? See how governed agentic workflows work at scale → https://lnkd.in/eRMnmdvT #AgenticAI #HumanInTheLoop #Dataiku

AI has the boardroom’s attention. Now it has to deliver across the business. Dataiku has been named a Leader in the 2026 Gartner® Magic Quadrant™ for AI Platforms for Data Science and Machine Learning for the 5th consecutive time. We believe this recognition reflects Dataiku’s continued focus on helping organizations turn AI strategy into everyday execution — connecting teams, workflows, and governance so AI can support decisions across the enterprise. Explore the carousel for highlights, then get the complimentary Gartner report to learn more about Dataiku’s placement for “Completeness of Vision” and “Ability to Execute”: https://lnkd.in/eu-_9vQk #AISuccess #GartnerMagicQuadrant

Every enterprise AI conversation eventually hits the same question: one agent, or many? We used to treat this as technical. It's really a governance choice. Deloitte reports that enterprises are moving quickly from testing agentic AI to deploying it. Our own "7 career-making AI decisions for CIOs in 2026" report found that 87% of global CIOs say AI agents are already embedded in their company's workflows, not sitting in a pilot somewhere. So how do you choose the right architecture? Here's how we think about it: Complexity: Well-defined, sequential tasks favor a single agent. It's faster to build and easier to govern. Cross-functional workflows (fraud investigations, claims processing) push past that. Governance readiness: Most multi-agent failures trace back to coordination and oversight that were never designed in. Can you see what each agent is doing, and why? The point is not to choose single-agent or multi-agent once and call it done. It’s to build an approach that can support both, governed, across the infrastructure your business already runs on. Learn how to choose the right agent architecture for your needs → https://lnkd.in/e3zeuxgf #AIAgents #EnterpriseAI