By Joshua Shale
This is Part 1 of a four-part series on the trends shaping identity management and data in 2026. We reviewed more than a dozen industry reports — from IAM vendors and security analysts to data platform leaders like IBM and Accenture — looking for patterns that showed up again and again, not just within one industry, but across both. Each post in this series covers one of those shared trends and explains why it matters for your business.
Identity teams and data teams are usually worlds apart. But in 2026, both are converging on the same problem: what do you do when the “user” accessing your systems isn’t a person anymore? Autonomous AI agents are now logging in, pulling data, and making decisions on their own, and neither field was built for that.
• Non-human identities now outnumber human ones, often by a wide margin. APIs, bots, service accounts, and AI agents already exceed human user accounts in most companies — by some estimates, 20 to 50 times over. Many of these accounts are unmanaged and invisible to traditional security tools, which makes them an easy target for attackers. Treating every machine and agent as its own accountable identity, not an afterthought, is quickly becoming standard practice.
• Agentic AI is forcing identity systems to handle delegation, not just logins. When an AI agent acts on behalf of a person or a business process, someone still needs to answer for what it did. That means giving agents their own verifiable identities, time-limited permissions that expire when a task ends, and a clear chain back to a human owner. The agentic AI market is projected to grow from roughly $5 billion in 2024 to over $40 billion by 2030, so this isn’t a niche concern for much longer.
• On the data side, AI agents are starting to run the pipelines themselves. Instead of people manually cleaning, tagging, and moving data, agentic systems are increasingly doing that work — flagging anomalies, enriching records, and adjusting workflows in real time. That speeds things up, but it also means data quality and governance decisions are being made by software with far less human review than before.
• Both fields are converging on the same fix: oversight and accountability, built in from the start. Whether it’s an AI agent requesting access to a system or an AI agent processing a customer’s data, the organizations getting this right are the ones building in human checkpoints, audit trails, and clear ownership before they scale up — not after something goes wrong.
The takeaway: AI agents are no longer an edge case for either identity or data teams. Both fields are being asked to answer the same question — who (or what) is acting, on whose authority, and how do we know — and the businesses that answer it now will have a real head start.