When AI adoption outpaces IT visibility

At 1Password, we started expanding our use of AI with a familiar IT playbook. We identified the problems we wanted to solve and the tools that could help us achieve those goals. The plan was straightforward: enable teams, move quickly, learn what worked, and build the visibility needed to manage the cost. Then the operating model changed. AI vendors introduced consumption-based pricing faster than our processes could keep up, leaving us with a distributed system of vendor-specific dashboards to track and manage our AI use. For IT, that created a new kind of chaos when it came to understanding how much we were spending on AI and where that budget was being used throughout the company. We had data spread across systems, but we didn’t yet have a clear, shared answer. How IT teams can govern AI use IT teams are close to the tools and access patterns that shape AI usage. That gives IT an important role in AI spend decisions, and is no small part of why AI governance can become framed as an IT mandate. Budget and model decisions belong with the leaders who set business and engineering priorities, while IT’s role is to provide the context those leaders need. In the face of the changing nature of AI governance, IT teams should focus on finding ways to make AI spend explainable, to give the company a more useful basis for making decisions. Visibility changes the conversation Previously, 1Password’s IT team could see activity in individual vendor consoles, but each view covered only part of the picture. We spent too much time moving between systems and interpreting different definitions. By the time we exported data from one tool and combined it with another, the result was already out of date. When we started using AI Spend and Consumption Management in 1Password SaaS Manager, it felt like a breath of fresh air. We now had a shared view of AI usage and spend across vendors and teams, with detailed insights on users and models, meaning that we could better understand our budget and burn-rate context. The view was immediately more useful than working through disconnected dashboards. The biggest change was the quality of the questions we could ask. For instance, when we saw an increase in spend, we could ask whether it was expected. Was a team working toward a product release? Has someone started a new project? Was a more expensive model being used by default? Was the activity legitimate, or did it require intervention? Before we started using AI Spend and Consumption Management, every one of those questions would have started with a search across various systems. With better visibility, the search became an investigation with a starting point that gave us the context we needed to make informed decisions. The organization defines the operating model We learned very quickly that visibility is just the beginning. We needed to formally define how AI governance would work for our team. AI vendors measure consumption on their own terms, with no uniform system across offerings. One vendor may report usage through credits, while another reports more directly in tokens or dollars. The controls used to manage that usage and spend can be similarly varied, from detailed administrative settings to only broad account-level controls. All of this makes it difficult for IT teams to apply a consistent approach across their organization. If we let each tool define our processes and rules, governance inherently becomes inconsistent. At 1Password, we recognized that we needed to establish our own approach to governing AI use. That process began with answering strategic questions that couldn’t be dictated by the AI tools themselves. Those questions included: Which tools can different teams use? Which use cases require additional review? What data can employees share within AI tools? Who approves a new model? What happens when spending increases unexpectedly? Who decides whether a team should receive more AI budget? The answers to these questions should inform policies and processes that reflect an organization’s priorities on how to govern AI consumption and use. From there, a policy cannot live in a document while every vendor is configured differently. For IT, that means building a repeatable way to turn policy into action through controls, approvals, alerts, reviews, and escalation paths that people can use in practice. Clear ownership across departments and teams is critical for AI spend management. For instance: IT will likely own the systems and implementation Security may define data handling requirements Finance should own budget oversight Procurement may review vendor terms Engineering leaders may set priorities for development tools This is just one example of how this could work in practice. The key is to ensure that each group understands its role before an AI spend issue appears that needs fast resolution. We are still working through what that model should look like at 1Password. AI tools and pricin

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