Technology amplifies what already exists. If the underlying intent is clear, it accelerates outcomes. If the intent is ambiguous, it amplifies confusion.
When AI suggests changes, that context has to be inferred, and that introduces uncertainty.
Anyone who has held the CIO position recently knows the role requires enormous time brokering between parties whose interests do not naturally align.
As AI agents take on higher-stakes customer interactions, organizations are discovering that trust, accuracy and governance—not automation alone—will determine success in the agentic age.
The next stage of identity security will not be defined by how we manage humans, machines or agents as separate categories. It will be defined by whether we can govern the chains that bind them.
Engineering thinking can close the gap between AI experimentation and organization-wide execution.
What the industry needs is a layer for verified product information.
Companies often chase AI capabilities before defining the operational problems they are actually trying to solve.
Many companies are using AI to automate tasks, cut costs and speed up existing workflows, but that approach risks missing the much bigger opportunity.
Snowflake’s quiet Natoma buy, alongside a $6B AWS deal, reveals its real ambition: governing what AI agents do, not just storing the data they reach for.