As organizations adopt artificial intelligence (AI), machine learning, and automated workflows, leaders face increasing pressure to demonstrate measurable returns on those investments. While much attention focuses on the cost of new technologies, workforce-related expenses can significantly affect overall return on investment (ROI). According to Gartner, organizations often underestimate these hidden personnel costs when planning AI initiatives.
A recent Gartner survey of 469 active CEOs found that 88 percent of organizations plan to increase investments in AI. Despite this widespread commitment, many organizations overlook the workforce implications of AI adoption. For laboratory managers overseeing AI-enabled workflows, understanding these potential costs can help inform decisions about staffing, budgeting, and long-term workforce planning.
Identifying workforce risks that affect AI return on investment
According to Jan Bansch, a senior director analyst in Gartner's HR practice, hidden workforce costs can substantially reduce the return organizations realize from AI investments. Gartner identifies three primary workforce cost risks that organizations should consider.
The first is the high cost of acquiring specialized AI talent. Professionals with advanced AI and computational expertise often command compensation packages three to four times higher than those of the average employee. At the same time, Gartner notes that technical skills may remain relevant for only two to five years as AI technologies continue to evolve. Organizations may therefore find themselves paying premium salaries for expertise that requires continual updating.
A second challenge involves performance management. As AI tools increase employee productivity, organizations that continue using existing pay-for-performance models may unintentionally reward gains driven primarily by technology rather than individual contributions. Reviewing productivity metrics and incentive structures can help ensure compensation remains aligned with employee performance.
Considering the long-term impact of workforce reductions
As AI automates some routine work, organizations may consider reducing staffing levels. However, Gartner predicts that by 2029, up to 30 percent of employees displaced by AI will be rehired by their former organizations, often at a higher cost because of increased competition for skilled talent.
Reducing entry-level positions may also affect an organization's long-term talent pipeline by limiting opportunities to develop future technical and leadership talent internally. For laboratories that rely on specialized scientific expertise, managing the hidden costs of staff turnover and maintaining a sustainable workforce pipeline may become increasingly important as AI adoption expands.
Applying Gartner's guidance to laboratory workforce planning
Although Gartner's research focuses broadly on workforce strategy rather than laboratories specifically, its findings offer several considerations for laboratory leaders implementing AI technologies.
Laboratory managers working with HR and organizational leadership may want to evaluate workforce planning alongside AI implementation rather than treating AI solely as a technology investment. Doing so can help organizations better understand the full costs associated with adopting new automation tools.
Key workforce planning considerations include:
- Evaluate compensation strategies for specialized AI and automation talent
- Review productivity metrics and incentive programs as AI changes how work is performed
- Consider long-term workforce development before eliminating entry-level positions
- Plan for ongoing training as AI-related technical skills continue to evolve
By incorporating workforce planning into AI implementation strategies, laboratory leaders can develop a more complete understanding of AI return on investment and make more informed decisions about staffing, training, and operational budgets.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
