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Companies unlock value from AI agents by redesigning work around measurable business outcomes—not by treating an agent as a software purchase or a standalone pilot. That means deciding which tasks people should do, which agents can handle, and where they should work together; funding the controls and supervision around the technology; measuring actual results; and deciding how verified gains will be used.
This approach is the core of a framework described by The Conference Board in a September 24, 2026 announcement. It draws on interviews, focus groups, and hands-on research with senior HR, talent, and AI leaders. The announcement summarizes the framework but does not provide full methods, sample sizes, or detailed case data, so it is a practical guide—not proof that a particular deployment will save money.
Why agent deployment needs a work redesign
An AI agent’s ability to complete tasks does not, by itself, establish that using it creates business value. The Conference Board’s Allan Schweyer put the distinction this way: “AI agents can complete more and more work, but capability alone doesn’t create value,” the announcement states.
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The shift is from asking, “Where can we use an agent?” to asking, “What outcome does the business need, and how should the work change to achieve it?” The Conference Board cites its C-Suite Outlook 2026 for context: 43.6% of executives identified AI and technology as an investment priority. That figure describes executive priorities, not outcomes from the work-redesign framework.
#1 Best Overall
1. Start with the business outcome
Choose the result the organization needs before selecting an agent or use case. It might be improving quality, reducing the time needed to complete work, lowering costs, or serving more customers. A proposed agent deployment should have a credible connection to that result; novelty or technical capability is not a business case.
Make the desired outcome specific enough to assess later. Identify what will be measured, how current performance will be understood, and who owns the result. Without that starting point, a pilot can show that an agent performs a task without showing whether the business is better off.
2. Redesign tasks before assigning them
Break a process into tasks, then decide whether each should be people-led, agent-led, or shared. The decision should reflect the work and its consequences—not a blanket rule that routine work belongs to automation or that an agent can take over an entire role.
| Allocation | Useful questions |
|---|---|
| People-led | Does the task require substantial judgment, accountability, or sensitivity to customer or employee needs? What are the consequences of an error? |
| Agent-led | Can the task be performed to the required quality, with errors detected and their consequences controlled? Who remains accountable for the result? |
| Shared | Which part can an agent perform, where should a person review or decide, and how will responsibility and handoffs work? |
Assess each allocation against quality, judgment, accountability, the consequences of errors, employee impacts, and customer value. Even when an agent performs work autonomously, people should remain accountable for the outcome. Define who oversees the work and what happens when an agent produces an uncertain, incorrect, or unsuitable result.
Redesign the human role along with the task. Consider the skills and training people will need, the job level at which work should sit, and whether responsibilities or compensation need to change. An agent may alter a role rather than eliminate it; the work allocation should make that change explicit.
3. Budget for the operating system around the agent
The purchase or model fee is only one part of the cost. A realistic plan also accounts for the data, permissions, controls, monitoring, maintenance, and human supervision needed to make an agent work safely and dependably in the organization.
The Conference Board announcement says that, in one illustrative model, direct AI-model use represented 9.4% of an agent’s recurring monthly cost. That is a figure from the announcement’s model, not a general benchmark or a forecast for another company. Its practical implication is to examine the full operating cost rather than assume model usage is the dominant expense.
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4. Measure what people and agents produce together
Track outcomes such as quality, speed, and cost against the business objective. Separate different kinds of gains so a claimed benefit is not confused with cash that actually comes out of the budget.
- Cash savings: Spending that disappears, such as overtime or contractor costs.
- Avoided future costs: Planned spending or hiring that is no longer needed.
- Higher-value work: Employee time redirected to work that produces a measurable result.
Freed-up time is capacity, not automatically a financial gain. It becomes a measurable benefit when the time is used for work that produces a result—for example, a documented improvement in quality or the ability to handle more work. State which type of gain is being reported and what evidence supports it.
5. Decide where verified gains will go
Set expectations before deployment for how verified gains will be used and communicated. The framework identifies possible uses including reducing costs, handling more work without equivalent hiring, reinvesting, or delivering benefits to employees and customers. The right choice depends on the organization’s goals; the important point is to make the decision deliberately rather than leave employees to infer what automation means for them.
Explain what changed, how results were measured, and where the gains are going. Clear communication connects the technical deployment to its effects on customers and employees and helps make the business case understandable.
Rank #4
Protect the learning that routine work used to provide
Routine tasks can be more than workload: they often give early-career employees a way to build judgment and experience. If agents take over that work, organizations need to create other routes to learn. The Conference Board recommends approaches such as mentoring, rotations, supervised practice, and progressively more complex assignments.
HR and IT should work together when automation changes how work gets done. HR can help shape roles, skills, staffing, training, and change management; technology teams can support the tools and controls. The aim is not merely to remove tasks, but to ensure people can develop and contribute in the redesigned work.
Put the five stages into practice
- Name the outcome: Specify the business result and the measures that will show whether it improved.
- Map and allocate tasks: Choose people-led, agent-led, or shared work based on quality, judgment, accountability, error consequences, employee impacts, and customer value.
- Fund the whole operation: Include data, permissions, controls, monitoring, maintenance, and human supervision in the plan.
- Measure the combined result: Track quality, speed, and cost, distinguishing realized savings, avoided costs, and measurable value from redirected time.
- Choose and explain the use of gains: Decide whether they support cost reduction, additional capacity, reinvestment, or benefits to employees and customers, and communicate the decision.
The Conference Board’s announcement names SkillsRight as a nonprofit research collaborator focused on workforce strategy, organizational capability, and change management. Its role as a collaborator does not establish a commercial service or endorsement.
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