Data & AI Healthcare Online schedule

At a high level, the session would explore how healthcare organizations move past isolated AI pilots and into real enterprise adoption, what that actually requires at the executive level, and where things most often break down. I typically focus on governance, accountability, and the operational realities of scaling AI across clinical and administrative environments, rather than theory. I have examples to share in healthcare and pharmacy, which are transferable to any industry, especially since healthcare is highly regulated.

• What do current FDA, ONC, CMS, and state-level rules mean in practice for LLMs, CDS tools, and automation models?
• How should organizations manage hallucination, drift, bias, and version control—especially for clinical LLMs?
• What does a responsible AI program look like inside hospitals, payers, and life sciences companies, and who should own it?
• How can governance accelerate innovation rather than act as a bottleneck?
• What should a rigorous vendor evaluation and continuous monitoring process include in 2026?

Moderator: Jason G. Cooper, Chief Data, Analytics & AI Officer – CONCENTRA

Panelists: Bicckie Solomon, Director of Pharmacy / Residency Program Director PGY2 HSPAL - HCA FLORIDA NORTH FLORIDA HOSPITAL

Almut Branner, Former VP Data Management and Analytics – OPTUM

• What “AI readiness” actually means in a healthcare context (beyond buzzwords)
• Common gaps provider organizations face when trying to scale AI
• Data governance as an enabler, not a blocker
• Where healthcare data should live today: on-prem, cloud, hybrid, warehouses, files, and clinical systems
• The role of data quality, lineage, and ownership in clinical and operational AI use cases
• Practical lessons from building AI and data operations inside a healthcare system
• A simple readiness framework leaders can use to assess whether they are truly ready to scale AI

The problem:

A large, recent survey (NBER, 2026) found that 78% of US companies have moved beyond pilots and adopted AI, but only 10% of them are seeing measurable impact. An overlooked reason for this is because Gen AI (especially agentic systems) change over weeks and months—new versions and tools, drift, reordered workflows, and “mutations”—but the workforce as a whole typically adopts new tech over years.

The solution: Continuous Implementation

• Treating AI as part of an adaptive operating strategy, not a one-time launch event
• Aligning leadership on the roadmap and strategy: rethinking workflows, roles, collaboration models, and decision-making structures for an AI-enabled enterprise; creating buy-in throughout the organization; and designating who’s accountable
• Strategies for aligning IT, data teams, clinicians, quality, and compliance around shared AI goals
• Adopting the right metrics and developing a plan in case usage stalls
• Creating a proactive, adaptive governance strategy that evolves alongside AI
• Formal, longitudinal feedback loops: Adoption breadth and depth metrics and periodic employee surveys and interviews so strategies can be adjusted
• Creating an AI-ready workforce with ongoing staff training and micro-videos, designating power-user champions, and holding anonymous office hours

The presentation will include examples of a typical AI adoption scenario vs. adoption with Continuous Implementation.

• How AI assistants, chatbots, and navigation tools are reshaping access, triage, and patient support—and what results are emerging.
• How personalization, segmentation, and risk stratification can improve engagement, adherence, and outcomes.
• What parts of the patient journey can realistically be automated or streamlined without losing the human touch?
• How AI impacts equity, privacy, and trust—and what must be done to mitigate widening disparities.
• What ROI metrics should leaders use to evaluate patient-experience AI investments in 2026 and beyond?

Moderator:

Harish Vundavalli, Senior Information Technology Architect – STRATEGIC EDUCATION

Panellists:

Sameer Sethi, Former SVP, Chief AI Officer – Hackensack Meridian Health

Sruthi Gopalakrishnan, Chief Data and Analytics Officer - WellBe Senior Medical

Mohan Krishna Mannava, Data Analytics & Business Intelligence Leader – TEXAS HEALTH

Bickkie Solomon, Director of Pharmacy / Residency Program Director PGY2 HSPAL - HCA FLORIDA NORTH FLORIDA HOSPITAL

What separates companies experimenting with AI from those generating real enterprise value? This keynote explores how U.S. enterprises are operationalizing AI, building modern data foundations, and moving from pilots to production.

• Why many AI pilots fail to scale and what successful enterprises are doing differently
• What does a production-ready AI strategy actually look like in 2026?
• How organizations are aligning data strategy, AI investment, and business outcomes
• What foundational capabilities—data quality, governance, infrastructure—are required before AI can deliver real value
• How leaders are measuring ROI from AI initiatives across the enterprise

As enterprise organisations race to build AI-ready teams, universities face the inverse challenge: how do you prepare students and current professionals for roles, workflows, and expectations that are actively evolving? This panel brings together higher education leaders working across pre-workforce development and current workforce upskilling to explore how academia and industry can better align in the age of AI.

Discussion points:

• Preparing students for jobs that don’t fully exist yet, how do institutions design curricula for an uncertain AI landscape?
• AI literacy vs. technical specialisation: what does the workforce of tomorrow actually need?
• Upskilling the current workforce: the role of certifications, AI literacy programmes, and industry-aligned learning
• How universities are responding to shifting employer expectations in data and AI
• The role of K-12 through higher education in building long-term workforce adaptability and lifelong learning habits
• Industry partnerships: what’s working, what’s missing, and what business leaders wish academia would do differently

Contributors:

Jayson VanHook, Chief Data Officer and Associate Vice President of Data Strategy – UNIVERSITY OF NORTH GEORGIA

Modern enterprises are redesigning their data architecture to support AI-native applications.

• Why modern AI initiatives require rethinking traditional enterprise data architecture
• What role do data fabric, data mesh, and lakehouse architectures play in enabling AI?
• How vector databases and retrieval-augmented generation are transforming how organizations interact with data
• What challenges arise when integrating legacy data systems with modern AI platforms?
• How can organizations ensure their data architecture is AI-ready for future innovation?

As AI becomes embedded across business operations, governance frameworks must evolve.

Panel discussion themes:

• What does responsible AI governance look like at scale?
• How are organizations balancing innovation with regulatory and ethical concerns?
• What frameworks can ensure AI transparency, fairness, and accountability?
• How can companies manage risks associated with hallucinations, bias, and model drift?
• What role should data leaders, legal teams, and boards play in overseeing AI deployments?
• How will emerging regulations reshape enterprise AI strategies?

Panelists:

Troy Howard, Vice President, Data Governance Manager – BANK OF AMERICA

Hiren Rokadia, Director AI & Data-driven Strategy – TTX COMPANY

Gopal Renganathan, Senior Director, Data & Analytics – ANYWHERE REAL ESTATE INC.

Prakhar Srivastava, Data & Applied Scientist / Applied Researcher in Machine Learning – ZURICH NORTH AMERICA

• How AI is transforming backend data pipelines: from manual processes to automated, intelligent workflows
• The role of AI-assisted coding and data engineering in accelerating analytics development
• Using AI to improve data quality, anomaly detection, and operational monitoring at scale
• How organisations are embedding AI into their analytics infrastructure to surface insights faster and reduce reliance on manual querying
• What data leaders need to consider when governing AI use within internal data and engineering teams

• Why traditional dashboards are falling short in an AI-driven world
• How augmented analytics is shifting organisations from descriptive reporting to predictive and prescriptive decision-making
• Making data accessible to non-technical business users through natural language and conversational BI
• What a successful transition from a dashboard-first to decision-first analytics culture looks like