Healthcare organizations are asking increasingly sophisticated questions about AI. Should we build or buy? Which models should we use? How do we address security, governance, and data sovereignty? How do we prepare our infrastructure for agents and automation?
These are important questions. But they often overlook something more fundamental. Before evaluating vendors or technologies, healthcare leaders should ask a simpler question:
What specific problem are we trying to solve, and how will we know when we’ve solved it?
That may sound obvious, but it is surprisingly uncommon.
Across healthcare, organizations are racing to deploy AI capabilities. Many have already launched pilots, formed AI governance committees, and begun evaluating vendors. Yet some of the most successful organizations are not necessarily those with the boldest AI ambitions. They are the ones that have been most disciplined about defining the problem before selecting the technology.
Too often, healthcare organizations begin with the solution. They want an AI strategy. They want an AI platform. They want to implement agents or copilots because they believe they should be doing something with AI.
The result is that teams end up optimizing for the technology rather than the outcome.
AI vendors can instrument almost anything. They can summarize information, generate content, automate workflows, identify patterns, and support decision-making. The possibilities are almost endless.
But without a clear understanding of the problem being addressed, organizations risk deploying impressive technology that delivers limited business value.
Technology Is Easier Than Clarity
Healthcare has become increasingly sophisticated in evaluating technology. Organizations know how to assess vendors, compare capabilities, review security requirements, and build business cases.
Defining the problem itself is often much harder.
Consider a health system struggling with integration operations. The issue may initially appear to be an AI opportunity. But the underlying problem could actually be excessive alert noise, slow troubleshooting workflows, inconsistent documentation, or limited engineering capacity.
Each of those challenges may benefit from AI, but they require very different approaches and success metrics.
Similarly, an organization exploring AI for prior authorization may discover that its biggest challenge is not generating documentation faster. It may be incomplete data, fragmented workflows, or inconsistent operational processes.
Without clarity on the root problem, organizations can easily find themselves implementing technology that addresses symptoms rather than causes.
The most effective AI strategies begin with specificity:
- Reduce onboarding timelines by 50%.
- Decrease troubleshooting effort by 30%.
- Improve alert response times.
- Accelerate documentation workflows.
Specific problems create measurable outcomes. Measurable outcomes create successful AI programs.
Build Versus Buy Is Often the Wrong Starting Point
The build-versus-buy debate dominates many AI discussions today. Should organizations develop their own solutions? Should they partner with vendors? Should they standardize on a single model?
Those decisions matter, but they generally come later in the process. Organizations should first understand what they are trying to accomplish. Only then can they determine the right approach.
Different problems may require different technologies. Some use cases benefit from large language models. Others may require machine learning, automation, analytics, or entirely different tactics.
The organizations making the most progress with AI are increasingly recognizing that flexibility matters.
Technology will continue to evolve rapidly. Models will improve. New capabilities will emerge. The organizations that succeed will not necessarily be those that selected the “perfect” technology in 2026. They will be the ones that clearly understood the outcomes they were pursuing and remained adaptable in how they achieved them.
The objective should not be to implement AI for its own sake. It should be to solve meaningful problems better, faster, and more efficiently.
Start With the Workflow
One of the most practical ways to approach AI is to begin with a specific workflow:
- Where are teams spending excessive amounts of time?
- What repetitive tasks create friction?
- Where do bottlenecks exist?
- Where does expertise become difficult to scale?
These questions often reveal opportunities where AI can provide immediate value.
In healthcare interoperability, for example, organizations frequently struggle with requirements gathering, troubleshooting, testing, and ongoing maintenance activities. These processes are essential but often time-consuming and repetitive.
AI can help accelerate these workflows, allowing engineers and technical teams to focus on higher-value work. Importantly, success should not be measured by how much code AI generates or how many workflows become automated, rather by how much time, capacity, and operational friction organizations remove from the system.
The most successful AI initiatives tend to look surprisingly pragmatic.
They solve specific problems. They deliver measurable outcomes. And they create momentum for broader transformation.
Clarity Before Capability
Healthcare is entering an era where AI will become increasingly embedded in clinical, operational, and technical workflows. The opportunities are significant. But so is the risk of pursuing technology without sufficient clarity around its intended purpose.
Before asking about models, pricing, security requirements, or implementation timelines, healthcare leaders should begin with a more foundational question: What specific problem are we trying to solve, and how will we know when we’ve solved?
The organizations succeeding with AI are not necessarily the ones with the boldest vision statements or the largest number of pilots. They are the ones that have been ruthlessly specific about the problems they want to address before they ever open an RFP.
Because in the end, AI is not the strategy. Solving meaningful problems is. The organizations that keep that distinction in mind will be in the strongest position to turn AI experimentation into lasting operational value.
See how Rhapsody is helping organizations build the interoperability foundation required for AI at scale.
