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Your AI agents are only as good as your data governance
As I explore in Stop Predicting: 13 lessons from 1,400 attempts to see what was coming, your data is almost certainly worse than you think. However, the race to deploy AI agents is accelerating. But the speed of adoption will not decide the winners. The real constraint is whether your AI can access data that is accurate, trusted, current, permissioned, and understood. Without that, agents do not create intelligence. They automate ambiguity.
Poor data governance used to create reporting problems. In an agentic organisation, it creates operational risk. AI systems will increasingly search, interpret, recommend, decide, and act across the business. If the underlying data is fragmented, contradictory, or badly governed, those weaknesses suddenly operate at machine speed.
The next phase of digital transformation is therefore not simply about deploying more AI. It is about building the information infrastructure AI can safely act upon.
- https://dynamicbusiness.com/leadership-2/expert/your-ai-agents-are-only-as-good-as-your-data-governance.html
The future fitness move
Treat data governance as strategic infrastructure, not an IT housekeeping exercise. Identify the critical data your future AI agents will depend on, establish clear ownership, clean up conflicting definitions, and decide what machines should, and should not, be allowed to access and act upon.
The other prompts:
Enterprise AI agents are only as reliable as the messiest documents behind them
Enterprise AI has largely been built around context engineering. Teams connect enterprise systems, generate chunks and embeddings, build retrieval pipelines, and assemble the context needed by individual AI applications. While this approach works well for isolated assistants and copilots, it treats enterprise knowledge as application-specific context rather than a shared enterprise asset.
OpenAI Halts AI Training on Advanced Model as It Detects Dark Signs Emerging
An OpenAI agent escaped its training sandbox without OpenAI’s knowledge and coordinated with other agents to launch a cyberattack .
https://futurism.com/artificial-intelligence/openai-halts-training-advanced-model
What CIOs are learning with AI projects and AI outcomes
The gap between AI ambition and AI impact has never been more visible — or more costly. Across every industry, organizations have invested heavily in AI pilots, models, and platforms. Yet for many enterprises, those investments remain stubbornly confined to experimentation, never graduating into durable, measurable outcomes.
https://www.netapp.com/es/blog/ai-projects-ai-outcomes/
Gartner: Low-code tech is projected to grow to nearly $27 billion in 2023
An ongoing dearth of tech talent and an increasing number of business technologists are driving an increase in low-code development technologies, which are projected to total $26.9 billion USD worldwide in 2023, an increase of nearly 20% from 2022, according to a recent forecast from Gartner. Business technologists work outside of IT and create tech or analytics capabilities for internal or external business use.
https://www.techrepublic.com/es/article/gartner-low-code-development-adoption/
AI Doesn’t Just Consume Compute. It Creates a Data Management Problem.
AI applications don’t simply consume the information organizations already have. They create new information every time they’re used. Prompt histories. Inference logs. Vector databases. Generated images, audio, video and documents. Metadata. Security logs. Compliance records. Backup copies. And all of it has to live somewhere.
CIOs are still waiting for AI’s cost savings
Pressure to deploy, not measurable gains, are setting the pace for enterprise AI investments
https://www.ciodive.com/news/CIOs-waiting-AI-savings-ROI/828883/
