Artificial intelligence (AI) in the healthcare sector is not a singular narrative; rather, it comprises three distinct stories that are advancing at varying paces, a detail often overlooked in mainstream discussions.
In the realm of hospitals and health systems, AI is steadily integrating into daily operations. It assists with essential administrative duties such as documentation, information summarization, and scheduling, all of which typically consume significant time for healthcare professionals. However, the integration of AI in areas like diagnosis and treatment decisions is approached with more caution.
Within the medical technology (medtech) field, the transition involves both regulatory and technical considerations. The FDA currently lists over 1,500 AI-enhanced medical devices, and regulatory bodies are developing frameworks that allow certain AI products to evolve post-launch, within specific, vetted parameters. This is crucial because AI technology is ever-evolving, while regulations for medical devices have traditionally focused on slower-shifting products.
In contrast, the pharmaceutical and biotech industries are following a significantly different trajectory. AI can significantly fast-track processes related to target identification and molecule discovery. However, it cannot bypass the lengthy phases of clinical testing, safety assessments, and regulatory evaluations that follow. While AI may expedite one segment of the development cycle, it doesn't eliminate the entire process.
Understanding this distinction is essential as the focus shifts toward demonstrating the value of AI implementations.
According to PwC's 2026 Global CEO Survey, 56% of CEOs reported that their organizations had not experienced increased revenues or reduced costs due to AI. This does not indicate failure; rather, it underscores that experimentation and value realization are not synonymous.
The pressing question has transitioned from "Can we adopt AI?"—a challenge many organizations have already met—to "Should we implement it in this context, and what quantifiable outcome can we expect?"
Although this might seem straightforward, it's often where AI projects falter. Many companies mistakenly start with a technological solution in search of a problem; the more effective strategy is to identify the business challenge first.
Before committing to an AI project, clarity on three critical questions is vital:
1. What specific business challenge are we addressing? 2. What metric should improve if the implementation is successful? 3. Who in the organization is responsible for that metric?
If these questions cannot be clearly answered, the project may not be ready for investment.
It's essential to focus on measuring true outcomes rather than just activities. Metrics like "hours saved" or the number of active users do not equate to business value. In healthcare, we should prioritize factors such as cycle time, error rates, quality, revenue, margins, inventory management, customer or patient experiences, along with risk and compliance.
Post-launch, it's also critical to revisit the projected results after 90 and 180 days to assess their alignment with the original business objectives. If the expected metrics haven't shifted, it may be time to revise the solution or withdraw funding. Despite strict capital allocation criteria for technology investments, many organizations lack similar rigor in evaluating their effectiveness.
Currently, some of the quickest benefits from AI in healthcare stem from processes that are repeatable, data-rich, and rules-based. Areas such as supply-chain forecasting, inventory management, quality assurance in manufacturing, predictive maintenance, customer support, clinical documentation, knowledge retrieval, and compliance workflows exemplify this trend.
Furthermore, the human aspect cannot be neglected. Employees may feel anxious about AI's implications for their jobs, and reassurances like “AI will never take your role” are often viewed as unconvincing.
Typically, jobs comprise two categories: repetitive tasks and those requiring human insight. AI excels at automating the former. In healthcare, this translates to reducing the documentation burden for nurses, minimizing data sorting tasks for researchers, or lessening paperwork for quality engineers. The real opportunity lies in providing professionals more time for tasks that demand their unique skills: judgment, problem-solving, creativity, empathy, and relationship-building.
Organizations must also simplify the adoption of safe practices. If employees lack access to approved tools that streamline their work, they might resort to unofficial methods, which can elevate risks.
The skill set required will also evolve. Technology experts will need to focus on integration, governance, monitoring, and vendor management, rather than just model development. For others in the organization, honing the ability to ask the right questions of AI, recognizing potential inaccuracies in responses, and understanding when human evaluation is essential will be crucial.
Ultimately, success in the AI landscape will not belong to those with the most pilot projects or the loudest promotional strategies. It will go to organizations that effectively integrate AI as a reliable, everyday component of their operations and that can demonstrate meaningful improvements as a result of its implementation.
Zeeshan Tariq serves as the Chief Digital & Information Officer for CooperVision.
