- AI-native organizations redesign work around abundant intelligence, not simply AI adoption.
- As clinical AI capabilities grow, human expertise becomes more valuable and scalable.
- Trusted clinical knowledge is the foundation for reliable, effective healthcare AI.
- IMO Health is evolving its technology and knowledge operations to scale expertise with AI.
- Success is measured by outcomes and leverage, not AI usage or automation metrics.
Over the last 15 years, the technology and clinical knowledge organizations at IMO Health have gone through three major transformations.
The first was Agile. We moved from large, sequential projects to iterative delivery. We learned how to work in smaller increments, incorporate feedback faster, and continuously improve.
The second was Cloud. We moved from managing infrastructure to building platforms. Cloud fundamentally changed how we architected, scaled, secured, and operated software.
The third was DevOps. We automated what had previously been manual. A culture of “you build it, you own it” emerged. CI/CD, infrastructure-as-code, observability, automated testing, reliability engineering, and modern operational practices changed how software moved from idea to production.
Each transformation changed how we worked. Each created uncertainty; each required new skills; and each made us more effective, more valuable, and more capable of delivering impact.
We are now in our fourth transformation: becoming an artificial intelligence (AI)-native organization. Like Agile, Cloud, and DevOps before it, AI-native is less about adopting new tools and more about redesigning how work gets done.
What AI-native actually means
Being AI-native does not simply mean using AI tools, adding AI to existing processes, or replacing people with automation. It means redesigning how work gets done when intelligence is abundant.
Just as cloud-native organizations design differently because infrastructure is abundant, AI-native organizations design differently because intelligence is abundant. The question is no longer: “How do we get more people to do more work?” The question becomes: “How do we enable our experts to have dramatically greater leverage?”
That applies equally to software engineers, data engineers, data scientists, SREs, architects, clinical informaticists, terminology experts, mapping analysts, and every other discipline across our organization.
What stays the same
Before discussing what changes, it’s important to discuss what does not.
Clinical and domain expertise
Our clinical knowledge has always been our differentiator. Decades of curated clinical terminology, expertise, and trust are what customers, partners, and healthcare organizations rely on us for.
As clinical AI becomes more capable, expertise becomes more valuable, not less. Our clinical informaticists, terminology experts, mapping analysts, and knowledge teams provide the context, judgment, and validation that make our knowledge solutions reliable and usable.
Those responsibilities do not disappear in an AI-native organization. Rather, they become even more important.
Governance, compliance, and reliability
Healthcare remains healthcare. Quality remains quality. Trust remains trust. The ability to automate work does not remove our responsibility to govern outcomes.
AI raises the importance of governance rather than reducing it. As organizations automate more work, the need for transparency, oversight, and quality controls only grows.
Human accountability
AI can generate recommendations, but humans remain accountable for decisions. The standard for ownership does not change.
Healthcare remains healthcare. Quality remains quality. Trust remains trust. The ability to automate work does not remove our responsibility to govern outcomes.
What changes
Historically, expertise was often constrained by time. Experts spent significant portions of their day performing work that, while necessary, did not fully utilize their expertise.
AI changes that equation.
The future is not experts doing less. Rather, it’s experts accomplishing dramatically more.
The role of the engineer evolves from writing every line of code to orchestrating systems, designing architecture, providing context, and assuring quality. The role of the terminology expert evolves from manually performing every step of knowledge production to directing and validating increasingly capable knowledge agents. The role of leaders evolves from managing tasks to designing systems that maximize leverage.
In every discipline, the focus shifts upward toward less execution and more judgment; less manual production and more orchestration; and less searching for information and more applying expertise.
This shift extends far beyond technology teams. As clinical AI becomes embedded across healthcare, competitive advantage will increasingly come from how effectively organizations combine human expertise, trusted knowledge, and AI-enabled scale.
Our AI-native development approach
To support this transformation, IMO Health is evolving how it builds software, creates knowledge, and governs increasingly intelligent systems.
Our goal is simple: make the easiest path the right path.
Just as cloud platforms abstract infrastructure complexity, AI-native approaches must abstract operational complexity and make effective ways of working the default. Organizations should not have to choose between speed and quality or between innovation and governance. AI-native systems must support both.
Every transformation creates new constraints. As AI accelerates software development and knowledge production, the bottlenecks move to context, governance, validation, and judgment. Those are exactly the areas where we’re investing.
With that in mind, IMO Health is building around five core capabilities:
Context
AI systems are only as effective as the context they receive. Much of an organization’s expertise lives in people’s heads. An AI-native organization makes that expertise increasingly explicit and machine-readable so it can be reused, governed, and scaled.
We are developing machine-readable standards, architectural patterns, security policies, approved designs, and operational expectations that can be consumed directly by AI systems. This creates a foundation where collective knowledge becomes reusable at scale.
Create
AI-assisted development is becoming the default. Tools such as Claude and Codex help accelerate the creation process and enable us to generate better solutions faster while embedding standards from the beginning.
Control
Governance must evolve alongside creation. As AI accelerates development and knowledge work, organizations need ways to evaluate quality, safety, compliance, and reliability at the same pace.
We’re evolving toward programmatic governance, where standards are enforced automatically, AI assists in reviewing AI-generated work, and people focus on validating outcomes rather than manually checking every rule. In short, governance that scales as AI becomes more capable.
Flow
Work should move through the system with less friction. Automation increasingly connects planning, development, testing, deployment, operations, and documentation.
Many artifacts we manually create today become byproducts of work rather than work themselves.
Learn
Every deployment, incident, code review, and operational signal becomes part of a continuous learning loop; telemetry, observability, and feedback that allow both humans and AI systems to continuously improve.
Over time, the system itself becomes smarter as operational knowledge, patterns, and outcomes are incorporated into a reusable organizational memory.
Need a break from reading? Check out our recent webinar, The power of IMO Health’s Knowledge Graph, to learn how we’re connecting clinical concepts, terminology, and real-world data into a deterministic clinical context layer that transforms fragmented healthcare data into trusted, actionable intelligence.
The AI-native engineer
Perhaps the biggest question people have is: “What does this mean for me?”
The answer is straightforward. We need engineers more than ever who can think critically, understand systems, design architectures, solve problems, and exercise judgment.
Across IMO Health, engineers are increasingly leveraging AI to amplify their capabilities. The AI-native engineer is someone who can frame problems clearly, provide high-quality context, evaluate tradeoffs, design systems, validate outcomes, govern quality, and leverage AI effectively.
The same evolution applies to data engineering, data science, architecture, SRE, and every technical discipline.
The industry is moving in this direction – our responsibility is to help everyone keep pace. That means providing tools, training, patterns, guardrails, time to learn, and an environment that encourages experimentation.
The AI-native knowledge organization
When it comes to healthcare AI, clinically validated knowledge is essential. AI systems operate on information. If that information lacks clinical context, consistency, or validity, the quality of outcomes suffers regardless of how sophisticated the underlying model may be.
As healthcare adopts AI, the need for trusted, structured, clinically validated knowledge continues to grow.
We are already seeing this trend across the market as organizations seek trusted clinical knowledge to power next-generation AI applications, ambient experiences, clinical workflows, and decision support solutions.
IMO Health’s response is not simply to apply AI to existing processes. We are redesigning how clinical knowledge is created, maintained, validated, and delivered.
The opportunity is enormous, but healthcare cannot scale through additional manual effort alone. To meet growing demand, we must dramatically increase our throughput while maintaining the quality, governance, and trust that healthcare requires.
At IMO Health, we are increasing our knowledge-production throughput by 10x.
Achieving that outcome requires surrounding our experts with increasingly capable agents that eliminate manual effort, automate repetitive tasks, accelerate research, generate drafts, identify gaps, and streamline validation.
Overall, experts should spend more time applying judgment and less time performing mechanical work. Clinical informaticists are increasingly supported by AI agents that accelerate research, analysis, and validation. Mapping analysts become dramatically more productive as AI takes on more of the repetitive portions of the workflow.
The AI-native clinical knowledge organization is one where expertise scales.
What success looks like
Success is not measured by how many tokens we use. Success is measured by outcomes.
Are experts able to accomplish more than before? Are decisions improving? Is quality increasing alongside productivity? Are we delivering greater value to our customers?
Those are the outcomes that matter: faster delivery, higher quality, greater reliability, more innovation, more knowledge production, more customer impact, and greater leverage for every expert. The organizations that thrive over the next decade will not be those that simply adopt AI tools. They will be the organizations that redesign themselves around AI.
That is the transformation we are undertaking at IMO Health.
The organizations that thrive over the next decade will not be those that simply adopt AI tools. They will be the organizations that redesign themselves around AI.
Just as Agile, Cloud, and DevOps reshaped how we build software, AI will reshape how we create technology, produce knowledge, and deliver value.
This journey is critical for healthcare organizations everywhere. The skills, patterns, and capabilities developed during this transition will help define the next generation of healthcare technology and knowledge work.
We are not starting from scratch – we are already underway.
Now it is time to accelerate.
