The question isn't whether or not to adopt AI. It's whether to build integrated infrastructure now, to avoid technical debt that could handicap your organization for the next decade.
Health system executives are saying yes to AI. Yes to copilots, yes to ambient scribes, yes to revenue cycle tools, yes to imaging tools. After years of cautious pilots, systems are now stacking tools at speed.
But each “yes” to a new vendor comes with a new contract, a new data pipeline, a new security review, a new integration, and new workflows. Rather than driving innovation, this rapid accumulation of technical debt could quietly choke a health system’s velocity for years to come.
Deja Vu All Over Again
We’ve made this mistake before. Twenty years ago, health systems bought piecemeal EHR solutions: a lab system from one vendor, radiology from another, billing from a third. In 2012, over 450 certified EHR vendors competing for hospital business.1 What looked more nuanced and specific at the time of procurement became slow to scale. Data lived in silos. IT teams maintained numerous vendor integrations. Clinicians were forced to learn dozens of disjointed systems.
Today, we are at risk of repeating the same pattern with generative AI, except the stakes may be exponentially higher. Fragmented AI systems will suffer the same fate as fragmented EHRs (interoperability failure, workflow inefficiency, high cost of ownership), while bringing an entirely new class of risk: real-time governance, algorithmic transparency, bias monitoring, model drift, and constant cybersecurity exposure.
The market has already shown us the ending to this story. By 2021, two EHR vendors controlled 72% of hospital beds.2 System fragmentation had become operationally impossible to sustain. Health systems that delayed the shift paid a steep premium and absorbed years of disruption to catch up.
Now imagine rerunning that scenario across dozens, or hundreds, of AI systems.
The Compounding Costs of Point Solutions
Today’s AI point solutions are already locking health systems into significantly higher long-term costs that are easy to underestimate.
The visible expenses are obvious: licensing, implementation, professional services, maintenance. But where exactly are the hidden costs of this strategy and what are the alternatives?
Data Complexity Grows Exponentially
Every vendor requires a different data structure, a different pipeline, a different synchronization strategy. As deployments multiply, health systems accumulate parallel data feeds that can be difficult to keep aligned, limit interoperability across tools, and are fragile, where a single upstream data change can break multiple downstream integrations. Over time, maintaining this “AI plumbing” becomes a significant engineering burden, taking time away from building new capabilities.
The platform alternative: Standardizing data ingestion, normalization, and quality control can eliminate the need to build and maintain separate data feeds for each use case. A shared foundation improves interoperability, reduces the risk that upstream changes cascade across integrations, and allows new AI workflows to be deployed without compounding data engineering effort. It enables AI to scale across workflows without linear growth in infrastructure complexity.
Fragmented Governance Creates Unmanageable Risk
In a point-solution world, safety evaluations, bias testing, version control, audit trails, and change management are scattered across vendor portals. When a model behaves unexpectedly, there is no system of record, and accountability becomes challenging. Health system’s informatics teams may struggle to keep pace, and aligning to federal risk-management guidance across disconnected stacks will be highly challenging.
The platform alternative: Centralized tracking in a platform model enables continuous oversight, with every model, workflow, dataset, user, and update visible in one centralized dashboard with full auditability. When something breaks, you know exactly what changed and can roll back instantly. This is not just compliance readiness, but also operational resilience.
Bias Mitigation Becomes an Afterthought, Not a Design Principle
With point solutions, performance monitoring happens vendor-by-vendor, if it happens at all. Discovering bias means negotiating fixes across multiple contracts and technical architectures. By the time disparities can be rectified, they may have already affected patient care.
The platform alternative: Bias mitigation can be designed into the platform and is a part of the continual monitoring that is embedded in the governance dashboard described above. Performance can be monitored across populations and subgroups in real time, and remediation workflows can be configured ahead of time so that potential bias can be flagged immediately and addressed systemically across all use cases.
Security Becomes Unenforceable at Scale
As regulatory expectations tighten around identity, encryption, access control, and vendor oversight, enforcement across multiple vendors becomes challenging. Security reviews devolve into checkboxes and every new external vendor connection becomes a potential breach vector. Recent cyberattacks have demonstrated the cost of fragmented security, and vendor access is often one of the weakest links.
The platform alternative: A platform allows enforcement of security controls at the infrastructure layer, so that all applications and use cases inherit the same protections. Fewer access points and standardized routing through pre-set security controls mean lower exposure. When a vulnerability appears, it can be addressed once instead of coordinating across a patchwork of vendors.
Clinicians Drown in Tools, and Adoption Stalls
Each new “solution” arrives with its own interface, logic, thresholds, training requirements, and reliability curve. Clinicians face an expanding and incoherent toolset. What was meant to reduce burden becomes cognitive and operational friction. Adoption stalls, and shadow AI usage grows as people bypass sanctioned tools for speed.
The platform alternative: A platform allows clinicians to operate in one environment and one experience, with consistent patterns and reusable training. With the right design, AI can feel like an extension of existing care delivery tools and workflows to ease adoption and prevent the search for workarounds.
When Point Solutions Actually Make Sense
Certainly, platforms won’t be the answer to every problem. Highly specialized clinical algorithms with minimal data sharing and clear outcomes may justify standalone deployment. Short-lived pilots with defined sunset dates can test hypotheses for what should be added to your platform. And some of these tools can be brought into existing platform governance, security, and monitoring even if they're not platform-native.
But the starting point should be a platform with plans to extend from there rather than the other way around. Exceptions should be documented, time-bounded, and evaluated against the platform path alternative. In almost every organization that we’ve seen, what feels “fast” in procurement (buying a point solution) becomes painfully slow in execution.
Platform Economics Change the Math
By building integrated AI infrastructure today, health systems can avoid hundreds of millions of dollars in downstream expenses. Shared infrastructure components can be built once and reused. Volume purchasing and consolidated vendor management can reduce contract overhead. Most importantly, organizational capacity can be redirected from maintenance toward value creation.
In our experience, the ROI inflection point typically arrives after just a couple of use cases. The first few implementations on a platform do require foundational work. But each subsequent use case inherits that infrastructure. By the tenth use case deployment, you're moving at a fraction of the time and cost of point solutions, and your team has capacity to keep accelerating. As unit economics evolve, the cost of a point solution in time and labor might, in fact, be similar to the cost of making a platform purchase.
For organizations that want to move towards a platform strategy, the next steps are straightforward:
- Inventory your current AI footprint
- Establish unified infrastructure and governance
- Migrate high-value use cases first
- Retire redundant tools
- Reinvest the savings into scale
In less than a decade, the EHR market moved from over hundreds of vendors to an effective duopoly. The AI market will consolidate faster, driven not by federal mandates but by the compounding operational tax of managing governance, security, and cost across disconnected systems.
Organizations that establish unified platforms now will build the competitive advantage that comes only from innovation that scales.
1. Charles D, Bean C, Purnell-Saunders S, et al. Vendors of Certified Electronic Health Record Technology: Trends and Distributions from Meaningful Use Attestations as of October 31, 2012. 2012 Oct. In: ASTP Health IT Data Brief [Internet]. Washington (DC): Office of the Assistant Secretary for Technology Policy; 2012. 6. Available from: https://www.ncbi.nlm.nih.gov/books/NBK612944/
2. Holmgren AJ, Apathy NC. Trends in US Hospital Electronic Health Record Vendor Market Concentration, 2012-2021. J Gen Intern Med. 2023 May;38(7):1765-1767. doi: 10.1007/s11606-022-07917-3. Epub 2022 Nov 8. PMID: 36348217; PMCID: PMC10212829.
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