What are today’s required skills for the AI transition and what could that type of data problem look like once the basics are in place? This episode discusses different services for data structure, data processing principles and the future of data in an AI world.

What are today’s required skills for the AI transition and what could that type of data problem look like once the basics are in place? This episode discusses different services for data structure, data processing principles and the future of data in an AI world.

Translating research findings into actionable requirements requires a skillset that combines both research methods with operational knowledge. In this episode we explore different angles of this expertise: (1) Research infrastructure as a service, (2) Research as Last-Mile Delivery expertise and (3) Research scaling through the Quality Insurance lens.

Beyond the vanilla, textbook cases.

Performance optimization is about more than technical requirements. It is about both tangible measurements (such as budget, efficiency, speed of delivery, features, bugs, etc), and intangible ones (such as distributed learning across teams). In this episode I discuss performance optimization through the lens of real-world projects as well as strategic considerations for AI-enabled design teams. I include a short discussion on Co-Pilots and efficiency measurements/costs in that context.

This episode first and foremost highlights the multi-dimensional nature of optimization, measurement-as-design-principle, concrete dramatic results, and the strategic thinking that differentiates comprehensive approaches such as the ones I am sharing in this episode, from typical "make it faster", "make it cheaper" performance work.

The gap between "it worked when we tested it" and "it works in the real world environment" (with end users) is often the failure point of systems design. For systems that deploy successfully and gain user engagement and traction once implemented, teams have to think about owning the entire scope of the journey - from the conception to the testing, training, measuring and improvement of the system. This episode discusses the different types of "end to end" concepts in projects, the skills required for end to end system design and implementation and the rising debate on AI development - specialization vs generalization within the workforce.

This episode discusses systems designing and implementation across different data actors - be it internal legacy systems, third-party packages or clients with their own data structure. I conclude by discussing future inter-operability skills needs in an AI-first design world. Throughout this episode, teams will find insights on best practices as well as conceptual ways of visualizing cross-platform integrations.

Designing and implementing a system in healthcare informatics requires a specialized skillset. In this episode, I outline some of the specific skills needed for projects.

• Understanding patient motivation and behavior for system adoption
• Understanding provider needs and constraints as users of a system
• Design considerations for emerging technologies - existing workflows, regulations and maturity levels

• Ramifications of multiple-players landscape: provider/payer/patient relationships
• How other background such as retail, healthcare policy and research contribute to better system design ( ex. Customer journey optimization, data-driven personalization, regulatory navigation, clinical workflow understanding, rigorous methodology, evidence-based validation, UX design for user adoption)

As a system analyst, compliance is at the forefront of design considerations. This episode discusses a few use cases from my past experience including:

• HIPAA-compliant system architecture 
• Safety-critical knowledge systems 
• Multi-stakeholder compliance navigation 
• Cross-industry risk assessment
• Constraint-driven design

This is a discussion on the benefits of a platform-first design (ex. systems/modeling approach) vs a product-first design (ex. MVP). While both approaches are fundamentally agile, they require different time horizons and scale differently. This is why the systems approach might yield better return on investment for larger, more complex data-driven projects.