A deliberate approach to AI adoption helps health systems achieve real impact while upholding safety and trust.
By Srilekha Akula, chief data and AI officer at TRIMEDX
Artificial intelligence (AI) is transforming how people work, learn, and innovate. Generative AI and agentic AI—including large language models—represent a fundamental shift in software infrastructure, redefining the way organizations consider, build, and consume the software.
One survey found 85% of healthcare leaders are exploring or have already adopted gen AI capabilities. Health systems moving to swiftly implement this ground-breaking technology must be intentional about building an AI roadmap that yields the highest impact while ensuring strong safeguards are in place. This is especially vital for the healthcare sector, where patient safety and privacy are top priorities.
Health systems can gain the most value from AI advancements by partnering with organizations that leverage proprietary data to design AI-driven workflows tailored to their unique needs and patient care goals.
At a time when health systems are facing narrow margins, it is imperative to prioritize higher return-on-investment when rolling out AI initiatives. Health systems need to ensure innovation remains grounded in real-world value, supported by internal proof points, and aligned with strategic outcomes—and their vendors and partners should help them succeed under this framework.
The healthcare industry has an opportunity to lead the way in responsible, deliberate AI adoption—and healthcare technology management (HTM) teams can play a key role in this effort. The most successful organizations will be laser-focused on implementing AI in a way that better serves patients, providers, and the broader healthcare ecosystem.
Guardrails Help Organizations Move Faster
AI has the potential to eliminate significant portions of the $1 trillion in wasted health care spending. Health systems should take advantage of emerging technologies now to capture real value and remain competitive. Diligence and clear guardrails are key when adopting emerging technologies or those that are evolving rapidly. Health systems must work within the boundaries of what’s safe and valuable for their patients, workforce, and organization.
Because health systems operate on thin margins and require high reliability, HTM teams should focus on leveraging AI to support these essential priorities. Maintaining high uptime is foundational to prevent revenue loss for the hospitals and maintain a positive patient experience along with seamless operations.
By setting clear parameters, teams can quickly eliminate or deprioritize initiatives that don’t serve the central mission and concentrate on what use cases will provide the most value and comply with the health system’s guidelines. Organizations will build lasting credibility when the innovations are anchored in outcomes and patient safety.
A Framework for Intentional AI Adoption
Health systems should adopt a thoughtful, phased approach when creating an AI roadmap. First, consider what routine, non-strategic tasks could be eliminated, if not automated, to allow the human workforce to focus on more complex and creative jobs. This is especially important as workforce challenges remain a top issue for healthcare executives.
When considering where to incorporate AI-powered automation, organizations should avoid overengineering and only apply what’s truly beneficial.
Next, health systems can look at building and fine-tuning machine learning models to generate predictive insights tailored to specific function. While this would likely be challenging for a health system to do independently, organizations can work with a partner that has the needed expertise. This is the dominant strategy for AI adoption in health care, according to global management consulting firm McKinsey.
Health systems should seek partners who can discern which AI capabilities best address specific business challenges and who can apply machine learning models to deliver measurable value.
A trusted partner will maximize revenue by forecasting device failures, optimizing maintenance schedules, or uncovering patterns in large datasets. The goal remains solving real problems with precision, not deploying technology for its own sake.
Finally, health systems can explore more advanced generative and agentic AI options cautiously. These technologies offer powerful capabilities, but their use should be guided by strategic partnerships with a strong emphasis on safety. Guardrails around data protection, prompt integrity, and auditability must be non-negotiable to ensure innovation remains responsible, secure, and aligned with health care’s regulatory and ethical demands.
An ideal partner will view automation, machine learning, and generative AI as components of a toolkit to be applied intentionally based on the unique needs of a health system.
Proof Points Build Credibility
Establishing proof points will allow health systems to build credibility and clearly see where AI presents real value. There are several opportunities to implement trackable AI initiatives within HTM programs:
- Predictive monitoring and analytics: When health systems implement predictive work systems for their medical devices, they can detect failures before they happen. This allows clinical engineering teams to address issues and repairs before the equipment goes down. Teams can schedule work around patient scheduling to maximize uptime and maximize the revenue per asset.
- Avoidable device damage detection: AI-powered technology can identify preventable errors that occur during clinical use. For instance, improper cleaning and handling of ultrasound probes can cause lens cracks. The failure to use bite blocks on trans-esophageal echocardiography probes can lead to damage or patient safety issues. AI can spot these patterns and alert health systems if the same error is happening repeatedly. Health systems can then implement specific training to prevent ongoing mistakes. This ultimately reduces the cost of replacing damaged equipment.
- Field service report automation: Field service reports are often unstructured and inconsistent. Vendors or technicians may submit PDFs, handwritten notes, faxes, or spreadsheets. Traditionally, someone manually reviews these reports to figure out what was repaired, what parts were used, what tasks were completed, and how much it costs. The process is time-consuming and prone to errors. AI models can read and interpret these varied reports, extract relevant data with precision for billing, analytics, and tracking. This solves a significant operational pain point, reduces overhead, improves accuracy, and allows teams to focus on more meaningful work.
These examples are clear demonstrations of how AI can solve real, recurring problems and build trust to pave the way for further innovation down the road. Practical uses of AI that enhance efficiency without introducing complexity foster trust and empower teams to embrace new technologies.
Strategic Alignment Puts Early Adopters at an Advantage
Health systems can unlock transformative opportunities by collaborating with forward-thinking partners to create the future of health care. Organizations can ask relevant parties what their top priorities are—and then work to shape those solutions alongside industry leaders.
Collaborative innovation allows health systems to harness generative AI in ways that directly address the needs of their HTM teams. By participating in these partnered initiatives, organizations can accelerate adoption of cutting-edge solutions while ensuring the technology is practical, effective, and tailored to their environment.
When industry stakeholders work together, AI efforts will be aligned with the desires of clients, partners, and the healthcare workforce. Health systems that are involved in the creation of AI-powered tools will be at a significant advantage over later adopters.
Intentional innovation is a commitment to safety, credibility, and meaningful impact. As AI continues to evolve, successful healthcare leaders will adopt new technology with purpose—not pressure—grounding each decision in patient safety, operational value, and strategic alignment. By fostering collaboration across the industry, including independent service organizations, OEMs, health systems, and third-party AI partners, organizations can build advanced and trusted solutions to better serve their communities.
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About the author: Srilekha Akula is chief data and AI officer at TRIMEDX. Akula brings over 20 years of technology leadership experience in designing scalable, impact-driven solutions in health care and high-growth digital environments. Before joining TRIMEDX, she served as CTO of Alto and had senior roles at Optum, Amazon, and Google, where her work spanned generative AI, computer vision and AR/VR, API monetization, and other advanced analytics capabilities.
