El Mostafa Bouattane

The first wave of AI in healthcare was defined by experimentation and proof-of-concept pilots. Today, we’re entering a new era — AI 2.0 — where the focus is shifting from isolated innovation to sustainable, integrated clinical tools that enhance outcomes, reduce burden, and scale impact. Source practical tips to:

• Transition from pilot projects to enterprise-scale implementation.
• Align AI development with clinical governance, safety, and usability standards.
• Integrate AI tools into EHRs and care pathways to reduce friction and enhance adoption.
• Overcome pilot fatigue, ensure clinical buy-in, and achieve measurable improvements.

Improve patient care by positioning AI 2.0 not as a future vision, but as a practical engine for change today.

Hôpital Montfort began its data transformation with a clear-eyed assessment of its current state: fragmented governance, limited scalability, and inconsistent data literacy. To address these gaps, Montfort designed a strategic roadmap anchored by a robust governance framework, phased policy development, and technology modernization. Adopt best practices to:

• Establish a centralized data governance model by defining clear roles and responsibilities and implementing a Microsoft Fabric Lakehouse architecture for scalable, real-time analytics.
• Optimize an AI-ready, compliant, and agile data ecosystem — enabling advanced analytics, responsible AI deployment, and continuous innovation across the organization.

Master your healthcare data strategy with a governance-first approach that enables innovation while ensuring trust, compliance, and scalability.