D&A Live Summer 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

In the highly competitive age of digital transformation financial service organizations are facing accelerated urgency to improve their customer and employee experience while simultaneously reducing operating costs, and managing risk and compliance.

To meet these competing demands on their business, these organizations are racing to deploy deep learning to achieve a new competitive edge by optimizing their back office operations with intelligent document processing, personalizing their customer experience with cutting edge NLP models, and reducing fraud and risk using state-of-the-art deep learning.

AI is here and delivering new capabilities to help businesses solve large and complicated challenges. Join Bob Gaines to learn what that means for your business and how deep learning is helping organizations:

• Achieve higher compliance, faster and with lower costs • Dramatically improve Customer Experience • Reduce time to value from years to weeks

Sergio Rego is a customer engineer at SambaNova Systems where he helps clients deploy purpose-built, deep learning solutions in weeks rather than years. Sergio started his career in financial services, where he worked in strategy; active and index management; and product design and management. Sergio also served as a senior data scientist and team manager for a system integrator where he helped federal government agencies deploy ML and AI solutions.

The problem:

Most companies are considering adopting AI or are piloting AI, but within weeks post-deployment usage stalls, with no efficiency gains or ROI. On the scale of months, genAI and especially agentic AI change rapidly—new tools and versions, drift, and reordered workflows—but humans adopt new tech over years, not months.

The solution: The Continuous Implementation Framework

• Treating AI as part of an adaptive operating strategy, not a one-time launch event
• Strategies for aligning leadership on the roadmap and strategy: Rethinking workflows, roles, collaboration models, and decision-making structures for an AI-enabled enterprise, and creating buy-in throughout the organization
• 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 governance strategy that evolves as AI changes
• Collecting data beyond overall usage: Periodic employee surveys and interviews with formal feedback looks
• Creating an AI-ready workforce with ongoing staff microlearning's, formal feedback loops, and strategy readjustment
• Examples of typical AI adoption scenario vs. adoption with the Continuous Implementation Framework

• 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?

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 

Details coming soon!

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

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

Topics include:

• 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?

AI agents are shifting analytics from dashboards to automated decisions.

• What exactly are AI agents, and how are they changing enterprise analytics workflows?
• How can AI agents autonomously retrieve, analyze, and act on enterprise data?
• What risks emerge when decision-making becomes partially automated?
• How will natural-language interfaces transform the way business users interact with data?
• Could AI agents eventually replace traditional BI dashboards?

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?

AI is transforming analytics from descriptive reporting into predictive and prescriptive decision intelligence.

• How AI is enabling automated insights and proactive analytics
• The growing role of natural language analytics and conversational BI
• How augmented analytics empowers non-technical business users to leverage data
• What new skills will analysts need as AI becomes embedded in analytics tools?
• How organizations can transition from reporting-focused analytics to decision intelligence

Industry leaders debate the future of enterprise data and AI platforms.

Discussion themes:

• How AI is enabling automated insights and proactive analytics
• The growing role of natural language analytics and conversational BI
• How augmented analytics empowers non-technical business users to leverage data
• What new skills will analysts need as AI becomes embedded in analytics tools?
• How organizations can transition from reporting-focused analytics to decision intelligence

Audience Q&A and interactive discussion.