A few years ago the state of data governance was like the wild west. Since then it has seemingly matured a bit so there is general acceptance in business circles that it is a good idea, though people still seem to struggle with how to implement it, how to define it, and how to prioritize and fund it. In my observation there is a recent move to accept that data governance needs to be in service to business priorities and problems and not just data governance for the sake of data governance. If data governance was the wild west, it seems that AI governance is the wild, wild west. They hype around AI seems to be creating a feeding frenzy to implement it even when there are not clear use cases. It is in everything from Excel and Word to Internet searches to project management and ticketing software such as Jira, Smartsheet, and ServiceNow. The fear is that if you don't implement AI you'll get left behind by the populace that is clamoring for it, whether that differentiator is valid or not. What do you all think? Is data governance reaching some semblance of maturity, or at least adolescence? Is AI governance different enough that it should be handled by different people? Is the demand for AI governance outpacing the ability to understand and implement it? Will they eventually converge? I welcome your thoughts?

Interested to know how much MDG tooling drives governance frameworks and practices?

Wondering if anyone has aligned the two governance approaches in terms of approach and policies etc

From this article. Workiva's 2026 Executive Benchmark Survey just dropped, and the signal is loud: 79% of executives are now prioritizing data automation and governance — not because it's trendy, but because geopolitical instability has brutally exposed their enterprise-wide data gaps. This is a cross-industry survey (finance, accounting, sustainability, audit, operations, legal), and the findings echo what Deloitte recently found in life sciences: chaos doesn't wait for your data to be ready. Key Takeaways: 🔹 Data governance is getting real budget and real teams. 73% report dedicated IT support and 71% have secured dedicated budgets for transformation. This isn't lip service anymore — it's line items. 🔹 AI is delivering, but under supervision. 91% say AI has improved the timeliness and value of their decisions. But leaders aren't going all-in blindly: 76% have internal audit teams testing their AI models, and 65% use AI only in select components of disclosures. The keyword here? Guardrails. 🔹 The C-Suite must converge. This might be the most telling stat: 96% agree that CFOs, CIOs, and CSOs must unite around a shared data governance strategy. Data governance is no longer an IT problem. It's a boardroom problem. The Bigger Picture? When the world gets more unpredictable, your data infrastructure becomes your strategic backbone — or your biggest liability. The organizations that invested in governed, automated data pipelines before the chaos are the ones making faster, better decisions during it. Let's Discuss: 💬 The Budget Test: Your company says data governance is a priority — but does it have a dedicated budget, or is it buried inside "IT maintenance"? There's your real answer. 💬 The C-Suite Alignment Gap: CFO, CIO, CSO — 96% say they must align. What percentage actually do? What's the #1 thing blocking that convergence in your organization?

Hi everyone 👋 I’m Abel, a software engineer currently building AI-powered tracking and automation tools (Notiqo). My focus is on: - Turning behavioral data into actionable insights - Lightweight AI systems for SMEs - Product-driven analytics (not just dashboards, but decision engines) I’m particularly interested in how others here are using AI beyond experimentation — into production environments. What are you currently building with AI that’s actually generating measurable impact?