Healthcare

<h2 style="text-align: justify;"><span style="font-size: 12pt;">Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?</span></h2><p style="text-align: justify;">I am a global executive with 28 years of leadership experience spanning Technology, Operations, Finance, HR, Legal, and M&amp;A across the U.S., Europe, and India. I am a proven builder and leader of innovation-led Global Capability Centers (GCCs), focusing on transforming them into strategic value creators for multinational enterprises.</p><p style="text-align: justify;">Currently, I am the Managing Director (member of the board) of D2Sol India GCC and Corporate Vice President of D2Sol Inc. Apart from scaling D2Sol in India, I focus on influencing D2Sol&rsquo;s global strategy and operation optimization. Before D2Sol, I served as Country Head for Merative India GCC (member on board), overseeing end-to-end product delivery, professional services, and corporate functions across multiple locations (Bangalore, Hyderabad, and Chennai GCCs). I previously led IBM&rsquo;s Watson Health business unit from inception through its global scale-up and eventual divestiture, playing a pivotal role in driving product innovation and delivery excellence in digital health.</p><p style="text-align: justify;">With deep expertise in Digital Health Transformation, Government Health &amp; Human Services (HHS), Value-Based Care, and healthcare regulatory frameworks, I am widely recognized as a thought leader and speaker at technology and leadership forums. I bring a unique blend of operational depth and strategic vision, making me a trusted advisor on global delivery models, innovation ecosystems, intrapreneurship, and talent strategies.</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q2. What are the maturity levels of innovation-led GCCs across Tier I and Tier II Indian cities, and how is that expected to evolve by 2027?</span></h2><p style="text-align: justify;"><strong>Current Maturity Levels (2025)</strong></p><p style="text-align: justify;">Tier I Cities (e.g., Bengaluru, Hyderabad, Pune, Chennai, NCR)</p><ul style="text-align: justify;"><li>Medium to High maturity in innovation-led functions such as AI/ML, data science, product engineering, cloud, cybersecurity, and platform development</li><li>Deep integration into global enterprise innovation roadmaps</li><li>Strong partnerships with academia, incubators, and startups</li><li>Focus on design-led thinking, IP creation, and end-to-end product ownership</li></ul><p style="text-align: justify;"><strong>Tier II Cities (e.g., Coimbatore, Bhubaneswar, Chandigarh, Indore, Kochi, Jaipur)</strong></p><p style="text-align: justify;"><strong>Emerging to mid-level maturity</strong></p><ul style="text-align: justify;"><li>GCCs here are sometimes extensions of larger Tier I operations</li><li>Innovation is more execution-focused than driven by R&amp;D or product strategy</li><li>Talent pools are growing, especially in software engineering, QA, and DevOps, with a gradual movement toward AI/ML and analytics</li></ul><p style="text-align: justify;"><strong>Expected Evolution by 2027</strong></p><p style="text-align: justify;"><strong>Tier I Cities</strong></p><ul style="text-align: justify;"><li>Will remain innovation hubs but face growing cost pressures and saturation</li><li>Shift from execution + innovation to innovation-first roles, particularly in strategic R&amp;D, sustainability tech, and health-tech</li><li>Increased focus on intrapreneurship models and spinout startups within GCC frameworks</li></ul><p style="text-align: justify;"><strong>Maturity</strong>: Fully evolved centers of innovation and strategic decision-making</p><p style="text-align: justify;"><strong>Tier II Cities</strong></p><p style="text-align: justify;">Set to become next-wave innovation centers, driven by:</p><ul style="text-align: justify;"><li>Improved infrastructure and digital connectivity</li><li>Remote-first hybrid models are making Tier II talent more accessible</li><li>STP/SEZ and Government-backed IT clusters, startup incubator hubs</li><li>May begin to see domain-led innovation in BFSI, health-tech, and analytics</li></ul><p style="text-align: justify;"><strong>Maturity</strong>: Mid to high maturity, particularly in focused verticals with centers of excellence (CoEs).</p><p style="text-align: justify;"><strong>Key Drivers of GCC Innovation Maturity by 2027</strong></p><ul style="text-align: justify;"><li>Talent democratization across cities due to hybrid and flexible work models</li><li>Digital public infrastructure (like India Stack) enabling faster solution development</li><li>Shift to value-based KPIs &mdash; GCCs are also being measured on innovation outcomes, not just cost</li><li>AI, GenAI, and automation adoption are becoming core mandates from HQs</li><li>Stronger local innovation ecosystems &mdash; universities, startups, and skilling programs are catalysts</li></ul><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q3. What are the projected YoY growth rates in terms of the number of Life Sciences and Healthcare (LSHC) GCCs and workforce size between 2025 and 2030?</span></h2><p style="text-align: justify;"><strong>Number of LSHC GCCs</strong>: Expected to increase from approximately 100 in 2024 to over 200 by 2030.</p><p style="text-align: justify;"><strong>Workforce Size</strong>: Projected to grow from about 200,000 professionals in 2024 to over 420,000 by 2030.</p><p style="text-align: justify;">This projected growth is primarily driven by factors such as the increasing adoption of AI and automation in drug discovery, clinical trials, regulatory compliance, population health, and payor/provider segments. India&rsquo;s skilled and experienced talent pool in the US Healthcare ecosystem, tech adaptability of Indian developers, and cost efficiencies are the most important factors in this potential growth story.</p><p style="text-align: justify;">Recent case study: Bristol myers squibb GCC</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q4. How are healthcare GCCs using the emerging technologies to simulate biological processes, human cells, or clinical environments? Give examples</span></h2><p style="text-align: justify;">Healthcare GCCs are leveraging emerging technologies&mdash;such as AI/ML, digital twins, organ-on-chip, AR/VR, and quantum computing&mdash;to simulate biological processes, human cells, and clinical environments. These simulations are significantly disrupting drug discovery, diagnostics, and personalized care.</p><p style="text-align: justify;">Some examples below:</p><p style="text-align: justify;"><strong>Digital Twins of Human Physiology</strong></p><p style="text-align: justify;">Digital twins are real-time, virtual replicas of biological systems (organs, cells, or even entire patients).<br><strong>Use Case</strong>: GCCs are developing patient-specific digital twins to simulate disease progression and treatment outcomes.</p><p style="text-align: justify;"><strong>Examples</strong></p><p style="text-align: justify;">IBM Watson Health (India GCC) has leveraged AI-based modeling to simulate chronic disease management pathways and predict treatment adherence or risk.</p><p style="text-align: justify;">Philips&rsquo; Healthcare Innovation Center in Pune is exploring digital twins to simulate cardiac functions for personalized diagnostics.</p><p style="text-align: justify;"><strong>AI/ML in Drug Discovery &amp; Cell Simulation</strong></p><p style="text-align: justify;">AI and machine learning models are trained on molecular and genomic data to predict how cells or proteins react to various compounds.</p><p style="text-align: justify;"><strong>Use Case</strong>: Simulating molecular interactions, target validation, and toxicity at the cellular level.</p><p style="text-align: justify;"><strong>Examples</strong></p><p style="text-align: justify;">Novartis&rsquo; Hyderabad GCC uses deep learning to model protein folding and predict drug efficacy before clinical trials.<br>AstraZeneca&rsquo;s Bangalore center applies AI to simulate gene expression and optimize treatment regimens in oncology.</p><p style="text-align: justify;"><strong>Organ-on-a-Chip Technology</strong></p><p style="text-align: justify;">Microfluidic devices that mimic the functions of human organs at a cellular level.</p><p style="text-align: justify;"><strong>Use Case</strong>: Allows GCCs to simulate organ-level drug interactions without animal testing.</p><p style="text-align: justify;"><strong>Example</strong></p><p style="text-align: justify;">While R&amp;D is often centralized in HQs, GCCs like Johnson &amp; Johnson in India support simulation data analysis and chip-based testing environments for early-stage compounds.</p><p style="text-align: justify;"><strong>AR/VR for Clinical Simulations</strong></p><p style="text-align: justify;">Augmented and virtual reality tools to simulate surgery, patient behavior, or treatment delivery.</p><p style="text-align: justify;"><strong>Use Case</strong>: Training, prototyping, and remote diagnostics.</p><p style="text-align: justify;"><strong>Examples</strong></p><p style="text-align: justify;">GE Healthcare&rsquo;s Bangalore center uses VR to simulate clinical environments for device testing and physician training.</p><p style="text-align: justify;">Medtronic&rsquo;s R&amp;D center in Hyderabad is developing AR-based interfaces to simulate device usage in virtual anatomical settings.</p><p style="text-align: justify;"><strong>Quantum and High-Performance Computing (HPC)</strong></p><p style="text-align: justify;">Simulating molecular behavior at quantum levels for more accurate predictions in drug discovery.</p><p style="text-align: justify;"><strong>Use Case</strong>: Modeling quantum interactions in protein-ligand binding.</p><p style="text-align: justify;"><strong>Example</strong></p><p style="text-align: justify;">Pfizer&rsquo;s India GCC is collaborating on early quantum algorithm testing to simulate highly complex biological environments&mdash;currently more exploratory but promising.</p><p style="text-align: justify;"><strong>Synthetic Data for Clinical Trial Simulation</strong></p><p style="text-align: justify;">AI-generated data sets that simulate patient populations for trial design and scenario testing.</p><p style="text-align: justify;"><strong>Use Case</strong>: Reduces dependence on real-world patient data while improving trial accuracy.</p><p style="text-align: justify;"><strong>Example</strong></p><p style="text-align: justify;">IQVIA&rsquo;s India teams are deeply involved in developing and validating synthetic cohorts to predict clinical trial feasibility and optimize protocol designs.</p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q5. What&rsquo;s the potential for edtech-GCC partnerships to evolve into vertically integrated L&amp;D models for healthcare and digital tech talent?</span></h2><p style="text-align: justify;">The potential for EdTech&ndash;GCC partnerships to evolve into vertically integrated Learning &amp; Development (L&amp;D) models for healthcare and digital tech talent is both strong and strategically significant, especially as GCCs shift from cost centers to innovation hubs.</p><p style="text-align: justify;"><strong>Drivers of the EdTech &ndash;GCC Convergence</strong></p><p style="text-align: justify;"><strong>Talent Supply&ndash;Demand Gap</strong></p><p style="text-align: justify;">GCCs in healthcare and digital health demand hybrid skill sets (e.g., AI + clinical knowledge, regulatory tech + data privacy).<br>Tier I/Tier II cities need continuous skilling pipelines, especially in biostatistics, RWE (real-world evidence), regulatory affairs, interoperability standards (e.g., FHIR), and cloud-based health platforms.</p><p style="text-align: justify;"><strong>Need for Role-Ready Talent</strong></p><p style="text-align: justify;">EdTech platforms (e.g., upGrad, Coursera, Medvarsity, LinkedIn Learning) can co-create role-based skilling pathways aligned with GCCs&rsquo; workforce planning.</p><p style="text-align: justify;"><strong>Shift from Horizontal to Vertical L&amp;D</strong></p><p style="text-align: justify;">Traditional L&amp;D was generic (communication, leadership, coding).</p><p style="text-align: justify;">The new model will focus on industry-specific certifications, real-time labs, and in-house credentialing (e.g., digital therapeutics, health analytics, AI for diagnostics).</p><p style="text-align: justify;">How Vertically Integrated L&amp;D Models may Work</p><p style="text-align: justify;">&nbsp;</p><p><img style="display: block; margin-left: auto; margin-right: auto;" src="data:image/png;base64,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" width="628" height="345"></p><p style="text-align: justify;"><strong>Benefits to Each Party</strong></p><p style="text-align: justify;"><strong>For GCCs</strong></p><ul style="text-align: justify;"><li>Create just-in-time, role-ready talent</li><li>Improve retention through career progression paths</li><li>Reduce onboarding time with customized bootcamps</li></ul><p style="text-align: justify;"><strong>For EdTechs</strong></p><ul style="text-align: justify;"><li>Gain enterprise revenues and long-term pipeline deals</li><li>Enhance credibility with industry-aligned programs</li><li>Tap into domain-specific L&amp;D (healthtech, med-device tech), which is underserved</li></ul><p style="text-align: justify;"><strong>Real-World Cases of This Trend</strong></p><ul style="text-align: justify;"><li>Infosys Springboard and Coursera partnerships with GCCs for role-based upskilling</li><li>IQVIA, Novartis, and AstraZeneca are exploring internal L&amp;D academies for digital health, often co-powered by external</li></ul><p style="text-align: justify;"><strong>EdTechs</strong></p><ul style="text-align: justify;"><li>Government initiatives (e.g., Skill India + private sector coalitions) encourage GCCs to become L&amp;D hubs for local talent in healthcare and life sciences</li></ul><p style="text-align: justify;"><strong>2030 Outlook</strong></p><ul style="text-align: justify;"><li>We&rsquo;ll likely see EdTechs embedded inside GCCs (in a captive or BOT model) focused on domain-specific academies</li><li>Talent development will become hyper-personalized, modular, and real-time, using AI tutors, VR patient simulators etc</li></ul><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;">Q6. Which cybersecurity and regulatory-tech platforms are gaining traction among healthcare GCCs and may present investment opportunities?</span></h2><p style="text-align: justify;"><strong>Cybersecurity Platforms Gaining Traction</strong></p><p style="text-align: justify;">Zero Trust Architecture (ZTA): GCCs are implementing ZTA to ensure that every access request is authenticated, authorized, and encrypted. This approach minimizes the risk of data breaches by eliminating implicit trust within the network.</p><p style="text-align: justify;"><strong>Security Information and Event Management (SIEM) Systems</strong></p><p style="text-align: justify;">Platforms like Splunk and IBM QRadar are being utilized for real-time monitoring, threat detection, and incident response, providing a centralized view of security events across the organization.</p><p style="text-align: justify;"><strong>Endpoint Detection and Response (EDR)</strong>: Solutions such as CrowdStrike and SentinelOne are deployed to monitor and protect endpoints, enabling rapid detection and remediation of threats.</p><p style="text-align: justify;"><strong>Cloud Security Posture Management (CSPM)</strong>: With the migration to cloud services, GCCs are adopting CSPM tools to continuously monitor and manage cloud security risks and ensure compliance with industry standards.</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;"><strong>Regulatory Technology (Regtech) Platforms in Use</strong></p><p style="text-align: justify;"><strong>Automated Compliance Management</strong>: Platforms that automate the tracking of regulatory changes and manage compliance workflows are essential for GCCs to stay aligned with evolving regulations like HIPAA, GDPR, and FDA guidelines.</p><p style="text-align: justify;"><strong>Data Privacy Management Tools</strong>: Solutions facilitating data mapping, consent management, and data subject rights fulfillment help GCCs comply with data protection laws.</p><p style="text-align: justify;"><strong>Electronic Trial Master File (eTMF) Systems</strong>: These platforms streamline the management of clinical trial documentation, ensuring regulatory compliance and audit readiness.</p><p style="text-align: justify;"><strong>Risk Management Software</strong>: Tools that assess and monitor risks across various operations enable GCCs to proactively address potential compliance issues.</p><p style="text-align: justify;"><strong>Investment Opportunities</strong></p><p style="text-align: justify;">The growing reliance on cybersecurity and regtech platforms by healthcare GCCs presents significant investment&nbsp;opportunities.</p><p style="text-align: justify;"><strong>Cybersecurity Ventures</strong>: Given the increasing demand for robust cybersecurity measures, investing in companies that offer advanced threat detection, cloud security, and endpoint protection solutions can be lucrative.</p><p style="text-align: justify;"><strong>Regtech Startups</strong>: Startups that provide innovative compliance management and data privacy solutions are poised for growth as regulations become more stringent.</p><p style="text-align: justify;"><strong>Integrated Platforms</strong>: Companies offering integrated cybersecurity and compliance solutions tailored for the healthcare sector may attract substantial investment due to their comprehensive approach to risk management.</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?</p><ul style="text-align: justify;"><li>How is your platform purpose-built to meet the evolving compliance and data sovereignty demands of healthcare GCCs operating under multi-jurisdictional regulations like HIPAA, GDPR, and India&rsquo;s DPDP Act, while still delivering AI/ML-driven threat detection at scale?</li><li>How do you validate your platform&rsquo;s effectiveness in real-world GCC deployments (e.g., sandboxing, pilot studies, partnerships)?</li><li>What partnerships (cloud providers, audit firms, compliance bodies) are you leveraging to stay ahead of regulatory changes?</li><li>What percentage of your R&amp;D is dedicated to adapting for verticals like life sciences or med-tech?</li></ul><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p>