Resource Management Is Breaking—and AI Won’t Fix It Alone

The Research Management Institute has published a study, commissioned by Kantata, “Resource Management in the Age of AI“. The research is based on a quantitative survey answered by individuals from 44 different organisations. The survey included 17 questions. The report begins with a list of five key observations. With the rest of the content dedicated to providing data visualisations of the gathered responses. Unusually, there is very little in the way of commentary or analysis from the report. There is also no qualitative element.

However, attendees at the seventh annual Resource Management Global Symposium in Indianapolis from April 20–22 can hear more. There will be a session on Tuesday, April 21, at 13:00 titled ‘The End of ‘Who’s Available?”.: What Resource Management Will Look Like in 2028 — and How to Get There.

The session will draw on the research findings. It will examine how AI, changing client expectations, and outcome-based delivery, are reshaping the composition of the ideal team. It will also cover what resource management leaders need to do now to prepare.

There is little doubt that AI is set to transform resource management. Yet only 49% have a limited understanding of where and how AI should be applied in resource management. Only 4% say they feel well-equipped to manage hybrid teams of human and AI agents. The challenges or gaps that currently exist are across readiness, visibility, and execution.

Besides the lack of understanding, there are real barriers to adoption, including:

• Poor data quality or fragmented data (skills, outcomes, demand, capacity) – 47%
• Technology limitations (systems not designed to support AI-driven or hybrid staffing) – 41%
• Insufficient AI proficiency or training for resource managers – 26%

Early days for AI usage in Resource Management

RMI expressed their view saying, “While interest in AI-augmented resource management is high, most organisations remain early in their maturity, operating with traditional, utilisation-centric models and limited readiness to orchestrate hybrid human and AI teams.”

The survey shows that most organisations are still early in AI‑enabled resource management. 69% remain in traditional or experimental modes with limited or pilot use of AI. Only 31% have moved beyond experimentation to operationalise hybrid, data‑driven, and outcome-optimised resource management.

Not all respondents believe that AI agents will become part of their organisation, with 32% saying they do not expect agents to be assignable resources and 19% believing they are not yet applicable today. However, vendors like Kantata are developing agents that can help fulfil tasks within specific roles, increasing automation. Agentic AI is in its early phases, but the technology is now proven, and the early adopters could see significant gains.

That there is a need for a greater understanding of what Agentic Agents can achieve is clear.

The shift to outcome-based business models

Another significant area of study in the report was value- or outcome-based pricing. Resource managers are finding it difficult to do so without access to data regarding outcomes. Only 3% of organisations have adopted outcome-based pricing, and 16% are very likely to adopt it. 50% were unsure, and 31% were either not very or not at all likely.

However, if it is adopted, the study highlights five implications for resource managers.

• A shift from utilisation to outcome metrics
• Greater emphasis on skills and team composition
• Increased planning complexity and flexibility
• Higher cost, margin and risk sensitivity, leading to greater oversight for resource managers
• Greater strategic influence of Resource managers as their work becomes more cross-functional

Outcome intelligence is seen as critical to staffing decisions. 73.4% say it would be highly valuable to know which combinations of people or AI agents deliver the strongest outcomes, and 50.9% say proven outcomes already strongly influence staffing for new deals.

Can the importance of Resource Management Grow

The RMI is dedicated to improving the quality and value of resource management within organisations. However, it falls short of being a strategic enabler at the moment, despite the potential highlighted by the study. In most organisations, it remains operational rather than strategic (72%). The respondents were asked about the barriers to having a greater role.

• Competing operational demands (49%)
• Lack of executive mandate or influence (48%)
• Organisational resistance to change (45%)
• Insufficient data or insight (44%)
• Limited tooling or systems (41%)

The RMI noted, “Resource management continues to be viewed largely as an operational function, constrained by fragmented data, unclear AI application models, and insufficient outcome visibility.

“At the same time, the results signal a clear aspiration shift: resource management professionals are seeking more data-driven, outcome-aware, and strategically influential roles, where skills intelligence, forecasting accuracy, and proof-of-delivery impact become as critical as capacity and utilisation.”

For Kantata, which offers the promise of agentic agents, greater visibility, and a modern PSA solution that supports automation, this report offers a mix of concern and hope.

Sarah Edwards, Chief Product Strategy Officer at Kantata, commented, “Professional services firms are entering an era where the ideal team is no longer defined only by human availability or role fit.

“Resource managers are increasingly being asked to weigh skills, outcomes, economics, and the role of AI in delivery — often without the data foundations or workflow support to do that confidently. This research underscores both the scale of the opportunity and the operational gaps organisations still need to close.”

Enterprise Times: What does this mean

The survey posed some interesting questions, and the answers were comprehensive and provided valuable insights. Digital Workers are coming to professional services. However, not everyone wants Digital Workers, and not every organisation is ready for them. Certainly, organisations that are still using spreadsheets or disconnected systems are likely to face adoption issues.

Organisations need data, processes, technology, and people aligned to ensure their AI adoption journey succeeds. In other words, AI-powered resource management is unlikely to succeed without the right foundation in place. It does, however, offer the promise of not just fixing resource management, but elevating it to a strategic level of importance.

There are some interesting findings in the report; these will be even more interesting next year if the survey is repeated and progress in attitudes is measured. AI is changing the world quickly, and professional services organisations are not immune to that change.

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