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A data science team can build a sentiment analysis tool for patient call centers using an LLM API for a few hundred dollars a month in token costs. A vendor will sell you something similar for tens of thousands annually. And increasingly, your EHR vendor is giving your organization tools to build custom AI workflows without writing code or hiring a startup.
So which path is right? The answer depends on more than cost. It depends on your team, your tolerance for risk, and how much governance you're ready to build around each approach.
The case for building
The economics of building AI tools in-house have changed dramatically. With access to foundation models through APIs, a capable IT or data science team can develop solutions at a fraction of what vendors charge: sentiment analysis on patient calls, intelligent ticket routing, predictive scoring for readmission risk, automated chart abstraction, prior authorization support, patient message triage, supply chain forecasting, and scheduling optimization based on census predictions. A strong team can prototype these tools in weeks at pennies per transaction.
The cost argument is only part of it. Building in-house fosters innovation and gives your team a deeper understanding of the technologies themselves. A case report from Penn Medicine leaders published in *Frontiers of Health Services Management* highlights this: in-house development gives health systems the agility to adapt quickly to emergent needs and build solutions tailored to specific institutional goals. When your team builds, they develop institutional knowledge about AI capabilities and limitations that makes every subsequent decision better informed.
The talent challenge is real, though. McKinsey research on generative AI talent found that 51% of employees who identify as heavy AI users or creators plan to leave their jobs within three to six months. McKinsey's guide to digital and AI transformation for healthcare payers reinforces this: organizations that have successfully attracted AI talent have done so by emphasizing healthcare's mission and pairing domain experts with technical specialists. The talent plan needs to be as intentional as the technology plan.
The hidden weight of building
When your best developer leaves, you inherit an application that nobody else fully understands. That risk alone should factor into every build decision. Beyond personnel, that low-cost tool still needs ongoing maintenance: monitoring model drift, updating prompts, testing outputs, and troubleshooting when something breaks at 2 AM on a holiday weekend.
Custom tools also become black boxes to the people using them. If a clinician can't follow the logic behind a recommendation, adoption stalls. If the organization can't explain how a tool works to regulators, that's a compliance risk. Sometimes the vendor subscription premium is buying you security, accountability, and support infrastructure, and that can be worth every dollar.
The case for buying
Vendor solutions come with advantages that are easy to undervalue: dedicated support, regular updates, security certifications, and a contractual relationship that includes accountability. Your technical team stays focused on their core responsibilities instead of becoming a permanent support desk for homegrown tools.
The buy decision makes the most sense when the tool addresses a well-defined, broadly shared problem where vendors have already invested in validation, compliance, and integration. AI-powered chart summarization is a good example. It sounds straightforward: feed clinical notes into a model and get a summary back. In practice, the clinical validation required is substantial. A 2026 study published in npj Health Systems evaluating AI-generated chart summaries found that while physicians were generally positive, they identified omissions in nearly a third of summaries, along with confusing content and hallucinations. Getting chart summarization to a level where clinicians trust it in their workflow requires extensive testing across specialties, patient populations, and documentation styles. Vendors who have invested in that validation across hundreds of health systems offer something most internal teams would struggle to replicate on their own.
Revenue cycle management is another area where buying often wins. AI-powered coding, denial management, and prior authorization tools from established vendors come with trained models, integration with clearinghouses, and regulatory update cycles that reflect the complexity of the payer landscape. The cost of a subscription looks different when you weigh it against the engineering hours, compliance risk, and maintenance burden of building and operating that toolset internally.
The key question is whether the vendor's solution solves a problem your organization shares with hundreds of others, or whether your needs are specific enough that a custom approach would deliver meaningfully better results.
When your EHR becomes the AI platform
Here's where the lines between "build" and "buy" start to blur.
At HIMSS 2026, Epic announced Agent Factory, a no-code visual builder that lets organizations design, customize, deploy, and monitor AI agents across clinical, operational, and patient-facing processes. Agent actions are traceable, and organizations can equip agents with local policies and knowledge bases. Epic is positioning Agent Factory as a sandbox for innovative health systems who want to invent and reimagine workflows on their own timeline. For healthcare AI startups, this development is worth watching closely: when your potential customers can build AI agents inside their own EHR, the competitive landscape shifts.
Agent Factory is designed for IT teams, informatics leaders, and data science groups. McKinsey's analysis of agentic AI in healthcare recommends that organizations be "focused transformers," picking a few high-impact domains to start rather than scattering efforts across dozens of agents. They also flag the build vs. buy question directly: organizations will need to evaluate whether to build their own agentic systems or partner with startups, and enterprise architects will play an increasingly important role in making those calls.
Clinicians are gaining more control within AI tools that already exist. Epic's AI Charting, launched in February 2026, lets clinicians personalize how their notes are structured during visits. That kind of configuration is valuable and signals where things are headed.
The harder governance question is what happens outside the EHR: clinicians and staff using general-purpose AI tools like ChatGPT, Claude, or Gemini for clinical tasks and documentation. That activity is already happening at most organizations, often without IT's knowledge or any formal oversight. IT's role shifts from building tools to testing, validating, and monitoring what's being created and used across the organization. The team becomes the quality assurance layer.
The governance thread
Every path requires governance, just different kinds. Building internally requires code review, model monitoring, and succession planning. Buying requires rigorous evaluation and contract management. Enabling org-level AI creation through platforms like Agent Factory requires clear policies on what can be built, how it gets tested, and who maintains it. And the growing use of general-purpose AI tools by staff requires discovery, education, and guardrails.
The hardest part is the overlap. A single department might be using a vendor AI tool, a custom-built model from the data science team, an Agent Factory workflow from informatics, and a clinician running patient prep through ChatGPT. Governing each category in isolation misses the point. The real challenge is coordinating across all of them with a consistent approach to oversight, accountability, and risk.
Finding the right balance
The right approach starts with honest answers to a few questions: Do you have the talent to build and sustain custom tools? Are your vendors delivering real value? Is your organization ready to take advantage of no-code AI platforms inside your EHR? And are your clinicians and staff already using AI tools that nobody in IT knows about?
That last question might be the most important one to ask right now.
