Operational improvements

The greatest transformation over the next 12 to 24 months will be the transition from labor-intensive operating models to more structured and scalable ways of running laboratory operations, built to sustain performance despite ongoing staffing pressure. This is not about implementing new technology. It is about redesigning how work actually flows through the lab in response to persistent workforce constraints, increasing testing complexity, tighter compliance expectations, and financial pressure.

Labs should be acting now. They should standardize workflows, clarify roles and ownership, strengthen operational governance, and use operational excellence as the foundation for targeted automation. Organizations that do this well will see clear improvements in turnaround time, quality, and workforce stability. Those that do not will continue to struggle, regardless of how much technology they deploy.

-Luis Carvajal, Director, Business Operations UHealth Laboratories, University of Miami Health System, Miami, Florida

A need for expertise

AI and automation are accelerating our understanding of increasingly complex biological systems and diseases, which in turn is driving demand for more advanced and efficient testing. At the same time, staffing shortages and reimbursement pressures are forcing laboratories to become more productive while operating with the same or fewer resources. In this environment, automation has become a critical strategic lever for maximizing investment and improving operational efficiency. AI is also making sophisticated technologies more accessible to less specialized operators, enabling laboratories to address growing complexity while easing workforce constraints. As these trends continue, the laboratory workforce of the future will increasingly require both familiarity with AI and automation technologies, as well as strong research and clinical expertise.

- Yves Dubaquie, Senior Vice President, Diagnostics, Revvity

Future lab insights

One of the many changes likely to have a major impact on clinical laboratory operations over the next 12-24 months is the rapid convergence of artificial intelligence, automation, and workforce redesign. Laboratories are already managing persistent staffing shortages (documented in the ASCP vacancy survey), rising test complexity, expected shorter turnaround times, and heavier quality documentation demands. The practical question is no longer whether or what technology will enter the laboratory workflow, but how well laboratories will integrate the technology without weakening professional identity, trust, or culture.

AI and automation may help stabilize operations by reducing repetitive manual work, improving data visibility, supporting test triage, and enabling laboratory professionals to focus on interpretive, quality, and patient-impacting responsibilities. However, this transition has a psychological dimension that laboratory leaders should not underestimate. Despite a high vacancy rate^1,2, staff may reasonably wonder whether AI will eventually eliminate their jobs⁴, downgrade their job classifications, reroute tasks away from them, or create opportunities for administrators to reduce compensation. If these concerns are left unaddressed, technology adoption can be perceived less as support and more as a threat. 

Laboratories should therefore prepare for AI implementation as both an operational and cultural change. In my paper, Reshaping Organizational Culture in Pathology,³ I emphasized that pathology and laboratory departments undergoing rapid transformation must move beyond traditional hierarchical models and intentionally build cultures grounded in collaboration, transparency, psychological safety, adaptability, and shared purpose. Those same principles apply to AI adoption. Staff are more likely to engage with new tools when leaders explain why the change is occurring, how patient care and quality will be protected, what work will remain human-centered, and how employees will be reskilled rather than simply displaced.

In the next two years, laboratories should prepare by developing clear governance around AI-enabled tools, including validation, competency, oversight, documentation, escalation pathways, and defined limits of use. They should also develop communication plans addressing the human side of implementation. This includes acknowledging uncertainty directly, involving frontline laboratory professionals in workflow redesign, identifying tasks appropriate for automation rather than human judgment, and creating training pathways that help staff move into higher-value analytical, quality, informatics, supervisory, and patient-safety functions.

Laboratories navigating this transition most effectively will be those that treat technology as an extension of laboratory expertise, not a substitute for it. AI may change how work is distributed, documented, and monitored, but it will not eliminate the need for professional judgment, regulatory discipline, quality oversight, ethical decision-making, and accountability. Preparing now means strengthening both the technical infrastructure and the cultural infrastructure required for change. Laboratories should be inspection-ready, data-driven, and digitally capable. Still, they must also remain psychologically safe places where staff understand their future role in an increasingly technology-enabled laboratory environment.

- Darryl Elzie, PsyD, MHA, MLS (ASCP), CQA (ASQ), Manager, Regulatory Affairs, Inova Blood Donor Services

Leadership opportunities

In my MLO article New and current laboratory challenges that impact staffing: What you can do to overcome and reach success⁵ (April 7, 2026), I discuss the financial pressures facing medical laboratories under H.R.1 OBBB, commonly known as The One Big Beautiful Bill. Reduced healthcare affordability and tighter access are projected to leave as many as 14.2 million more Americans uninsured. Combined with lower Medicare and Medicaid reimbursement, these pressures could be the tipping point for already struggling rural hospitals.⁶ They may also prompt hospitals to view laboratory services as a loss leader and consider selling them to outside reference labs. Additionally, I highlighted workforce trends affecting laboratories: as Gen Z enters the workforce and baby boomers retire, staffing, recruitment, and retention are changing in ways laboratory leaders cannot afford to ignore.

Together, these changes point to what I see as the greatest opportunity for clinical laboratories: leadership development. It is discouraging to visit almost any laboratory social media group and see the unchecked expression of negativity and hostility. Some of this may be driven by bots designed to boost engagement by imitating human emotion and, in the process, amplifying harmful beliefs. However, there is often an underlying theme regarding a lack of leadership for addressing issues. Some of what I wince while reading include team members that are bullied by co-workers or other problematic behavioral issues that are reported but ignored, unequal work expectations and favoritism, micromanagement and the refusal to support the team, safety concerns, and even unethical conduct or unprofessional behavior from the supervisors themselves. Many times, these posts end with the questions: “What should I do?” “Should I stay here?” Or even “Should I leave the field of laboratory medicine?” Is this because so many laboratories promote individuals that are high in technical skills without considering if they are strong in interpersonal skills? Is it because our profession is so short staffed that there is no one who will step up into leadership? Or is it a systemic issue within the organization that leadership training is not needed or unnecessary? I suspect all of these problems might be in play, but the bottom line is that without strong leadership, there is not a strong team. I highly recommend that if not provided by their employer, new leaders seek out leadership training that will ensure success. Become active in your professional organizations such as ASCLS or ASCP. Volunteerism is a great way to learn leadership skills in a nonjudgemental way that you can then take back to your organization and shine! There are a lot of resources within these organizations to tap into: read books, listen to podcasts, seek out a mentor, or hire a coach!

-Patty J. Eschliman, MHA, MLS(ASCP)^CM, DLM(ASCP)^CM Director of Laboratory Operations, NKC Health, The Lab Leader Coach, LLC

Insights from UAMS

A challenge impacting clinical laboratories over the next 12–24 months will be the continued mismatch between testing volume and complexity and the available workforce to perform that testing. Laboratories are being asked to do more with fewer experienced personnel, while at the same time managing ever-changing regulatory expectations. We need to establish more University and College based training programs to supply the workforce. Our current programs are doing a great job, but even working at capacity are producing less than 50 percent of what is needed. In the short-term, laboratories may need to shift to non-traditional students and specialized training (e.g. categorical certification) to meet workforce needs.

- Dr. Nathan Johnson, Chair of Laboratory Sciences at the University of Arkansas for Medical Sciences (UAMS)

The workforce shortage is real. I have the opportunity to visit Medical Laboratory Technician (MLT) programs all over the United States and they are doing phenomenal work. To better support these students, our MLT programs need support from local hospitals to host students. There are willing students, but our future workforce pipeline depends on hospitals carving out the time to host them. It's hard because these hospitals are understaffed, but it is something we must do.

-Mr. Jason Key, Assistant Professor/Program Director for online studies at the University of Arkansas for Medical Sciences (UAMS) 

As AI becomes a more affordable and commonplace tool adopted across medical professions, clinical laboratories will continue moving away from physical manipulation of samples and manual bench testing at a more rapid pace.  Over the next 12-24 months, clinical laboratories and the role of the medical laboratory scientist will continue to shift towards more quality management oversight, including increased data analysis and result interpretations, along with increased attention on machine/automation troubleshooting and more interdisciplinary communication and data sharing across the healthcare facility. Labs can better prepare by focusing on creating guidelines for the use of effective AI tools, training department supervisors on how to incorporate such tools into their daily workflows while actively listening to the frontline laboratory staff regarding which tools are working and which are problematic, overly burdensome and/or reduce accuracy, precision and timeliness of test result release back to the ordering provider.

-Mr. Paul Nelson, Associate Professor/Program Director for non-degree studies at the University of Arkansas for Medical Sciences (UAMS)