AI agents can act across tools and workflows, but that capability alone does not guarantee useful business results. Organizations need to redesign the systems around them: clarify process ownership, standardize data, define what an agent may decide, route exceptions to people, and measure outcomes across the workflow.
Why an AI agent is only the starting point
A conventional AI tool responds to a prompt. An agentic system can initiate work and coordinate actions across systems and people. That shift matters, but it also makes the surrounding process more important: an agent can only work reliably when it has access to usable information, a defined task, and clear authority.
John Samuel’s September 17, 2026 article in The AI Journal argues that many organizations add AI to processes originally designed for people without rethinking how those processes operate. Call-center automation is a familiar example of technology being applied without sufficient system redesign. The issue is not simply whether the model can perform a task; it is whether the workflow, governance, and people around it make the task repeatable and accountable. The AI Journal article
Samuel puts the distinction succinctly: “Knowledge without system is just potential, and potential doesn’t show up on a balance sheet.”
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What needs to change around an agent
Before assigning work to an agent, an organization needs to establish the conditions that let it act without turning routine variation into unmanaged risk. These are design considerations, not a guarantee of performance.
Give the process an end-to-end owner
Someone must be accountable for the whole workflow, not just one team’s step. Without that owner, teams may maintain incompatible process variants, and no one may be responsible for resolving delays or recurring exceptions across handoffs.
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Make the data consistent and accessible
Agents depend on information that can be interpreted consistently across systems. Standardized data and clear access are more useful than simply connecting more tools: inconsistent records can lead to inconsistent actions.
Separate the normal path from exceptions
Identify which work follows a repeatable path and which cases need judgment. The agent can handle suitable routine steps; exceptions should be visible and routed to a person rather than silently treated as ordinary cases.
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Set decision boundaries and escalation rules
Specify what the agent may do autonomously, what requires approval, and when a person takes over. A handoff needs to make the relevant context and reason for escalation clear enough for the human to act.
Measure the workflow, not just agent activity
Count outcomes that matter to the process, rather than treating the number of agent actions or deployments as evidence of value. The measure should reflect the workflow’s intended result and help its owner identify where performance or exceptions need attention.
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How the customer-onboarding example illustrates the problem
Samuel’s article uses a hypothetical onboarding workflow to show why adding an agent is not the same as fixing a process. The imagined agent encounters inconsistent data, different procedures across teams, a growing number of exceptions, and no end-to-end owner. In that environment, the system gives it neither a stable path nor a clear authority structure.
The article’s proposed redesign is to clarify ownership, standardize data, map the normal process, and define the boundary between agent decisions and human decisions before deployment. This is an illustration, not a reported experiment or verified case study; the article provides no measured result for the scenario. The AI Journal article
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What adoption and outcome figures do—and do not—show
A Harvard Data Science Review article reports that McKinsey’s 2025 survey found 78% of enterprises used generative AI in at least one function, while more than 80% reported no material contribution to earnings. These are attributed 2025 survey results, not measurements of 2026 adoption or proof that agents themselves caused the earnings gap. Harvard Data Science Review
The HDSR article also describes practitioner-reported examples, including reduced time for audit reporting at an industrial firm and a B2B sales workflow. Those examples show what practitioners have reported, not what every organization should expect. The article says systematic replication studies are still needed, so a result from one organization should not be treated as a forecast for another. Harvard Data Science Review
How to assess an agent workflow proposal
When comparing proposals, look at the design of the workflow rather than only the model or the list of connected applications. Ask:
- Who owns the process from start to finish, including handoffs?
- Is the necessary data standardized and accessible to the agent?
- Which tasks follow a stable, repeatable path?
- How are exceptions identified, explained, and routed?
- Which decisions can the agent make, and which require human approval?
- When does a person intervene, and what context will they receive?
- Which workflow-level outcomes will show whether the system is working?
A proposal with crisp answers to these questions is easier to govern and evaluate than one that relies on a general promise of autonomous work. The criteria help compare designs; they are not a validated universal scoring system.
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