2026-07-04

Organisations are increasingly leveraging Artificial Intelligence (AI), data, and digital engineering to drive innovation and operational excellence. Greg Killian, Senior Vice President (SVP) and Global Head–Life Sciences and Health Care Business, EPAM Systems, discusses AI adoption, drug discovery, Global Capability Centers (GCCs) GCCs, and the future of AI-enabled pharma enterprises with Pharma Industrial India.

Q. Give us an overview of EPAM Systems and your role in driving its life sciences business globally.

Greg Killian: EPAM is global leader in Artificial Intelligence (AI) transformation, engineering, and integrated consulting, serving a broad range of customers from Forbes Global 2,000 companies to emerging startups. With over 30 years of expertise in custom software, product and platform engineering, we empower our clients to become AI-native enterprises, driving measurable value from innovation and digital investments. I lead the global life sciences and health care business wherein we help customers add value to their customers and patients across the health care value chain: from drug/therapeutic discovery through launch, supply chain, and to the provider and patient.

We sit deep inside our customers’ platforms and increasingly act as a co-innovation partner rather than a vendor. India is central to that, making it our largest delivery hub worldwide. The focus is on helping healthcare organisations translate technology investments into measurable business and clinical outcomes, including faster development cycles, stronger AI ready data foundations, better evidence at the point of care and digital experiences that create more meaningful engagement for providers and patients with innovative therapeutics.

Q. How can life sciences companies adopt AI at scale while balancing innovation, regulatory compliance and measurable business value?

Greg Killian: I would start with the clear principle that AI programs need to be closely tied to measurable outcomes such as development cycle reduction, cost efficiencies, market performance or commercial effectiveness. The industry is moving toward AI adoption across the entire enterprise, and organisations are increasingly prioritising use cases that demonstrate proven operational or business impact before scaling further.

Adoption at scale depends on 3 areas progressing together. First, organisations need data platforms designed for interoperability, flexibility and governance, so data can be used consistently across discovery, clinical, regulatory and commercial functions. Second, governance frameworks need to ensure that AI-generated outputs remain traceable, validated and aligned to regulatory expectations, particularly in areas tied to submissions, labelling or patient engagement. Evolving US Food and Drug Administration (FDA) and European Medicines Agency (EMA) frameworks are encouraging AI usage and we should take advantage of that rather than feel constrained. Third, organisations need change management across scientific, clinical and commercial teams, including clarity on where AI-driven outputs complement traditional deterministic systems.

Finally, enterprise data is the key to AI-driven innovation in pharma. Organisations are still working through challenges around data governance, cataloguing, modelling and quality. Without strong data foundations, scaling AI across scientific and operational workflows becomes significantly difficult and can lead to unintended consequences.

Q. Where do you see AI delivering the most tangible impact in drug discovery over the next few years, and what limitations still need to be overcome?

Greg Killian: Chemistry is relatively well mapped and modelled, while biology continues to rely heavily on identifying patterns across complex datasets. As a result, the most immediate impact of AI is emerging in two areas.

The first is ‘-omics’, where large-scale datasets and compute-intensive analysis are essential to identifying biological relationships. AI is helping accelerate the connection between biomarkers, treatment response and patient stratification, thereby supporting more targeted approaches to precision medicine. The second is Real-World Evidence (RWE), where AI can help analyse federated datasets to better understand patient outcomes and inform decisions across discovery, development and care delivery.

Data across the industry remains fragmented. Any AI-driven insight must meet high standards for accuracy, reproducibility and compliance, making strong data governance and interoperability essential. It is also important for organisations to clearly distinguish between probabilistic AI models and the deterministic outputs of traditional enterprise systems.

Q. How do partnerships between life sciences organisations and digital engineering companies accelerate innovation compared with traditional R&D models?

Greg Killian: Traditional R&D operating models have historically followed sequential processes and longer internal development cycles. Partnerships with digital engineering companies are changing that in two important ways. First, development is becoming more collaborative, with Proof-of-Concepts (PoCs), Minimum Viable Products (MVPs) and digital platforms in creasingly co-developed alongside sponsor teams rather than mere outsourcing. Second, these partnerships bring access to faster innovation with cloud, data and AI capabilities andpractices that result into enterprise-grade solutions.

RWE and real-world data capabilities are also helping connect different parts of the life sciences value chain more effectively. Increasingly, organisations are focusing on standardised data foundations to support AI and Generative AI (GenAI) applications across research, clinical development and post-market surveillance. Compared with traditional in-house models, this approach can help accelerate innovation by reducing the time required to translate scientific questions into usable digital and analytical capabilities.

Q. How do you see Global Capability Centres (GCCs) evolving—from support functions to strategic innovation engines within global pharma organisations?

Greg Killian: India has emerged as one of our fastest-growing regions for life sciences and health care and a strategically important global location. A similar shift is visible across many clients’ Global Capability Centres (GCCs). What began primarily as a delivery and operations base is evolving into a hub for higher-value work across AI-enabled commercial operations, digital patient services, regulatory analytics, and multiple facets of research. India today is a Center of Excellence (CoE) in multiple areas including regulatory, pharmacovigilance, and R&D activities.

The next phase of evolution for GCCs will depend on their ability to move further up the value chain by combining engineering capabilities with deeper scientific, clinical and commercial expertise. This includes building more cross-functional talent that can work across technology and domain contexts, as well as expanding ownership from process execution to broader business and innovation outcomes. GCCs that successfully make this transition are likely to play a more central role in global enterprise strategy and innovation.

Q. In your opinion, what factors are driving India’s emergence as a hub for life sciences innovation, and what must the ecosystem do next to strengthen this role?

Greg Killian: India offers a solid foundation of digital infrastructure, AI talent, and domain expertise. The next phase of biologics and personalised therapeutics innovation, which is increasingly digitally powered, creates a real opening for India’s data and AI ecosystem to contribute earlier to the drug development lifecycle. To strengthen this role, the ecosystem is now focusing on moving talent up the value chain by combining engineering skills with deeper scientific, clinical and commercial domain knowledge. That is what is turning India’s role from a delivery region into a hub of integrated development and innovation.

Q. Looking ahead to the next decade, how do you envision the fully AI-enabled pharmaceutical enterprise—from discovery and clinical trials to manufacturing and patient engage ment—and what organisational changes will companies need to make to achieve that vision?

Greg Killian: Agentic AI systems are likely to influence multiple phases of pharmaceutical R&D, including target identification, clinical trial optimisation, manufacturing, supply operations and patient engagement. The broader shift is toward standardised data environments that support AI and GenAI across life sciences research, clinical studies and post-market surveillance. In discovery, AI is increasingly being used to identify biological patterns across large multi-omics datasets. In clinical development, federated real-world data and digitally-enabled trial models are changing how studies are designed and executed.

Manufacturing and supply operations, particularly for cell and gene therapies and other personalised treatments, require real-time coordination across supply chains, laboratories and care settings that are highly manual and can benefit from the quality and efficiency benefits of agentic AI. In commercial and patient engagement, organisations are investing in connected digital platforms that integrate regulatory, workflow and patient experience requirements across interactions among pharmaceutical companies, physicians and patients with the modern chat/AI type of experience that people appreciate and increas ingly expect.

The operational and organisational requirements are equally significant. Companies need data platforms built around in teroperability, governance and flexibility to support scalable AI adoption across functions. Organisations will also benefit from stronger alignment among scientific, clinical and commercial teams, along with a clear understanding of how probabilistic AI models differ from traditional deterministic systems. In parallel, and in order to support this, co-innovation partnerships and capability centers are becoming a more established part of enterprise operating models for digital and AI-led transformation.