Ashutosh Das
Transforming Care with Data, Insight, and Purpose
Los Angeles, California, United States
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Recognized leader at the intersection of healthcare and artificial intelligence, with over 14+ years of experience driving innovation across payer, provider, and digital health landscapes. Currently serving as Business Tech leader at Health solutions Organization. Led strategic AI initiatives focused on transforming Data insights, improving operational efficiency, and delivering patient-centric outcomes at scale.
With a background spanning data science, healthcare operations, and product strategy , have spearheaded enterprise AI programs, including intelligent claims processing, predictive population health models, and automated provider network optimization. Known for bridging technical and business domains.
Area of Expertise
Topics
Moving from Silos to Seamless Systems
Fragmented data across teams and systems forces users to repeat their information, delays critical decisions, and creates operational gaps. This session explores how breaking down data silos enables coordinated experiences across the entire user journey. Learn from real implementations that reduced service gaps, improved satisfaction, and accelerated time to resolution through secure, connected ecosystems. Discover actionable strategies for seamless coordination while maintaining data privacy and security.
Key focus areas:
1. Implement secure data exchange strategies that enable coordination across teams while maintaining privacy and regulatory compliance.
2. Identify common data silos in your organization that force users to repeat their stories and create preventable gaps.
3. Build cross functional collaboration models that break down organizational silos.
4. Apply interoperability standards to build connected ecosystems that enable secure data exchange across operations.
Governing AI at Enterprise Scale. Strategies for Product Leaders
Multiple departments inside one enterprise quietly built fifteen separate AI chatbots. Each had its own APIs, its own data pipeline, its own vendor contract, and its own infrastructure. None of them talked to each other. Duplicate spend crossed $1.5 million before anyone had a complete inventory, and it took a public facing AI incident at a peer organization to force the executive team to act. This session is the operational story of what came next, told through the product roadmap and governance decisions that made the difference.
We walk through the governance model and shared capability approach that reduced AI infrastructure waste by roughly 60 percent, cut duplicate cloud spend, tightened compliance posture, and shortened time to deploy new AI use cases from months to weeks. The focus is on the product decisions, the roadmap trade offs, the intake and architecture review process, the funding model changes, and the measurement discipline that made the transformation stick.
Attendees will see the actual AI Governance Committee charter, the intake process that catches duplicate solutions before budget is committed, the shared capability inventory that lets teams reuse authentication, data access, model hosting, and audit logging instead of rebuilding them, and the metrics dashboard that proves the model is working.
Attendees leave with three tangible artifacts:
1. AI Governance Committee charter template adaptable to enterprise product organizations.
2. AI Sprawl Audit worksheet for cataloging duplicate capabilities across product portfolios.
3. Five level Governance Maturity Scorecard for benchmarking product AI programs against peers.
Governing AI at Enterprise Scale
Multiple departments inside one enterprise quietly built fifteen separate AI chatbots. Each had its own APIs, its own data pipeline, its own vendor contract, and its own infrastructure. None of them talked to each other. Duplicate spend crossed $1.5 million before anyone had a complete inventory, and it took a public facing AI incident at a peer organization to force the executive team to act. This session is the operational story of what came next.
We walk through the governance model and shared capability approach that reduced AI infrastructure waste by roughly 60 percent, cut duplicate cloud spend, tightened compliance posture, and shortened time to deploy new AI use cases from months to weeks. The focus is not on the technology stack. The focus is on the governance decisions, the intake and architecture review process, the funding model changes, and the measurement discipline that made the transformation stick.
Attendees will see the actual AI Governance Committee charter, the intake process that catches duplicate solutions before budget is committed, the shared capability inventory that lets teams reuse authentication, data access, model hosting, and audit logging instead of rebuilding them, and the metrics dashboard that proves the model is working. We walk honestly through what did not work, including a failed first attempt at centralized approval that had to be redesigned into a lighter federated model within six months.
Key takeaways:
1. Identify duplicate AI capabilities across your organization that create 60 to 80% waste in engineering effort and infrastructure spending.
2. Design reusable AI capabilities (APIs, libraries, containerized services) that multiple teams can leverage across different products.
3. Build governance intake processes that catch duplicate solutions before spend is committed.
4. Calculate ROI of platform thinking using metrics like reduced cloud spend, faster delivery cycles, and improved compliance.
Governing AI at Enterprise Scale
Multiple departments inside one enterprise quietly built fifteen separate AI chatbots. Each had its own APIs, its own data pipeline, its own vendor contract, and its own infrastructure. None of them talked to each other. Duplicate spend crossed $1.5 million before anyone had a complete inventory, and it took a public facing AI incident at a peer organization to force the executive team to act. This session is the operational story of what came next.
We walk through the governance model and shared capability approach that reduced AI infrastructure waste by roughly 60 percent, cut duplicate cloud spend, tightened compliance posture, and shortened time to deploy new AI use cases from months to weeks. The focus is not on the technology stack. The focus is on the governance decisions, the intake and architecture review process, the funding model changes, and the measurement discipline that made the transformation stick.
Attendees will see the actual AI Governance Committee charter, the intake process that catches duplicate solutions before budget is committed, the shared capability inventory that lets teams reuse authentication, data access, model hosting, and audit logging instead of rebuilding them, and the metrics dashboard that proves the model is working. We walk honestly through what did not work, including a failed first attempt at centralized approval that had to be redesigned into a lighter federated model within six months.
Key takeaways:
1. Identify duplicate AI capabilities across your organization that create 60 to 80% waste in engineering effort and infrastructure spending.
2. Design reusable AI capabilities (APIs, libraries, containerized services) that multiple teams can leverage across different products.
3. Build governance intake processes that catch duplicate solutions before spend is committed.
4. Calculate ROI of platform thinking using metrics like reduced cloud spend, faster delivery cycles, and improved compliance.
From Silos to Seamless. An AI Playbook for Enterprise Data Interoperability
Fragmented data across teams, systems, and vendors forces users to repeat their information, delays critical decisions, and creates operational gaps. This session explores how breaking down data silos enables coordinated experiences across the entire user journey through well designed APIs, event driven architectures, and consent aware data exchange. Learn from real enterprise implementations that reduced service gaps, improved satisfaction, and accelerated time to resolution through secure, connected ecosystems. Discover actionable strategies for seamless coordination while maintaining data privacy and security.
Key focus areas:
1. Implement secure data exchange strategies that enable coordination across teams while maintaining privacy and regulatory compliance.
2. Identify common data silos in your organization that force users to repeat their stories and create preventable gaps.
3. Build cross functional collaboration models that break down organizational silos.
4. Apply interoperability standards to build connected AI ecosystems that enable secure data exchange across health plan operations.
Engineering with Purpose. Building Sustainable Products & Platforms
As enterprise teams drive continuous delivery and cloud scaling, our environmental impact grows exponentially. Cloud native architectures and digital solutions consume massive computing resources, driving up both costs and carbon footprints. Through hands on experiences and practical strategies that have been piloted, tested and implemented, this session will share how to measure, monitor and optimize our systems for both performance and sustainability.
Drawing from real world case studies in Healthcare, this session explores practical approaches for integrating sustainability into your development practices. You will discover how to develop custom techniques for tracking environmental impact alongside traditional performance metrics, enabling you to build more intelligent capabilities using existing products and platforms. These smarter approaches will also help you significantly reduce unnecessary or unexpected cloud spend.
Key Learning outcomes of this session include:
1. Recognize how project based thinking creates unsustainable waste.
2. Apply product and platform operating models to eliminate redundant solutions.
3. Build reusable capabilities that reduce both cost and environmental impact.
4. Learn how to implement governance frameworks that prevent organizational resource waste.
Engineering with Purpose. Building Sustainable Products & Platforms
As enterprise teams drive continuous delivery and cloud scaling, our environmental impact grows exponentially. Cloud native architectures and digital solutions consume massive computing resources, driving up both costs and carbon footprints. Through hands on experiences and practical strategies that have been piloted, tested and implemented, this session will share how to measure, monitor and optimize our systems for both performance and sustainability.
Drawing from real world case studies in Healthcare, this session explores practical approaches for integrating sustainability into your development practices. You will discover how to develop custom techniques for tracking environmental impact alongside traditional performance metrics, enabling you to build more intelligent capabilities using existing products and platforms. These smarter approaches will also help you significantly reduce unnecessary or unexpected cloud spend.
Key Learning outcomes of this session include:
1. Recognize how project based thinking creates unsustainable waste.
2. Apply product and platform operating models to eliminate redundant solutions.
3. Build reusable capabilities that reduce both cost and environmental impact.
4. Learn how to implement governance frameworks that prevent organizational resource waste.
Engineering with Purpose. Building Sustainable Products & Platforms
As enterprise teams drive continuous delivery and cloud scaling, our environmental impact grows exponentially. Cloud native architectures and digital solutions consume massive computing resources, driving up both costs and carbon footprints. Through hands on experiences and practical strategies that have been piloted, tested and implemented, this session will share how to measure, monitor and optimize our systems for both performance and sustainability.
Drawing from real world case studies in Healthcare, this session explores practical approaches for integrating sustainability into your development practices. You will discover how to develop custom techniques for tracking environmental impact alongside traditional performance metrics, enabling you to build more intelligent capabilities using existing products and platforms. These smarter approaches will also help you significantly reduce unnecessary or unexpected cloud spend.
Key Learning outcomes of this session include:
1. Recognize how project based thinking creates unsustainable waste.
2. Apply product and platform operating models to eliminate redundant solutions.
3. Build reusable capabilities that reduce both cost and environmental impact.
4. Learn how to implement governance frameworks that prevent organizational resource waste.
Building Sustainable AI & Interoperable Systems
Enterprises waste 60 to 80% of engineering effort rebuilding AI capabilities across siloed teams. Learn how product and platform operating models enable interoperable, reusable AI infrastructure through governance frameworks and shared APIs. This session shares real enterprise case studies achieving 60% reduction in AI waste, faster delivery, and stronger compliance. Discover practical strategies to build once, scale everywhere, and drive sustainable innovation.
Key focus areas:
1. Identify duplicate AI capabilities across your organization that create 60 to 80% waste in engineering effort and infrastructure spending.
2. Design reusable AI capabilities (APIs, libraries, containerized services) that multiple teams can leverage across different products.
3. Calculate ROI of platform thinking using metrics like reduced cloud spend, faster delivery cycles, and improved compliance.
Beyond Automation. A Governance Framework for Third-Party Generative AI
Healthcare insurance organizations face increasing pressure to improve operational efficiency, reduce administrative costs, and enhance member and provider experiences. While artificial intelligence has demonstrated significant potential across healthcare operations, widespread adoption remains constrained by regulatory requirements, privacy concerns, legacy technology infrastructures, and governance challenges. This session proposes an operational framework for identifying where AI can be effectively deployed across healthcare payer workflows and evaluates the suitability of third party generative AI platforms such as ChatGPT, Claude, Microsoft Copilot, and Gemini within those environments.
The framework categorizes operational activities according to risk, regulatory exposure, and data sensitivity. It distinguishes between tasks suitable for enterprise AI implementation and those where third party AI tools can be safely utilized through appropriate governance controls. The session further examines compliance considerations including HIPAA, CMS regulations, state privacy laws, cybersecurity requirements, and emerging AI governance standards. Finally, it proposes practical guardrails that enable healthcare organizations to responsibly adopt third party AI solutions while protecting patient information and maintaining regulatory compliance.
While the case study centers on healthcare insurance, the framework applies to any regulated enterprise adopting ChatGPT, Claude, Copilot, or Gemini at work.
Key takeaways:
1. A risk based framework for categorizing enterprise workflows by AI suitability.
2. Practical guardrails for adopting ChatGPT, Claude, Copilot, and Gemini in regulated environments.
3. Compliance considerations across HIPAA, CMS, state privacy laws, and emerging AI standards.
4. Governance patterns that let teams use third party AI safely without creating shadow IT.
Beyond Automation. A Governance Framework for Third-Party Generative AI
Enterprises face increasing pressure to improve operational efficiency, reduce administrative costs, and enhance user and provider experiences. While artificial intelligence has demonstrated significant potential across enterprise operations, widespread adoption remains constrained by regulatory requirements, privacy concerns, legacy technology infrastructures, and governance challenges. This session proposes an operational framework for identifying where AI can be effectively deployed across enterprise workflows and evaluates the suitability of third party generative AI platforms such as ChatGPT, Claude, Microsoft Copilot, and Gemini within those environments.
The framework categorizes operational activities according to risk, regulatory exposure, and data sensitivity. It distinguishes between tasks suitable for enterprise AI implementation and those where third party AI tools can be safely utilized through appropriate governance controls. The session further examines compliance considerations including HIPAA, CMS regulations, state privacy laws, cybersecurity requirements, and emerging AI governance standards. Finally, it proposes practical guardrails that enable enterprise organizations to responsibly adopt third party AI solutions while protecting sensitive information and maintaining regulatory compliance.
Key takeaways:
1. A risk based framework for categorizing enterprise workflows by AI suitability.
2. Practical guardrails for adopting ChatGPT, Claude, Copilot, and Gemini in regulated environments.
3. Compliance considerations across HIPAA, CMS, state privacy laws, and emerging AI standards.
4. Governance patterns that let teams use third party AI safely without creating shadow IT.
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