How to Turn Your Internal Communications Into Usable Data for AI Agents

In short: Most enterprise AI projects don’t fail because of the AI. They fail because of the data: it’s scattered across ERP, CRM, spreadsheets and a legacy warehouse, with no clear rules on who can access what. A data platform like Databricks puts data, analytics and AI in one governed place. We recommend Databricks because it doesn’t lock you into one AI model or vendor. Its built-in tools, led by Genie, an AI analyst that answers questions about your data in plain language, deliver results in weeks. It also gives you a solid foundation for custom AI built for your business.

Everyone quotes the same statistic: the share of job postings mentioning AI has roughly doubled in a year. But that number alone doesn't tell us much.

A reasonable question before investing in AI infrastructure is why you would need to collect data at all. The models are already trained. GPT, Claude, Gemini: they have ingested more text than any human will read in a lifetime. So why does your company's data matter?

For senior leaders, the challenge is no longer finding information but filtering for signal. In 2026, the right technology event is a strategic resource to understand the roadmaps that will dictate your industry's competitive landscape. To help you prioritize your travel budget, we have selected the eight most critical global events for 2026. These are categorized by their impact on Corporate Leadership, AI Engineering, and Startup Growth.

Most companies experimenting with AI coding assistants like Claude Code, GitHub Copilot, or Cursor end up in the same place: initial excitement followed by growing frustration. The AI generates code quickly, but the resulting codebase becomes inconsistent, full of duplicated patterns, and increasingly difficult to maintain. The problem is not the AI tool. It is how teams are setting up projects before the AI gets involved.

For most senior leaders, the initial excitement around Artificial Intelligence has been replaced by a more sober reality. While almost every company is trying to use AI in some capacity, the percentage of AI projects actually moving the needle on the bottom line remains surprisingly low.

Enterprise AI adoption is at an all-time high — but measurable business impact is not.

Many SMEs launch promising AI pilots that look strong in a demo but break once exposed to real operational data, unpredictable users, and rising costs. The issue is rarely the idea. It is the hidden complexity beneath the surface. Factors like evaluation pipelines, data quirks, workflow integration, and cost dynamics determine whether an AI system can succeed in daily use.

AI is changing how companies innovate, plan, and grow. Discover the 10 most important AI trends for 2025 — and how they’ll impact business efficiency and strategy.