We’ve already hit a cloud tipping point in healthcare.
Nineteen of the top 20 pharmaceutical companies run on AWS, along with 80% of healthcare and life sciences unicorns. The question for many healthcare leaders is no longer whether to move to the cloud, but how to do so effectively, at scale, without compromising compliance, data safety, or clinical continuity.
The rise of AI is making this question even more urgent. According to McKinsey & Company, more than 80% of healthcare organizations have already deployed their first generative AI use cases to end users, and more than half are using the technology to improve clinical productivity. Infrastructure remains an AI bottleneck for many healthcare organizations. Thirty-one percent of healthcare leaders cite “insufficient data or tech infrastructure” as a barrier to scaling their AI initiatives, and 59% report difficulty integrating or adapting tools into existing workflows.
With healthcare organizations already looking to the cloud, and pressure mounting to deliver cost and clinical benefits via AI, leaders are searching for partners who both understand the sector’s unique challenges and have experience assisting other large health systems with their own migrations.
AI, Beyond the Hype
With many 2025 AI pilots failing to become production-ready systems in 2026, some leaders are understandably approaching the technology with caution. However, some organizations in the health and life sciences sectors have validated repeatable, high-value AI use cases that are delivering operational and clinical benefits.
For example, Capital District Physicians’ Health Plan Inc. (CDPHP), a not-for-profit health plan serving 400,000 members in upstate New York, modernized its medical data infrastructure on AWS to extract more value from large volumes of unstructured electronic health records. Previously, the organization relied on manual processes to extract and process medical records, making it difficult to use that data to inform analytics and improve care. Using AI, CDPHP has made its information queryable across the organization, leading to improvements in reporting and risk adjustment.
In clinical applications, AWS reports that generative AI can improve processes like medical imaging and patient-clinician interactions. Health systems are using the technology to improve image quality, detect anomalies and patterns, and generate synthetic training images. Also, AI-powered ambient listening and note-taking tools can transcribe conversations between clinicians and patients, allowing doctors and nurses to focus their attention on providing care rather than typing notes into EHR systems.
Overcoming Cloud Pitfalls
Most healthcare IT leaders already know that the cloud will likely play a starring role in any major AI initiative. According to the DDN’s 2026 State of AI infrastructure report, 97% of IT and business leaders agree that cloud platforms will be critical for scaling their AI initiatives over the next year. However, these leaders also know that cloud migrations are rarely simple.
For one, legacy systems often don’t translate cleanly to public cloud environments, or they simply cost too much to host in the cloud due to their architecture. This problem can be solved through app modernization efforts, but these can be costly and time-consuming. Even after migration is complete, ongoing cloud costs might be higher than expected. And, as always, healthcare leaders must zealously guard against security misconfigurations or weak access controls that could expose patient data and leave organizations open to regulatory action.
Softchoice, a World Wide Technology company providing software- and cloud-focused IT solutions, worked with a health and wellness organization that had outgrown an aging on-premises environment plagued with weak disaster recovery processes and unexpected downtime. Softchoice led a migration to AWS, positioning the organization to re-platform its SQL environment, adopt cloud-native file servers, and implement CI/CD pipelines. The result was not merely a move to the cloud but a more resilient foundation for future modernization.
In another example, Softchoice worked with one of the world’s leading international pharmaceutical providers to modernize its outdated systems via the cloud. Softchoice guided the organization through its new AWS environment, including a Proxy Virtual Machine tailored to its needs, while supporting VPN connections and virtual machine configurations within the customer’s data center. Softchoice also emphasized education throughout the engagement, helping the organization avoid one of the most common cloud pitfalls: treating migration as a one-time technical project rather than a long-term operating model.
These examples illustrate why healthcare cloud migrations require more than simply choosing a platform and moving workloads. Organizations must also account for legacy application dependencies, disaster recovery, network performance, security architecture, cost control, and ongoing management.
Taking the Next Step
On June 24, I’ll be participating in a webinar hosted by Techstrong, along with some of my colleagues and counterparts from Softchoice, AWS, and World Wide Technology’s Health and Life Sciences practice.
The event is aimed at leaders responsible for cloud, data, or digital transformation within healthcare organizations, and we’ll be sharing practical insights on AI and cloud adoption in the sector.
We will cover:
- Why healthcare’s cloud tipping point is now
- Where AI is actually delivering in the sector
- The most common cloud migration pitfalls in healthcare (and how to avoid them)
- What a high-performing AWS partner ecosystem looks like in practice
- How to build internal alignment and make cloud investment stick
I hope you’ll join us for a lively conversation about what’s working, where organizations face challenges, and how partners can help healthcare organizations accelerate their journeys from cloud strategy to AI outcomes.
To register for this webinar, go to: https://webinars.techstronglearning.com/healthcare-innovation-ai-in-the-cloud
