Executive Summary
Many leaders treat agentic AI and skills-based work as separate agendas, but they are one transformation: agentic AI needs work to be deconstructed into tasks and skills, and redesigning work at scale needs AI. The same decisions govern both.
In The Conference Board C-Suite Outlook 2026, chief human resources officers (CHROs) cited the “impact of automation, including AI” as their most pressing challenge for 2026, and 43.6% of global C-Suite executives named “AI/technology” an investment priority, outpacing all others. The purpose of that investment is productivity, but workforce readiness––not technology––is the constraint.
That readiness is built through work redesign: how leaders redesign work will determine the scope of the AI dividend—the productivity gains freed up by agents—and who receives it: investors, customers, or employees. The technology cannot decide that allocation; leaders must. Gains in the form of lowered expenses flow to investors; gains delivered through better service, speed, or quality flow to customers; gains achieved as reduced workload flow to employees. A successful transformation delivers dividends to all three.
This report provides a five-stage framework for how CHROs should manage the transformation over the next 18 months, defining the strategic choices required to turn agentic AI and work redesign into measurable workforce and business value.
Trusted Insights for What’s Ahead®
The following insights are the five-stage framework this series builds out: five decisions and commitments that govern the transformation. The first two are sequential—pick the outcome, then redesign the work; the remaining three are standing commitments, determined before deployment.
- Stage 1: Start with a few desired business outcomes. Identify work where improvement would contribute most to these outcomes. Deploy agents where they fit a strategic purpose, not a technology purchase, and build an enterprise-wide skills inventory connected to solving business problems.
- Stage 2: Allocate work before you deploy agents. Examine and redesign the identified work task by task. Decide which work belongs with AI alone, which requires people working with AI, and which should remain with people. Name one person accountable for the workflow’s result and for how agents are used within it.
- Stage 3: Build the infrastructure, governance, and budget that agents require. Provide the data, permissions, quality checks, decision rights, and funding agents need. Build the trust and skills workers need to use and manage them.
- Stage 4: Measure what people and agents produce together. Move beyond head count and cost-per-employee to measure improvements to existing work and results that redesign makes possible, using methods business and finance leaders can support such as the value and full cost of the combined work of people and agents.
- Stage 5: Decide where the gains go. Plan how expected gains will be used, then confirm or revise those plans when results are verified. Choices include reducing costs, handling more work without equivalent hiring, reinvesting in employees or growth, or combining these approaches. Protect early-career pathways and make redesigned work sustainable.
What is an AI agent?
An AI agent is a system that can pursue a defined goal across multiple steps and take or recommend actions within specified controls. It can use approved tools and data, track what it has already done, and use those results in later steps. An HR service agent, for example, might read an employee’s request, check the relevant policy, gather missing information, and send the case for approval. Its permissions would determine which records it could access and which actions would still require a person.
About the Research
Findings draw on 16 primary research sessions conducted February–July 2026, involving more than 75 senior HR, talent, and AI leaders—12 in-depth executive interviews and four focus groups, including an expert panel convened June 25, 2026—plus several sessions from The Conference Board Councils, a three-hour hands-on Member workshop, and a governed AI-workflow simulation platform used by more than 100 Members.
Executive Summary
Many leaders treat agentic AI and skills-based work as separate agendas, but they are one transformation: agentic AI needs work to be deconstructed into tasks and skills, and redesigning work at scale needs AI. The same decisions govern both.
In The Conference Board C-Suite Outlook 2026, chief human resources officers (CHROs) cited the “impact of automation, including AI” as their most pressing challenge for 2026, and 43.6% of global C-Suite executives named “AI/technology” an investment priority, outpacing all others. The purpose of that investment is productivity, but workforce readiness––not technology––is the constraint.
That readiness is built through work redesign: how leaders redesign work will determine the scope of the AI dividend—the productivity gains freed up by agents—and who receives it: investors, customers, or employees. The technology cannot decide that allocation; leaders must. Gains in the form of lowered expenses flow to investors; gains delivered through better service, speed, or quality flow to customers; gains achieved as reduced workload flow to employees. A successful transformation delivers dividends to all three.
This report provides a five-stage framework for how CHROs should manage the transformation over the next 18 months, defining the strategic choices required to turn agentic AI and work redesign into measurable workforce and business value.
Trusted Insights for What’s Ahead®
The following insights are the five-stage framework this series builds out: five decisions and commitments that govern the transformation. The first two are sequential—pick the outcome, then redesign the work; the remaining three are standing commitments, determined before deployment.
- Stage 1: Start with a few desired business outcomes. Identify work where improvement would contribute most to these outcomes. Deploy agents where they fit a strategic purpose, not a technology purchase, and build an enterprise-wide skills inventory connected to solving business problems.
- Stage 2: Allocate work before you deploy agents. Examine and redesign the identified work task by task. Decide which work belongs with AI alone, which requires people working with AI, and which should remain with people. Name one person accountable for the workflow’s result and for how agents are used within it.
- Stage 3: Build the infrastructure, governance, and budget that agents require. Provide the data, permissions, quality checks, decision rights, and funding agents need. Build the trust and skills workers need to use and manage them.
- Stage 4: Measure what people and agents produce together. Move beyond head count and cost-per-employee to measure improvements to existing work and results that redesign makes possible, using methods business and finance leaders can support such as the value and full cost of the combined work of people and agents.
- Stage 5: Decide where the gains go. Plan how expected gains will be used, then confirm or revise those plans when results are verified. Choices include reducing costs, handling more work without equivalent hiring, reinvesting in employees or growth, or combining these approaches. Protect early-career pathways and make redesigned work sustainable.
What is an AI agent?
An AI agent is a system that can pursue a defined goal across multiple steps and take or recommend actions within specified controls. It can use approved tools and data, track what it has already done, and use those results in later steps. An HR service agent, for example, might read an employee’s request, check the relevant policy, gather missing information, and send the case for approval. Its permissions would determine which records it could access and which actions would still require a person.
About the Research
Findings draw on 16 primary research sessions conducted February–July 2026, involving more than 75 senior HR, talent, and AI leaders—12 in-depth executive interviews and four focus groups, including an expert panel convened June 25, 2026—plus several sessions from The Conference Board Councils, a three-hour hands-on Member workshop, and a governed AI-workflow simulation platform used by more than 100 Members.
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