Practical Pilots: Unique challenges and critical success factors for applying AI in the Health Industry

AI has begun to transform the health industry, offering the ability to enhance clinical workflows, bring therapies to market more quickly, and improve patient outcomes. However, translating AI strategy into successful pilot programs comes with distinct challenges – like data fragmentation, legacy technology integration, regulatory constraints, and ethical considerations. Accounting for these challenges early sets the foundation for productive pilot programs and scalable solutions.

Unique challenges for AI Pilots in the Health Industry

The health industry is one of the most highly regulated, complex, and sensitive. Beyond developing or deploying new technologies, pilot programs must navigate:

• Data readiness and compliance – Data is fuel for AI models and applications, but in healthcare it is often fragmented, incomplete, or trapped in siloed systems. Regulations like HIPAA, CFR, and GDPR impose stringent requirements on data handling (especially when dealing with personal health information). Moreover, AI regulations at the state, federal, and international level are still evolving.
• Integration with existing systems – Complex tech stacks, legacy systems, and fragmented environments have become a recurring challenge for health companies. Data connectivity is often a multistep challenge requiring customized connections.
• Model transparency and validation – Many emerging AI models lack transparency, making the explainability required to gain clinical acceptance or meet regulatory standards challenging.
• Ethical considerations – Reducing underlying model bias, ensuring equitable access, and managing the implications of using AI for sensitive decisions all require ongoing attention and governance.

Critical success factors for AI pilots in the Health Industry

To navigate these challenges, organizations should create a program that holistically supports the people, processes, and technologies that coincide as a part of the changes.

People

Executive sponsorship ensures pilot programs are prioritized and adequately resourced. Frontier AI technologies can cause leaders to feel they need to invest in pilots to inform broader assumptions yet need an experience-based business case to unlock investments. Leaders have to connect the pilot’s significance to broader organizational goals and become comfortable with an iterative, stage-gate approach. These pilots demand collaboration between IT teams, domain experts, and business leaders, and aligning early on the goals at each stage helps navigate the continuous iteration that often leads to valuable enhancements or disruptive new ways of working.

Keep in mind that the skills or new workflows often necessitate upskilling teams, and that buy-in from those teams will require a continuous focus on change management. To speed up adoption it is critical to share why, how, and what will be changing. Explain how the new AI applications feed into a broader organizational strategy with realistic expectations, equipped teams, and either a lack of or a plan for any potential displacement.

Process

Use case selection should be fundamentally anchored to the organization’s AI vision, strategy, and roadmap. Pilots should be designed based on the scale required to achieve those organizational goals – with a clear framework for evaluating progress and predefined KPIs to measure success. A strong governance structure is essential, both to track progress against KPIs and to ensure compliance against the regulatory complexities mentioned earlier. Cross-functional teams should be empowered to investigate emerging questions around data privacy, multinational differences, and ethics (including sourcing external expertise as required).

AI pilots are iterative processes that evolve an organization’s ways of working overtime. Regular feedback loops allow teams to refine models, adjust workflows, and make improvements based on real-world performance. Continuous improvement ensures that the solution remains aligned with organizational goals and adapts to emerging challenges and opportunities. One of Vynamic’s large pharmaceutical clients recently scaled an internal platform engine to an insight-generating commercial and medical suite of solutions by iterating effectively over time.

Technology

The success of an AI pilot often depends on related data quality and accessibility. Selecting models for specific pilots requires considering how interchangeable and tailored the application should be. Often a domain-specific model, or a base model augmented with retrieval augmented generation (RAG) will yield better results than a generic solution. Integrating these solutions on top of legacy systems typically necessitates a modular design that can work as related technologies are updated or replaced. As a modular design takes shape, it’s important to consider how scalable the customized architecture will be. Best-in-class models often change every few 3-6 months, and an early investment in a flexible architecture becomes cost-effective as the use cases or supported business units expand over time.

The role of Vynamic in your pilot program

At Vynamic, we understand what it takes to launch a successful pilot. We help our clients carefully plan for future scalability, align across stakeholders, and consider the impacts to an organization’s people, processes, and technologies. By preparing effectively and communicating successes or learnings along the way, the pilots we have helped launch set a foundation for meaningful changes that fostered broader AI adoption.

Our AI Mobilization offering is designed to help you develop, manage, and scale pilots that deliver real, measurable value. We know the health industry and understand its unique challenges. Our team will partner with you to prioritize the right combination of impactful, feasible opportunities to ensure that each pilot is a step along a successful, valuable AI journey.