AI promises significant advances in patient care and healthcare operations, from improved diagnostics and personalized treatments plans to streamlining administrative tasks. Yet, a clear pattern has emerged in the decisions healthcare technology leaders are making around AI adoption within their organizations. Leaders are grappling with how to balance cutting-edge innovation with the risks and complexities of adopting an AI solution.
Despite the enormous potential of AI, healthcare executives are hesitant to sign contracts with AI vendors that exceed 12 months. This behavior won’t change anytime soon and the pattern is likely to continue for the foreseeable future—at least the next 3-5 years. Let’s dig into why.
A Constantly Shifting AI Landscape
The reason for this reluctance comes down to the rapid pace of change in the AI market. AI solutions are evolving quickly, offering new opportunities and innovations that keep the landscape dynamic and full of potential. In every meeting my team and I have had with clients and prospective customers this year, no healthcare organization has been willing to sign contracts for AI solutions that extend longer than 12 months. This sentiment reflects the uncertainty surrounding many AI products and services.
Since the launch of ChatGPT in late 2022, more and more AI companies and solutions have come onto the scene with each passing week, saturating the market. The waters are muddied, and the pace of innovation is unclear to the CIO, whose chief concern is keeping their hospital up and running and patients as healthy as possible. Making long-term commitments is challenging as today’s market leaders, such as a cutting-edge computer vision vendor for patient rooms, are continually advancing. There is always potential for even more innovative technologies and companies to emerge and drive progress in the coming year.
Executives face a predicament: invest in promising AI tools today or hold off for more stable, long-term solutions and risk being behind the curve. No matter which option healthcare technologists choose, patient care is at risk. Staying up to date on trends and challenges is crucial to making informed decisions, otherwise they risk choosing the wrong platform as innovation continues to accelerate.
Build vs. Buy: The Healthcare Dilemma
In working with healthcare organizations, the build-versus-buy debate is particularly relevant. Some organizations are choosing to develop their own AI models, investing heavily in data scientists, cloud infrastructure, and ongoing maintenance to ensure the AI can grow with the organization to meet specific needs and demands of the business. However, this requires significant investment, both financially and in terms of talent — which, as many in healthcare will attest, is already in short supply.
On the other hand, many healthcare systems are opting to buy pre-built AI solutions to avoid the complexity of developing and maintaining their own. While this may seem like the easier option, it also comes with its own set of challenges. The healthcare AI market is saturated, and executives are concerned about the longevity of the solutions they invest in. Even with robust financial business cases and carefully negotiated contracts, the shifting landscape makes it difficult to guarantee long-term returns on AI investments. Without being able to guarantee the longevity of a pre-built solution, healthcare leaders may fear building their IT network atop a house of cards. The cracks in the foundation may show in as little as three years.
The Challenges Beyond AI
The healthcare industry’s digital transformation is rapidly evolving. More data is collected and generated from various sources, such as electronic health records, medical images, operating room procedures, wearable devices, and genomic sequencing. At the same time, many providers are asked to “do more with less,” as shrinking margins, reduced reimbursements, and skyrocketing operational costs — such as staffing — create enormous pressure.
To stay competitive, IT teams are increasingly seen as a cost center rather than an enabler of innovation. They must balance patient care with technological upgrades, often leading to a backlog of tech debt and delayed projects. In this environment, AI solutions can feel like both a luxury and a necessity. With cloud modernization, hybrid infrastructures, and the growing reliance on data analytics, healthcare organizations are striving to leverage AI without overcommitting.
Moving Forward: AI’s Place in 2025 and Beyond
Despite these challenges, AI will continue to shape the future of technology in healthcare. Healthcare organizations are already implementing edge-AI use cases to transform patient care. These include advancements in predictive analytics, real-time patient monitoring, and even AI-driven drug discovery.
Another player in AI will inevitably emerge and healthcare leaders should keep a close watch on what’s to come in 2025. They need to carefully evaluate not only the immediate benefits of AI, but also the infrastructure required to support it. Cloud optimization, talent development, and risk management must all be top priorities.
In the end, AI in healthcare will thrive only when the tools being implemented today are built to adapt and scale with the industry’s unique needs. From where we stand now, that specialization and dependability may not arrive for several years. For healthcare leaders, the road to realizing AI’s potential may seem daunting, but the rewards for patient care and operational efficiency are too great to ignore.
About Andy Sajous
Andy Sajous is a passionate technologist with over 15 years of solution development, consulting and architecture experience. By trade Andy is a proficient problem solver, leveraging AHEAD’s portfolio to bring relevant solutions to AHEAD’s largest healthcare clients. At AHEAD, Andy is responsible for developing AHEAD’s healthcare solution and go-to-market strategy. Andy is responsible educating customers on emerging technologies, and designing innovative solutions that empower the problems his healthcare clients are looking to solve.
