Scaling Applications of AI Across Your Organization

This piece is the third installment in our series to help our clients think through key aspects of mobilizing against their organization’s AI journey. In the first article, “Mobilizing Your AI Ambitions: Three Questions for Life Science Organizations to Get Started”, we explored foundational steps for launching AI initiatives. The second article, “Practical Pilots: Unique challenges and critical success factors for applying AI in the Health Industry”,  focused on advancing from pilot programs to broader AI integration. Now, we delve into the next critical phase: enabling enterprise-wide adoption for sustained success.

Once pilot programs demonstrate value and provide learnings to refine the AI roadmap, organizations face the challenge of scaling these initial experiments for broader use. While pilot successes are often seen as a green light for further investment, scaling requires more than just replicating isolated solutions. Instead, it demands a shift in approach: focusing on expanding access to key solution components that other business units can adopt. This is where enterprise-wide enablement begins, allowing AI capabilities to be integrated across the organization.

Achieving this requires substantial investment in organizational learning, development, and change management. The most common reason for failure at this stage is neglecting the human element of the change. By focusing on the following three key areas, organizations can successfully scale their AI initiatives and fully realize benefits.

Proper Management for Scaling

Holistic program management is essential for bringing together the right technology, solution, and business experts at key moments. A strategic Program Management Office (PMO) acts as this bridge, coordinating resources and ensuring effective governance throughout the AI journey. This central team manages cross-functional implementation, oversees change management, tracks value, and keeps key stakeholders informed.

As with any enterprise-wide initiative, different stakeholders will have distinct priorities and roadmaps. At this stage, it’s crucial to align the broader organizational strategy, the AI strategy, and the goals of each business unit. Embedding AI into operational teams starts with understanding their specific challenges and needs—often through individuals within these teams who serve as change champions. These champions play a vital role in sharing success stories and insights with colleagues. Their firsthand experience helps new teams recognize and validate use cases, while their embedded presence fosters trust and enthusiasm for AI adoption across the organization.

Personalized Training and Adoption Planning

Data shows that organizations implementing role-specific AI applications achieve 20-30% improvement in operational efficiency and 10-15% increase in revenue generation[1]. These impressive results stem from solutions that are carefully tailored to specific processes and user needs, not generically scaling a single application. We’ve seen this firsthand in our work with a pharmaceutical company’s Data Science & AI team who customized AI capabilities to meet commercial and medical team needs – leading to more effective precision engagement and multi-channel execution​.

Training across numerous use cases can be challenging, especially when personalizing content for different roles. Team learning improves when it’s tailored to specific responsibilities rather than relying on general examples. In many AI programs, initial training is typically high-level, covering basics such as interacting with a generative component or key considerations for AI use. Beyond this, personalized training helps individuals connect their team’s goals with the broader strategy, laying a foundation for continuous learning. Our experience with upskilling an Oncology Marketing team demonstrated that careful personalization can significantly enhance both work quality and overall productivity.

Finding a Sustainable Balance

With the expectation that applications for AI will only increase over time, organizations need to balance expectations for change management with business stabilization. There will be a need for continuous measurement, refinement, and change. Organizations that regularly assess and refine their AI initiatives are twice as likely to achieve high returns on their AI investments[2]. This involves not only tracking adoption metrics and measuring key performance indicators (KPIs), but also maintaining open channels for user feedback and continuous improvement.

Those channels should be used for clear communication about milestones reached, future operating models, and new applications that maintain momentum or identify concerns before they become blockers. Embedding change management as a key workstream within your overall program management structure ensures adaptivity to continuously meet business needs.

Robust programs should consider both external market factors and internal business needs, especially as AI capabilities evolve faster than past technologies. Sustainable governance should include regular risk and maturity assessments, ongoing market research, and exploration of new solutions.

The Role of Vynamic in Your Scaling Journey

At Vynamic, we understand that scaling AI applications is about more than just technology—it’s about transforming how your organization operates. By meeting clients where they are in their journey and not using a one size fits all approach, we help our clients enable role-specific use cases, personalize training and communications, and drive expansion into future state operating models. We keep the lens of actionable strategy to help our clients’ programs scale and meet their objectives on time.

Our AI Mobilization offering is designed to help you scale programs that deliver real, measurable value. We know the health industry and understand its unique challenges. Our team will partner with you to manage the many teams, resources, and tactics required to maximize adoption across your organization.

References:

[1] https://www.pwc.com/us/en/tech-effect/cloud/cloud-ai-business-survey.html

[2] https://www.forrester.com/blogs/predictions-2025-artificial-intelligence/