By the numbers: The study surveyed professionals from 44 organizations.

49% cite a limited understanding of where and how to apply AI in resource management as the top barrier to adoption.
47% point to poor or fragmented data across skills, demand, and capacity.
69% of firms are still in traditional (39%) or experimental (30%) phases with AI, with just 31% having operationalized it.
53% of resource managers feel unequipped to manage hybrid teams of humans and AI agents.
72% say the resource management function in their organization is still viewed as primarily operational, with limited strategic influence.

Zoom in: The majority of resource managers feel unprepared to orchestrate the blended teams of the near future.

Only 4% of respondents feel "well equipped" to manage hybrid human-AI teams, while more than half said they are not equipped at all.
This signals a significant need for new training, playbooks, and workflow changes before AI-assisted staffing can become standard practice.

Between the lines: The push toward outcome-oriented models is compounding the challenge. Resource managers are increasingly expected to staff projects based on performance and results, but most lack the necessary data to do so effectively.

Only 7.3% of respondents said outcome data is easily accessible and routinely used in staffing decisions.
Nearly half reported that outcome data is either known only informally through major wins and failures (24.6%) or is not available to them at all (24.6%).
This is a critical information gap, as nearly three-quarters of respondents (73.4%) said it would be very or extremely valuable to know which combinations of people or AI agents consistently produce strong outcomes.

The big picture: The findings suggest the resource management function itself is at a crossroads. Despite the growing complexity of staffing, it remains largely siloed from strategic decision-making.

The main factors preventing a more strategic role include competing operational demands (49%), a lack of executive mandate (48%), and organizational resistance to change (45%).
This creates a difficult situation where resource managers are asked to adapt to new business models, like outcome-based pricing, without having a seat at the table to help shape them.

See the full Resource Management in the Age of AI report here:

Survey: Firms Unprepared for AI's Impact on Team Management

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