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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11The agentic AI mindset starts with the result a person wants, not a list of steps for them to perform. A worker supplies the goal, relevant context and constraints; an AI agent may then handle suitable parts of the execution, while people remain responsible for direction and review. In a December 22, 2025 opinion article for CIO, Warren Wilbee argues that this shift could reshape workflows—but his examples are illustrations, not proof that agents can reliably complete every complex task.
What does “from how to what” mean?
Traditional software often asks people to work through prescribed steps: open a tool, enter information, check a result and move it to the next stage. An outcome-oriented approach reverses the starting point. Instead of telling a system each action, a person specifies what needs to be achieved, provides the context that matters and sets boundaries for the work.
Wilbee frames agentic AI as a way to delegate execution, not responsibility. The system may carry out suitable tasks, but a person still defines the objective and evaluates whether the result is acceptable. The idea is therefore broader than adding an AI feature to existing software: it asks whether the work itself should be organized differently.
How might that change a workflow?
Start with the intended result
First identify the business outcome, rather than choosing a task simply because it appears automatable. Wilbee’s hiring scenario, for example, imagines specifying a role, location and conditions, then asking an agent to research, produce and distribute material and identify candidates. This is an illustrative scenario in his article, not a report of a tested recruiting system or measured hiring results.
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Delegate bounded execution
In supply-chain work, the article describes possible agent-supported activities such as tracking shipments, processing orders, anticipating demand shifts, scheduling production, placing replenishment orders and routing trucks around fuel prices, weather and delivery windows. These examples show the kinds of execution that might be considered for delegation; they do not establish that a particular system performs them reliably in a live operation.
Keep people accountable for direction and review
In Wilbee’s framing, planners set goals and constraints, review what the system produces and refine the inputs when results are not useful. That human role matters because an agent’s output must be judged against operational priorities and real-world limits, not merely accepted because a system generated it.
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What should an organization change before adopting agents?
Wilbee’s argument rests on four connected principles. They are a strategic framework from one CIO contributor, rather than conclusions from a controlled study.
- Prioritize outcomes over features. Ask whether a system advances a defined result, rather than whether a product advertises AI.
- Redesign the workflow. Examine whether execution steps can be removed or changed instead of placing a chatbot or summarizer on top of the same process.
- Redefine roles and skills. Some routine execution may give way to goal setting, orchestration, feedback and review. Wilbee’s approach implies that training and organizational change need to accompany the technology.
- Measure operational results. Select measures that fit the work, such as forecast accuracy, cycle time, disruptions, emissions, efficiency, resilience or sustainability.
Wilbee captures the ambition with the formulation, “The goal isn’t to make tasks faster — it’s to eliminate them.” That is an argument for reconsidering unnecessary work, not a promise that every task can or should be eliminated.
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Define the outcome and a baseline before choosing a system. For a supply-chain project, a team might select forecast accuracy or cycle time as a primary measure, while also tracking relevant trade-offs such as disruptions or emissions. The point is to evaluate the operation’s results, not count AI features or activity alone.
Gartner’s June 25, 2025 release recommends pursuing agentic AI when it can deliver clear value or ROI. It also forecasts that more than 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value or inadequate risk controls. This is a forecast, not a measured cancellation rate.
The same release reports a January 2025 poll of 3,412 webinar attendees: 19% said their organizations had made significant investments in agentic AI, 42% reported conservative investment, 8% no investment, and 31% were waiting to see or unsure. These are poll responses from webinar attendees, not a representative census of organizations. Gartner Senior Director Analyst Anushree Verma said in the release that many projects were early experiments or proofs of concept driven by hype or misapplied.
For a practical decision, assess the proposed use case against the outcome it targets, workflow integration effort, total cost, risk controls, result quality, need for human review and measurable ROI. This is a decision framework drawn from Wilbee’s outcome-and-workflow emphasis and Gartner’s concerns about cost, value and controls; it is not a comparison of particular vendors.
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What the agentic AI mindset does—and does not—claim
Wilbee’s December 22, 2025 CIO article is an opinion piece published through the Foundry Expert Contributor Network. Its contribution is a way to think about delegating work and redesigning processes, supported by hiring and supply-chain examples. It is not a neutral product review, independent evaluation of agent performance or evidence that agents can handle every complex workflow without oversight.
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