Leaders in AIHealthcare Summit
Monday, June 21, 2027
One-Day Healthcare Summit:
10:00 AM – 6:00 PM
Venue to be announced
.png)
Built for healthcare leaders. Join us on Monday for a dedicated healthcare AI summit, then explore the cross-industry Leaders in AI Summit Boston · June 22–23 →
Boston Healthcare Summit Agenda
Monday, June 21, 2027 · 10 AM–6 PM
One dedicated day for healthcare leaders, featuring executive panels, live polling and focused roundtable discussions.
10:00 AM – 10:20 AM
Networking Lounge
Arrivals & REGISTRATION
Start your Leaders in AI Healthcare Summit experience by checking in with our registration team. Collect your badge, enjoy coffee, and meet the healthcare executives joining you for a full day of panels, live polling and roundtable discussions.
10:20 AM – 10:30 AM
Main Stage
Opening Remarks
Kick off the summit with opening remarks from the Institute for AI Transformation, introducing the healthcare priorities and executive decisions shaping the day ahead. This brief session leads into our opening panel.
10:30 AM – 11:10 AM
Main Stage
Opening Panel
The Race for Healthcare AI: Agents, Infrastructure & Executive Decisions
Artificial intelligence is no longer a future initiative. It is embedded in board-level priorities and investment decisions across healthcare. The question is no longer whether to invest in AI, but how to scale it in a way that delivers measurable clinical, operational and financial value. As agentic AI, hybrid architectures and distributed inference reshape requirements, healthcare leaders must align governance, infrastructure and investment strategy to move from experimentation to trusted, organization-wide capability.
Key Learning Objectives for the Audience
- Clarify the Barriers to Enterprise-Scale AI:
Understand the most common structural and organizational challenges that prevent AI initiatives from reaching production maturity, including misaligned ownership, insufficient infrastructure readiness, and unclear value measurement.
- Define the Infrastructure and Governance Required for Agentic Systems
Explore how hybrid deployment models, secure data environments, and cross-functional governance frameworks are evolving to support scalable AI and autonomous systems.
- Align AI Investment With Measureable Business Impact
Gain practical insight into how executive teams are structuring funding models, accountability frameworks, and performance metrics to translate AI ambition into enterprise results.
Moderator
Bruce Monaco
Co-Founder / Head of Research
.webp)
11:10 AM – 11:50 AM
Main Stage
Featured Panel
Inference Where It Matters: Running AI Across Health Systems, Payers & the Point of Care
AI is no longer defined only by how models are trained, but by where and how they are deployed. As healthcare organizations move from experimentation to real-world impact, inference is becoming a critical layer across clinical, administrative and payer workflows. This panel explores what it takes to run AI reliably where decisions happen, balancing latency, cost, privacy and security with the demands of EHR integration, human oversight and continuity of care.
Key Learning Objectives for the Audience
- From Models to Decisions in Real Time
Explore how enterprises are shifting focus from training to inference, embedding AI into live workflows where speed, accuracy, and operational trust directly impact business outcomes.
- Running AI at the Edge of the Enterprise
Learn how organizations are architecting AI systems across on-prem, hybrid, and edge environments to meet latency, cost, and performance requirements.
- Inference at Scale in Regulated Environments
Understand how health systems and payers deploy AI in production, where privacy, reliability, auditability and clinical oversight are essential.
Moderator
Bruce Monaco
Co-Founder / Head of Research
11:50 AM – 12:20 PM
Networking Lounge
Opening Networking Break
12:20 PM – 1:00 PM
Main Stage
Featured Panel
The Healthcare AI Playbook for 2027: What Leaders Must Decide, Fund & Stop Doing
Healthcare leaders face a growing list of AI opportunities and limited capacity to pursue them all. This panel explores how executives prioritize investment, secure clinical and operational buy-in, and decide which pilots to scale, redesign or stop. Expect a practical discussion of ownership, workforce adoption and the measures that connect AI programs to patient experience, clinician capacity and sustainable operational improvement.
Key Learning Objectives for the Audience
- Active Buy-In & Alignment:
Explore how to secure clinical, operational and technology buy-in. Discuss shared decision-making, clear ownership and the approaches that help staff adopt AI within real healthcare workflows.
- Embedding Agility & Resilience:
Examine the critical need for adaptable cultures ready to embrace new processes. Panelists will provide insights on developing resilience at every level of the organization, ensuring a confident response to market shifts and fostering an environment that propels innovation.
- Measuring & Sustaining Adoption:
Discover how to track the impact of transformation efforts well beyond the launch phase. Panelists will share approaches for maintaining momentum through transparent metrics, iterative improvements, and leadership commitment—transforming early wins into lasting results.
Moderator
Bruce Monaco
Co-Founder / Head of Research
1:00 PM – 1:45 PM
Networking Lounge
Lunch Hosted By the
institute for ai transformation
1:45 PM – 2:30 PM
Roundtable Discussions
Executive Roundtable Discussions
Executive Roundtables Topic 1
Healthcare AI Governance, Risk & Compliance: Enabling Scale Without Slowing Momentum
As AI becomes embedded in clinical and administrative workflows, leaders must manage risk and accountability without stalling progress. Discuss validation, patient privacy, model oversight and the ownership required to scale trusted AI.
Topic 1 Discussion Points:
- Patient privacy and secure data access
- Clinical validation and model monitoring
- Accountability, escalation and human oversight
Executive Roundtables Topic 2
Hybrid Healthcare AI Infrastructure: Balancing Cloud, Edge & Cost
Explore the infrastructure needed to move healthcare AI into production. Discuss EHR integration, secure data access, cloud and on-premises deployment, and the performance and cost trade-offs across clinical and operational workloads.
Topic 2 Discussion Points:
- EHR integration and data interoperability
- Cloud, edge and on-premises deployment choices
- Latency, reliability and total cost of ownership
Executive Roundtables Topic 3
The Human Layer of Healthcare AI: Clinician Adoption, Trust & Change
Beyond models and infrastructure, AI success depends on how people use it. Discuss clinician and staff adoption, human oversight, workflow design and the measures that show whether AI is reducing burden or creating new friction.
Topic 3 Discussion Points:
- Clinician and staff trust
- Workflow design and adoption measurement
- Reducing burden without removing human accountability
Executive Roundtables Topic 4
Agentic AI in Healthcare: From Experiments to Operational Impact
As healthcare organizations move beyond pilots, agents are entering administrative and care-support workflows. Explore where to begin, how to set boundaries, and how to evaluate reliability, escalation and accountability before expanding deployment.
Topic 4 Discussion Points:
- Selecting administrative and care-support use cases
- Permissions, oversight and escalation boundaries
- Evaluating reliability and operational value
2:30 PM – 2:50 PM
Main Stage
Present Your Executive Roundtable Findings & Live Polling
Each roundtable will share its key takeaways with the full audience. Designate one representative to present your group’s findings and discussion highlights, then compare priorities through live polling across the room.
2:50 PM – 3:20 PM
Networking Lounge
Afternoon Networking Break
3:20 PM – 4:00 PM
Main Stage
Featured Panel
Understanding the True Cost of Healthcare AI: Tokens, Compute & Capacity
As healthcare AI adoption accelerates, the challenge is no longer just experimentation; it is the economics of scaling. From token usage and inference costs to compute availability and infrastructure strategy, leaders must understand the financial and operational realities behind production AI. This discussion explores how health systems and payers forecast workloads, optimize model usage and balance cost, performance and capacity while measuring the value of clinical and administrative use cases.
Key Learning Objectives for the Audience
- Understanding AI Economics:
Learn how token pricing, inference workloads, and model selection influence the true cost of deploying AI across the enterprise.
- Planning for Compute and Capacity:
Explore how organizations are forecasting AI demand, managing GPU constraints, and building infrastructure strategies to support production-scale AI.
- Optimizing AI for Sustainable Scale:
Understand how leaders are controlling costs through model strategy, workload management, and infrastructure decisions that balance performance and budget.
Moderator
Bruce Monaco
Co-Founder / Head of Research
4:00 PM – 4:40 PM
Main Stage
Featured Panel
Transforming Healthcare: From AI Pilots to Clinical-Grade Systems
Healthcare organizations are under pressure to deploy AI at scale while operating in one of the most regulated, data-constrained environments in the enterprise. Leaders are moving past experimentation and asking harder questions: where AI truly improves outcomes, where risk increases, and how systems can be trusted inside clinical and operational workflows.
This panel brings together healthcare data and AI leaders to discuss how organizations are operationalizing AI across care delivery, supply chains, and enterprise operations. The focus is not on future promises, but on what is working today — how leaders are navigating data access, governance, and validation to move AI from pilots into production without compromising safety, compliance, or performance.
Key Learning Objectives for the Audience
- Deploying AI in Data-Constrained Clinical Environments:
How healthcare organizations are using synthetic data, model tuning, and controlled access strategies to innovate while protecting patient privacy and meeting regulatory requirements.
- Making AI Trustworthy for Clinical and Operational Use:
How leaders validate, monitor, and govern AI systems so outputs can be trusted by clinicians, operators, and executives — not just data science teams.
- Scaling AI Without Disrupting Care Delivery:
How enterprises are integrating AI into real workflows, reducing friction for clinicians and staff while ensuring reliability, accountability, and measurable impact.
Moderator
Bruce Monaco
Co-Founder / Head of Research
4:40 PM – 5:00 PM
Main Stage
Closing Remarks
5:00 PM – 6:00 PM
Networking Lounge
After-Party