Why CHROs see a workforce readiness gap in AI plans

The AI confidence gap is a readiness gap

Protiviti’s fifth AI Pulse Survey found that almost 80% of C-suite executives expect AI to boost bottom-line performance and strengthen overall revenue over the next three years. Yet only 5% of Chief Human Resources Officers (CHROs) believe AI will support at least half of all HR-related tasks over the same period. “The AI-People Conundrum: Learning to Lead, Not Lag” puts two expectations side by side: business value and the extent to which work will be reorganized around AI.

The distinction matters for investment decisions. CHRO caution about the share of HR tasks supported by AI can coexist with confidence in AI’s potential value. The larger issue is whether jobs, skills, career paths, compensation, processes and management structures can change at the pace assumed by AI plans. An enterprise can expect substantial value from AI while remaining less confident about those workforce conditions.

CHROs report lower workforce readiness

The sharpest differences concern the conditions required for broad adoption. Protiviti found that just 13% of CHROs “strongly agree” that their enterprise’s job designs are ready for mass AI adoption, compared with 28% across the entire C-suite. On learning capabilities, 14% of CHROs strongly agree that their organisation’s capabilities are AI-ready, versus 36% overall. These figures show materially different assessments of current organizational readiness.

Job design matters because AI can change which activities belong together in a role and how much human capacity those activities require. Protiviti argues that organisations may need to rethink how roles are structured, how employees are paid and how careers develop as AI automates or accelerates tasks. Some jobs could become more focused and specialised, while others could require fewer people. Those changes would affect workforce planning alongside deployment of the AI system.

The effects can compound. More specialised roles can require different skills, changing learning priorities and the available internal talent pool. Changes in responsibility can also affect salary structures and career paths. An AI investment can therefore require organizational changes beyond the technology deployment itself.

Learning capability matters when new AI-enabled responsibilities require different skills. An organisation then needs ways to identify those skills and develop them in the people who will perform redesigned roles. The survey shows that CHROs and the wider C-suite assess this condition differently. Establishing whether learning capability affects investment performance requires testing that assumption in the specific deployment.

Fran Maxwell, global leader of People & Change at Protiviti, describes the difference as a “disconnect in the C-suite.” “Most leaders are focused on the value AI can deliver for the business. HR leaders are focused on whether their organisations and people are actually ready to deliver it,” Maxwell said. He argues that organisations need to invest in “people enablement, operating model redesign, and process redesign” alongside technology. An operating model is the way responsibilities, decisions and processes are organized across a business.

Protiviti has a commercial stake in organizational transformation, so Maxwell’s recommendation is the firm’s interpretation of the survey findings. The figures establish differences in executives’ assessments of readiness. Establishing whether job design, learning capability or operating-model constraints have caused specific AI investments to miss their expected returns would require additional evidence. For executives, the findings point to assumptions that may need testing before investment.

Headcount makes those assumptions concrete. If AI allows a role to cover more work, management can increase output, redirect employee time, concentrate responsibilities into more specialised jobs or reduce staffing for some activities. Each choice changes costs and the future shape of the workforce. Workforce planning can therefore affect the economics assumed in an AI investment case.

CHROs still expect broad AI adoption

CHROs still forecast extensive workplace use of AI. Protiviti reports that 82% of CHROs expect a hybrid workforce of humans and AI by 2030, compared with 93% of C-suite leaders. A hybrid workforce here means work performed through a combination of human employees and AI systems. Protiviti also reports that IT leaders are the most optimistic group cited in the findings, with 96% believing in their establishment’s AI readiness.

These measures address different time horizons and questions. Expectations for a human-and-AI workforce by 2030 describe a future state, while confidence in current job design and learning capabilities describes present readiness for mass adoption. High expectations for eventual AI use can coexist with lower confidence in the organization’s ability to make the necessary changes today. Investment decisions need to distinguish between those judgments.

That distinction also changes how executives should interpret forecasts about AI’s reach. Economic viability, reliability, process integration and workforce skills all affect whether technical capability translates into deployed use. Management choices about redesigning the surrounding role matter as well. Forecasts of broad adoption depend on implementation choices as well as technical capability.

AI readiness varies across functions

Protiviti reports different current and expected levels of AI-enabled work across IT, finance, supply chain and audit over the next three years. The measures are worded differently and should be interpreted on their own terms rather than as a single standardized readiness metric. They still show that expectations vary by function. A company-wide assessment can therefore hide important differences in where and how work is expected to change.

For investment planning, executives need to examine the function where work will change. A function expecting AI to affect a large share of its work can face different role, skill and process requirements from one expecting narrower adoption. The key questions are which activities change, how responsibilities should be grouped and whether staffing requirements change. Those answers connect the technology investment to labor costs and operating effects.

Learning, career and compensation systems then need to fit the redesigned work. If a deployment requires different skills, leaders need to assess whether employees can develop them on the required timetable. Changes in responsibility can also alter career paths and pay structures. These conditions become implementation assumptions behind expected returns.

Process design belongs in the same assessment. AI can change how work is divided among employees, systems and managers, including where decisions are made and who remains accountable. A substantial process change can also require a different operating model. Executives therefore need to test whether these organizational changes can happen at the pace assumed by the investment case.

Maxwell argues that HR should help shape AI transformation while technology strategy and investment assumptions are being formed. “AI transformation is ultimately a workforce transformation. Technology alone won’t determine which organisations succeed,” he said. “Capturing AI’s full value will depend on leaders treating workforce transformation as a strategic priority alongside technology transformation. That starts with HR playing a leading role in shaping the roles, skills and organisational changes required to turn AI investments into business value.”

That remains Protiviti’s commercially interested view rather than independently established causation. It does identify a specific governance question executives can test: whether an AI business case depends on new skills, reorganised roles, different staffing levels or process redesign. When it does, HR has information relevant to whether those assumptions are feasible. Bringing that information into investment review lets leaders test labor, skills and organizational assumptions before committing capital.

For CIOs and CTOs, workforce feasibility adds another category to due diligence. Technical feasibility, security, data, integration and economics remain central to an AI investment. Workforce conditions can affect timing, implementation cost and achievable returns when the business case depends on substantial changes to jobs or processes. The appropriate unit of assessment is the specific work being changed and the organization required to perform it.

Key highlights

• Treat AI readiness as a workforce issue: CHROs report significantly lower confidence than the wider C-suite in job design and learning readiness. Leaders should test skills, roles, staffing and operating-model assumptions alongside technical feasibility before committing to AI investments.
• Separate AI adoption expectations from current readiness: CHROs still expect widespread human-AI work by 2030 despite concerns about present capabilities. Investment plans should distinguish between long-term adoption forecasts and the organization’s ability to execute today.
• Assess AI readiness by function: Expected AI use varies across IT, finance, supply chain and audit, making enterprise-wide readiness measures insufficient. Leaders should evaluate the roles, skills, processes and staffing changes required for each specific deployment.
• Include workforce feasibility in AI due diligence: AI-driven changes to jobs and processes can affect implementation costs, timelines and achievable returns. CIOs, CTOs and HR leaders should test workforce assumptions as part of the business case, alongside security, data, integration and economics.