Pragmatic use-case grounded transformation and Responsible AI adoption venumadhav November 6, 2025
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Pragmatic use-case grounded transformation and Responsible AI adoption

Insights from Uma Meyyappan [AI Transformation Executive Advisor, Hanger] and Jagan Mohan Jami [Chief Operating Officer, Acuvate]

About This Episode

In this thought-provoking episode of Coffee Conversations, Jagan Mohan Jami sits down with Uma Meyyappan, an industry veteran with decades of experience driving innovation in financial services and healthcare technology. Uma, who has held senior leadership roles at LPL Financial and Wells Fargo, was recognized among the Top 20 Women Leaders in the Bay Area, and is an inventor with 30+ U.S. patents. She shares her perspective on blending emerging technology, enterprise governance, and human ingenuity to create lasting transformation.

Together, Jagan and Uma explore what it takes to build innovation cultures that deliver measurable business outcomes not just shiny proofs of concept. From identifying the right use cases and managing technical debt, to setting up governance models and building high-performing teams, this conversation dives deep into the real mechanics of enterprise modernization.

Key Topics Discussed

  1. Innovation That Starts with Purpose
    Uma shares how necessity, not novelty, drives her approach to identifying pain points and designing simple, effective, scalable solutions.
  2. Balancing Push and Pull Innovation
    How leaders can blend push innovation (technology-led ideas) and pull innovation (business-led needs) to ensure every transformation effort has real impact and sponsorship.
  3. Managing Risk and Technical Debt
    In highly regulated industries like finance and healthcare, Uma emphasizes the importance of taking calculated risks — advancing innovation with “A leash-dog approach”. 
  4. From Innovation Lab to Business-as-Usual
    Uma shares her framework for transitioning ideas into real-world impact ensuring alignment with business priorities, securing buy-in, and maintaining momentum through pilots and POCs.
  5. The AI-Driven Enterprise
    How to pick the right AI use cases based on ROI, governance, and business relevance and how enterprises can accelerate responsibly.
  6. Governance for the AI Age
    A deep dive into AI governance frameworks including how to structure AI councils, define intake processes for new use cases, ensure explainability, and align models with data privacy and ethical standards

Watch the Full Conversation
Fill out the form to watch the complete episode and discover how purpose-driven innovation, guided by governance and enabled by emerging technologies like AI, can transform enterprises sustainably, responsibly, and at scale.

Responsible AI Adoption - FAQs

Enterprises must shift their focus from simply adopting emerging technology to calculating the Enterprise AI ROI Calculation. A Pragmatic AI Transformation Strategy prioritizes business value over hype. Whether a company is just starting or scaling, the goal is to view technology as an enabler for specific business pains. Future initiatives should aim to be “AI Native,” embedding AI at the core of the solution rather than adding it as an afterthought.

Innovation should always solve a genuine business problem. The most effective way to identify these problems is through a dual approach: 

  • Push: Technology teams analyze market trends and propose new solutions to modernize workflows. 
  • Pull: Business leaders proactively approach IT with specific pain points they need to resolve. 

This method ensures that innovation is not just about complexity but about doing things in a more efficient manner. 

When adopting nascent technologies, risk mitigation is critical. An AI Pilot to Production Roadmap suggests starting with a “Start Slow” approach: begin with a small, manageable use case and execute a Proof of Concept (PoC) or prototype. This allows the organization to evaluate the technology’s viability before committing significant resources. Leveraging reliable technology partners during this phase can also provide the necessary expertise to jump-start the journey. 

In regulated industries, innovation must be balanced with strict control often described as a “leashed dog” approach where boundaries are respected. A Responsible AI Governance Framework ensures that all AI solutions are explainable, data is properly masked, and privacy regulations are followed. To maintain Financial Services AI Compliance, organizations should establish a formal Governance Council to vet use cases, ensuring they meet both ROI targets and regulatory standards. 

Improving Developer Productivity in the AI Era requires moving beyond simple metrics like speed or lines of code. Instead, leaders should focus on providing clarity and equipping teams with AI tools that assist in code creation, testing, and DevSecOps. Success should be measured by meaningful, purpose-driven outcomes. By holding developers accountable for the impact of their work rather than just their output, organizations foster ownership and operational efficiency.