AI Strategy in HealthcareCornell Certificate Program

AI is arriving in healthcare faster than most organizations can evaluate it. The tools are proliferating, the stakes are high, and the pressure to adopt is real. Knowing how to cut through the noise and make sound decisions about AI requires a framework, not just instincts or buzzwords.

In this course, you will explore that framework across three interconnected dimensions: the AI tool itself, your organization, and the regulatory landscape. You'll evaluate AI tools by examining their types, risk profiles, and the quality of the evidence supporting them. You'll then assess your organization's readiness across four pillars: technology infrastructure, data readiness, talent and workforce, and culture and change management. Finally, you'll consider the regulatory and compliance considerations required for any responsible AI deployment, including FDA classification, CMS reimbursement implications, and state-level legislation.

By applying this framework, you will develop a structured AI readiness assessment that you can bring directly back to your organization.

As AI adoption in healthcare accelerates, the gap between choosing a tool and deploying it successfully is becoming one of the field's most pressing operational challenges. Mapping how the AI tool changes clinical workflows, validating whether the vendor's evidence reflects your patient population, and designing an adoption strategy that accounts for how clinicians will respond to the change are vital to success. By using a standardized approach, you will set your organization apart from most others.

In this course, you will map clinical workflows before and after the proposed AI integration, evaluate vendor validation data against your own patient population, and design an adoption strategy built around the real reasons clinical staff resist new technology. You'll apply a rigorous lens to the data, identify which type of patient a tool may be leaving behind, and navigate the potential human dynamics that determine whether an implementation succeeds or stalls. By working through a fictionalized healthcare case, you'll develop the skills to build an effective AI implementation plan for your own organization.

You are required to have completed the following course or have equivalent experience before taking this course:

• Examining the AI Landscape in Healthcare Delivery

Most healthcare organizations have a governance structure for technology, but few have one that actually works for implementing AI. Significantly, traditional oversight committees move at the pace of procurement cycles and annual reviews, but AI moves at the pace of rapid updates. The result is a growing accountability gap. Organizations that close the gap are the ones that can clearly identify who owns AI decisions, who responds when something goes wrong, and what is the protocol before a problem occurs.

In this course, you will develop the structural components of an AI governance framework; evaluate the ethical, legal, and consent considerations that any responsible deployment requires; and produce a governance charter and policy document grounded in your organizational context. You'll work through questions that most organizations haven't answered yet: Who owns AI decisions, what transparency standards apply, when patient consent is required, and what happens when a deployed model starts to drift. By working through a fictionalized healthcare case, you'll practice building an effective AI governance package for your own organization.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Examining the AI Landscape in Healthcare Delivery
• Designing Clinical Workflows for AI Adoption

Is your organization adopting AI because everyone's doing it and it's expected of you or because you've completed a structured case showing it makes sense for your business? You want to be able to evaluate the contract terms, the true cost, or the build-versus-buy question before you commit, and you can't count on the vendor to provide that information for you.

The information vendors share is designed to sell, not to surface hidden costs, contract risks, or the internal investment your organization would actually need to make the tool work. Healthcare systems can get caught off guard if they don't uncover the hidden costs and value. Making a strong business case means gathering evidence the vendor doesn't volunteer, reading the contract terms that actually carry risk, and honestly deciding whether it's better to buy the tool or build one yourself.

In this course, you will ensure that you're making an informed decision by conducting three connected analyses for a fictionalized AI initiative: a financial model that captures the true total cost of ownership and the full range of direct and indirect value; a vendor evaluation that weighs contract terms, data rights, liability, and procurement risk; and a build-versus-buy decision grounded in your organization's capabilities.

By working through a fictionalized healthcare case, you will gain the knowledge and skills to prepare a business case recommendation that's tailored to the unique needs of your organization.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Examining the AI Landscape in Healthcare Delivery
• Designing Clinical Workflows for AI Adoption
• Building an AI Governance Framework for Healthcare

Conducting a successful pilot is a great start, but it's only evidence that the conditions were ideal during the pilot: Your AI tool champions were motivated, the workflow was smooth, and the vendor was still in the room to lend a hand. When those ideal conditions don't travel, the tool can encounter obstacles that weren't noticed during the pilot.

Scaling an AI tool across an organization is a different discipline from piloting one. It requires an honest assessment of whether your organization is actually ready, a sequenced plan that accounts for how things break differently at volume, and a monitoring strategy that watches for the right signals long after the vendor has left.

In this course, you will diagnose scaling readiness across five pillars: clinical integration, operational workflow, financial viability, technical architecture, and governance. You will build a 30-60-90-day roadmap with milestones, phase gates, and a risk register, and design a continuous monitoring plan spanning technical, clinical, human, and operational dimensions.

By working through a fictionalized healthcare case, you will develop the skills and frameworks to build a scaling plan for your own organization and bring a recommendation your leadership can act on.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Examining the AI Landscape in Healthcare Delivery
• Designing Clinical Workflows for AI Adoption
• Building an AI Governance Framework for Healthcare
• Evaluating Costs, Contracts, and ROI for Healthcare AI

After an organization deploys an AI tool at scale, the launch plan ends, the go-live celebrations are over, and the vendor has left the room. Now comes the harder question: Is the tool still working, and how would you know?

A launch plan typically accounts for milestones at 30, 60, and 90 days; Day 91 has no end date. The patient population shifts, the model ages, adoption erodes, and the data that would warn you is scattered across systems, departments, and people, each of whom owns a piece of it. Monitoring a scaled AI deployment is two tracks at once: the technical work of watching model performance, clinical outcomes, and operational impact, and the human work of learning what stakeholders are seeing that the numbers don't show.

In this course, you will use a fictionalized case study to map where monitoring data lives in an organization, who owns it, and where the gaps are. You'll define the metrics behind technical, clinical, and operational monitoring dashboards: what is measured, the thresholds that trigger action, and who owns each number. You'll socialize your monitoring strategy with stakeholders and decide what to measure, what to defer, and what to cut. The result is a continuous monitoring and optimization plan your organization can operate.

By working through a fictionalized healthcare case, you will develop the skills and frameworks to build a monitoring plan for your own organization and keep a deployed AI tool safe, effective, and worth its cost.

You are required to have completed the following courses or have equivalent experience before taking this course:

• Examining the AI Landscape in Healthcare Delivery
• Designing Clinical Workflows for AI Adoption
• Building an AI Governance Framework for Healthcare
• Evaluating Costs, Contracts, and ROI for Healthcare AI
• Scaling AI Implementations in Healthcare Beyond the Pilot Stage

Symposium sessions feature two days of live, highly interactive virtual Zoom sessions that will explore today's most pressing topics. The AI Symposium offers you a unique opportunity to engage in real-time conversations with peers and experts from the Cornell community and beyond. Using the context of your own experiences, you will take part in reflections and small-group discussions to build on the skills and knowledge you have gained from your courses.

Join us for the next Symposium, in which we'll share experiences from across the industry, inspiring real-time conversations about best practices, innovation, and the future of AI. You will support your coursework by applying your knowledge and experiences to some of the most pressing topics and trends in the field. By participating in relevant and engaging discussions, you will discover a variety of perspectives and build connections with your fellow participants from across a variety of industries.

All sessions are held on Zoom.

Future dates are subject to change. You may participate in as many sessions as you wish. Attending Symposium sessions is not required to successfully complete any certificate program. Once enrolled in your courses, you will receive information about upcoming events. Accessibility accommodations will be available upon request.

eCornell online Workshops are live, interactive learning experiences lasting 1 to 4 hours and led by Cornell faculty experts. These premium, short-format sessions focus on AI topics and are designed for busy professionals who want to gain immediately applicable skills and strategic perspectives. Workshops may include faculty presentations, breakout discussions, and guided hands-on practice.

The AI Workshops All-Access Pass provides you with unlimited participation for 6 months from your date of purchase. Whether you choose to attend one Workshop per month or several per week, the All-Access Pass allows you to customize your AI journey and stay on top of the latest AI trends.

Hosted by Cornell faculty at the forefront of their fields, Workshops cover a range of cutting-edge AI topics applicable across industries. Workshops are offered at three levels to allow you to choose topics that match your experience.

• AI Foundations ​​​​​
  • These Workshops introduce core AI concepts, terminology, capabilities, limitations, and practical applications. No prior AI experience is required.
  • Best for: Beginners, AI-curious professionals, and teams starting their AI journey.
• AI in Practice ​​​​​​
  • These Workshops focus on practical skills, workflows, and strategies that help participants use AI more effectively in their day-to-day work. Some familiarity with AI tools is recommended.
  • Best for: Professionals who have experimented with AI and want to build confidence and capability.
• AI Leadership and Transformation ​​​​​​
  • These advanced Workshops explore emerging technologies, strategic implementation, governance, organizational impact, and specialized applications. AI fluency is expected, and some Workshops may have prerequisites.
  • Best for: AI leaders, transformation teams, executives, and advanced practitioners.