RMI Market Research: The Emergence of AI in RM

The RMI Survey series conducts short surveys to help professionals compare and contrast their Resource Management operations to their peer groups. Each research survey explores various aspects of running Resource Management operations.

The Emergence of AI in Resource Management

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Artificial intelligence is rapidly emerging as a significant area of interest across Resource Management, creating new opportunities to improve workforce planning, resource allocation, forecasting, decision-making, and operational execution. As organizations explore the potential of AI-enabled capabilities, they are evaluating a wide range of applications, technologies, investment strategies, and adoption approaches to support evolving business needs. This study examines how organizations are approaching AI within Resource Management today, including current adoption patterns, areas of application, organizational readiness, perceived barriers, and future priorities. It also explores how organizations define intelligent Resource Management and what outcomes they hope to achieve through AI-enabled capabilities. This benchmark enables organizations to assess their position relative to peers, better understand emerging trends in AI adoption, and identify opportunities to advance their Resource Management capabilities.

Insights for this study were gathered from a diverse group of professionals, including services executives, resource managers, resource management office (RMO) leaders, project managers, and delivery leaders. Participants represented 106 organizations across a broad spectrum of industries, including Professional and Consulting Services; Enterprise IT; Product Development; Marketing Agencies; Accounting, Audit, Tax, and Advisory Firms; and Law Firms.

For questions about RMI Market Research send us an email at info@resourcemanagementinstitute.com.

Survey Highlights:

  • AI Adoption has Reached the Mainstream, but Maturity Remains Early
    While 60% of organizations report using AI to support Resource Management, most remain in experimentation mode. Half of respondents place themselves in the experimentation stage, while fewer than 15% report systematic use and only 2% describe themselves as AI-native. Adoption is spreading, but most organizations are still determining how AI can be operationalized and scaled across RM functions. Organizations have moved beyond awareness, but enterprise maturity remains limited.
  • AI is Primarily Being Used to Improve Efficiency Rather than Transform Decision-Making
    Reducing manual effort is the dominant driver for AI adoption, selected by more than 80% of respondents. Current AI use cases are concentrated around resource matching, skills inference, and forecasting support. This indicates organizations are initially leveraging AI to automate and streamline existing processes rather than redesigning resource planning and allocation practices. Most AI value today is operational rather than strategic.
  • Data Quality has Become the Greatest Obstacle to Intelligent Resource Management
    Data quality was identified as the most significant barrier to AI adoption, followed by lack of tools, limited expertise, and bandwidth constraints. At the same time, most respondents describe their data readiness as average or below average. This suggests that achieving intelligent Resource Management is increasingly a data challenge rather than a technology challenge. Organizations cannot fully leverage AI without stronger data foundations.
  • Organizations Supporting Internal Customers are Demonstrating Greater AI Maturity than their External Counterparts
    Organizations supporting internal customers report higher levels of systematic AI use, greater maturity, and stronger outcome realization than organizations serving external clients. Internal teams are more likely to report established AI practices and moderate performance improvements, suggesting they may have fewer commercial pressures and greater opportunities to standardize AI-enabled processes before scaling them broadly.
  • Expectations for AI Significantly Exceed Current Results
    Most respondents report only small efficiency gains from AI today, yet nearly two-thirds expect strong or transformational impact within the next two years. This optimism suggests organizations view current adoption efforts as foundational investments rather than indicators of AI’s long-term value. The challenge moving forward will be converting experimentation into measurable business outcomes and strategic workforce advantages.
  • Download a copy of the full report where we break down results by organizations serving external clients to include professional/consulting services, marketing agencies and accounting, audit, tax and advisory firms and organizations serving internal clients including enterprise/IT, product development teams, and engineering.