Healthcare organizations that want to successfully adopt AI must start with cloud modernization, not AI itself. Without a modern, governed cloud foundation, AI initiatives stall, security gaps widen, and the patients who would benefit most never see the value. That was the core message I brought to the Canada West Healthcare Innovation Summit 2026, hosted by Bamberg Health in Vancouver, and it's a message every healthcare and healthtech leader in Canada needs to hear right now.

I had the opportunity to explore this alongside Rob Haig, CISO at Pacific Blue Cross, in a panel discussion moderated by Cynthia Sinclair. The questions on that stage were exactly the ones that belong in every boardroom and budget cycle in Canadian healthcare. Because at the end of the day, this isn't about technology. It's about the communities we live in, the patients we serve, and the care that every person in this country deserves access to.

Here are the most important takeaways, structured as the questions we were actually asked.

Should Healthcare Organizations Prioritize AI, Cloud Modernization, or Cybersecurity?

Cloud modernization should come first. It is the multiplier for both AI and cybersecurity.

You cannot build secure AI or scalable AI on a fragmented, legacy foundation.

If we don't take care of that foundation first, we won't be able to scale or secure promising AI solutions that could meaningfully benefit patient care. We'll get partway there and stall, or worse, we'll build on a fragile base and eventually have to tear it down.

By contrast, a modern, automated cloud environment enables:

• Scalable AI deployments that integrate with clinical workflows

• Security by design, protecting patient data from the start

• Faster delivery, reducing timelines from months to weeks

The bottleneck is no longer the tooling. It's the organizational will to prioritize the foundation.

What's changed is the cost equation. Modern Agentic AI tools, deployed securely within your own environment, have made complex modernization projects exponentially more affordable than they were just two years ago. The barrier that once made legacy migration feel impossible is dropping fast.

How Can Healthcare Organizations Improve Data Quality and Interoperability While Protecting Patient Privacy?

The answer is data governance, and it's as much a trust problem as a technical one.

Healthcare organizations are sitting on vast amounts of data, much of it siloed, partly by design and partly out of privacy caution. The irony is that fragmentation is itself a risk. Siloed data limits clinical visibility, creates compliance blind spots, and slows the response when something goes wrong.

From our experience at OpsGuru, strong data governance frameworks are the foundation for reliable AI:

• Quality in, quality out: Clean, structured data at the source is far more effective than trying to fix it later.

• Governance over hoarding: Focus on the right data, not just more data, to reduce errors and bias.

• Privacy-preserving techniques: Federated learning, de-identification, and consent management enable safe AI use.

• Interoperability standards like FHIR: The path forward for healthcare data sharing. AWS HealthLake is a practical, HIPAA-eligible example of what an AI-ready, FHIR-compliant data layer looks like at scale.

• Trust as much as technology: Shared standards and accountability let clinicians, admins, and IT teams collaborate safely.

• Cloud advantage: Cloud-native tools automate compliance and maintain audit trails, allowing secure sharing without losing control.

We put this into practice with a large Ontario hospital, implementing a secure, AI-ready data lake that gave clinicians and analysts real-time access to the critical data they need while governance and audit controls operated seamlessly in the background. 

Strong data security and genuine clinical usability are not in conflict. They are designed together, or they fail together.

Trust, in healthcare, is everything — with patients, with regulators, and with the clinical teams whose buy-in determines whether any of this works in the real world.

What Are the Biggest Unmet Needs in Healthcare Cloud Adoption?

Change management, legacy debt, and procurement speed, in that order.

Modernization is not just about technology; it’s about people, processes, and culture. From my experience, healthcare organizations face several persistent obstacles:

Change Management: Technology often moves faster than the people and processes adopting it. Even the best systems fail if teams are not prepared to use them effectively. Leadership and ongoing training are critical to bridge this gap.

Legacy System Integration: Many healthcare organizations are curious about the cloud, but their operations remain anchored to on-prem systems that are difficult to migrate. Modernizing these systems requires careful planning to avoid disrupting critical services.

Legacy Debt: A major unmet need is a clear migration path for monolithic legacy applications, those that are too critical to shut down but too old to move easily. Tools like AWS Transform Custom are beginning to change the equation. We've seen entire codebases translated, re-architected, and modernized in a single day.

Skills Gap: Cloud- and AI-literate talent in healthcare is scarce. Effective adoption requires both technical capability and healthcare domain knowledge, a combination that's difficult to hire for and harder to build internally.

Procurement Velocity: Healthcare moves carefully by design. But rigid, multi-year fixed contracts lock in yesterday's architecture and eliminate the elasticity that cloud is supposed to provide. If procurement timelines don't keep pace with technology cycles, organizations end up investing in solutions they'll need to replace, wasting capital and delaying real outcomes.

This is where partners like OpsGuru add value. We bridge the gap between cloud capability and healthcare-specific implementation, helping organizations modernize safely, adopt AI effectively, and realize tangible outcomes for both clinicians and patients.

Where Is AI Delivering Real Value in Healthcare Today?

The model is not the bottleneck. The data quality is.

With the right foundation in place, the AI applications that matter most become achievable. Organizations that skip the foundational work and go straight to AI pilots will find themselves in proof-of-concept limbo, unable to scale what they've built and unable to deliver real value to patients.

Where AI is delivering real, measurable value today:

• Ambient AI for clinical documentation: AI that listens to physician-patient interactions with consent and handles documentation, coding, and ordering in real time. This solves the number one crisis in healthcare delivery: physician burnout. By removing the screen between the doctor and the patient, we use high-tech to bring back high-touch medicine.

• AI-orchestrated care pathways: Systems that adapt treatment plans dynamically based on patient data, ensuring the right interventions at the right time.

• Personalized treatment planning: AI that analyzes patient history, lab results, and imaging to recommend tailored therapies, adjusting in real time as conditions change.

• Decision support for complex cases: AI can surface insights and suggest options when clinicians must balance multiple variables, improving accuracy and efficiency.

A principle I keep coming back to: put AI in the hands of frontline clinicians, the people closest to patient outcomes and most motivated to improve them. Tools like Claude Code that allow domain experts to build and deploy AI-powered workflows securely, on their own terms, represent a different kind of innovation.

Who better to innovate than the people at the front lines delivering care every day?

Canada is uniquely positioned to lead in this space because of our public health data, cloud infrastructure, and focus on governance and interoperability. With the right foundation, these AI applications can fundamentally transform how healthcare is delivered.

Where Is Healthcare Headed in the Next Decade?

A quick note: when this question came up on the panel, there was a moment of collective laughter. None of us — not the CISO, not the moderator, not me — would claim to know exactly what healthcare looks like in ten years. We can barely predict what AI looks like in ten months. But the direction matters, even if the destination is uncertain. So here's my honest answer.

The future of healthcare will be shaped by the choices made in the next few years. If we get the infrastructure right:

• Care becomes proactive, not reactive. We move from a system designed to treat sickness to a system designed to maintain wellness through AI-assisted monitoring, early intervention, and personalized health management that works before a crisis develops.

• Patients own and access their own data, with real portability across providers and systems.

• Clinicians focus on care, not documentation, and the administrative burden that is driving talent out of the profession.

• Rural and underserved communities gain consistent access through virtual and AI-assisted delivery that meets them where they are. This, to me, is one of the most meaningful things we can achieve.

• AI personalization becomes real for individuals with treatment plans and clinical decision support tailored not to a population cohort, but to the specific person in front of the clinician.

If we don’t:

• Fragmented systems deepen existing inequities, widening the digital divide between those who benefit from AI and those who don't.

• AI bias goes unchecked, eroding public trust in ways that will take years to rebuild.

• Canada falls behind peer nations not just in health outcomes but also in health-tech competitiveness.

Getting it right requires cloud infrastructure that is as reliable and invisible as the electricity running through a hospital. Foundational, taken for granted, and absolutely essential to everything built on top of it.

Healthcare transformation is not just about technology; it’s about aligning infrastructure, governance, AI, and human expertise. At OpsGuru, we partner with organizations to modernize safely, adopt AI responsibly, and unlock better outcomes for patients and providers alike.

If you're looking to accelerate your modernization journey, strengthen data governance, or deploy AI at scale with confidence, get in touch with our team. Together, we can build a healthcare system that is smarter, safer, and more equitable for all.