AI deployment has accelerated, but the systems needed to control and oversee it have not kept pace. Many enterprises now run AI assistants and autonomous agents across several departments, where they connect to internal data and handle work that once required a person.
That creates a basic question with no easy answer: who owns the agent when it makes a decision, changes a system, or starts a transaction? The problem is not only technical. It reaches into approvals, permissions, workflow design, monitoring, and the people responsible for connecting business plans with daily execution.
Approval Lists Cannot Govern Real AI Use
Much of today’s governance infrastructure grew from older assumptions about software use. Those systems were designed to track approved applications and models, but AI now operates inside the applications and workflows employees already use. Governance cannot stop at a list of approved models; it has to show how people and agents use those systems in practice.
Platforms like WalkMe can help governance teams understand actual workflows and AI activity. That visibility matters because the same approval and monitoring process should not cover every AI task. Applying one set of controls to low-stakes assistance and high-consequence activity can create both friction and risk.
The difference between a proof of concept and a production system exposes the pressure. A model may look impressive in a demo, then struggle with fragmented systems, inconsistent data, approval workflows, permissions enforcement, or auditability. In those situations, technical accuracy may matter less than how the full system behaves.
Keith Ferrazzi described the wider problem this way: “AI is surfacing weaknesses in organizational decision-making and creating additional demands on the people responsible for connecting strategy with execution.” Automation does not erase unclear ownership. It can make unclear ownership harder to trace.
Ownership Starts With the Workflow
Before automating a process, an organization needs to understand that process. That means knowing who owns it, where decisions are made, and which activities still require human involvement. Poorly defined approval processes become harder to follow once an agent performs actions across systems.
Access restrictions are central to this work. Much of the value of connecting AI to internal information depends on respecting those restrictions, and different actions carry different consequences. Retrieving a document, editing it, sharing it, and initiating a transaction should not receive identical controls.
Higher-consequence actions need tighter controls. The same principle applies to AI agents that can make production changes without a person checking each step. The share of enterprises trusting AI agents to make production changes on their own has fallen from 75% to 56%, a sign that confidence in autonomous action is under pressure.
The market also shows that adoption does not follow one simple path. Twice as many companies use OpenAI’s agent orchestration platform as Anthropic’s. Yet adoption numbers alone do not show whether those agents work safely, follow permissions, or produce useful outcomes.
Shuhua Xu captured the coordination problem in one sentence: “Agents can be individually correct and still produce a wrong result when they work together.” A group of agents can complete separate tasks correctly and still create a wrong final result when their actions interact.
Measure Results, Costs, and Control
Organizations need measures that reveal what happens after deployment. Useful metrics include human overrides, failed transactions, unauthorized access attempts, model errors, and workflow exceptions. These operational outcomes provide a clearer picture than volume-based adoption figures, such as the number of users or tasks involving AI.
Cost also belongs in the decision. The cost of running agents will rise fivefold by 2028 even as tokens get cheaper, so lower token prices will not settle the question of whether an AI system makes business sense. Resource demands at scale must be part of deployment planning, along with environmental objectives that affect both cost and footprint.
Effective governance requires an inventory of models and agents, their owners, permissions, and review procedures. That inventory cannot sit untouched. Models, integrations, and access requirements change, so governance needs ongoing attention.
A practical control layer combines visibility, identity controls, workflow monitoring, and governance processes. Together, these layers give organizations a way to see what AI is doing, determine who can authorize it, monitor how work moves through a system, and review whether the process still fits its purpose.
The central challenge is not simply deciding whether an organization should use AI. It is deciding who owns each action, what approval it requires, how the result gets checked, and what happens when the system fails. Organizations that combine those controls may be better positioned to scale AI reliably.
Based on
- Who on-calls an AI? This is the unresolved crisis of agent ownership — thenewstack.io
- Enterprise AI is scaling faster than companies can govern it | TechCrunch — techcrunch.com
- AI Orchestration Tools & Agentic Workflows | VentureBeat — venturebeat.com
- The Closed-Loop Production Web: What Changes When Agents Answer Agents — forbes.com
- Why Enterprise AI Agents Require More Than A Technology Strategy — forbes.com
