Introduction

In a recent Elevate podcast, technology and healthcare leaders gathered to explore how cloud computing and AI transform life sciences. As the host, I welcomed Dr. David Rhew, Microsoft’s Global Chief Medical Officer and Dr. Sandesh Prabhu, HCLTech Senior VP, both of whom span clinical practice and technology leadership. Their message was clear: modernising legacy life sciences systems with cloud and AI is about delivering better patient care, streamlining operations and doing so ethically and responsibly. This summary distils their guidance for decision-makers aiming for pragmatic, collaborative and outcome-driven transformation.

Modernising legacy systems with cloud as an enabler

Healthcare and life sciences organisations often have decades of data and processes buried within siloed, outdated systems. The panel emphasised that the cloud is crucial for bridging the gap between old and new. “Legacy systems often do not talk to each other. The good news is, we have a way to bridge that gap: the cloud,” said Dr Rhew. Migrating or integrating legacy data with cloud platforms can break down silos and enable advanced AI analytics.

The urgency is clear: Everest Group reports that 70% of life sciences companies see legacy tech as a significant barrier to innovation. However, modernisation is more than moving old processes to the cloud; it’s about reimagining workflows for greater impact and efficiency. Dr Rhew referenced Henry Ford: “If I had asked people what they wanted, they would have said faster horses.” True transformation might mean going beyond the status quo entirely.

For example, rather than clinicians spending hours pulling data from scattered systems, a cloud-based platform can aggregate and analyse information in real-time. Gartner predicts that by 2027, 90% of healthcare and life sciences enterprises will adopt a hybrid-cloud IT strategy. HCLTech’s survey found that 80% of organisations have better results when they refactor legacy applications during migration, rather than simply “lifting and shifting.” Cloud’s real value emerges when applications and processes are modernised alongside infrastructure, unlocking AI, scalability and interoperability. The takeaway: cloud isn’t just an infrastructure choice, but a catalyst for change.

People, skills and partnerships: The human side of transformation

While technology is a key enabler, it's people who determine the success of digital transformation. Adopting cloud and AI requires new skills, mindsets and often a cultural shift. Employees need training and support to adapt to new tools and workflows. Dr Prabhu observed that people-centric challenges, like upskilling and changing habits, are often the toughest hurdles.

Organisations should invest in training, clear communication and leadership that supports change, so teams understand both the “why” and “how.” Partnerships can accelerate and de-risk the journey. “If someone wants to achieve all the benefits of AI quickly, the simple answer is an ecosystem of partners,” Dr Prabhu noted. Strategic partners can provide expertise in cloud migration, AI development and change management.

Collaboration with specialist providers—such as HCLTech working alongside Microsoft’s engineers—brings deep healthcare knowledge and proven frameworks. According to HCLTech research, 83% of enterprises that partnered with expert providers saw significant improvements in IT efficiency and performance post-migration. Partnerships also foster joint innovation, with joint labs and co-created solutions addressing complex healthcare challenges rapidly.

The message: you don’t have to do it all alone. Invest in people and partnerships to amplify your capabilities, bring fresh ideas and share the implementation load. Success comes from managing technology and people in tandem—cultivating adaptability and engaging with partners who complement your strengths.

Responsible AI: Driving innovation ethically and safely

As organisations integrate AI into healthcare, one theme stands out: the need for responsible, ethical AI. With sensitive data and patient well-being at stake, trust must begin with data security and privacy. Leading cloud platforms offer robust compliance with healthcare regulations and granular access controls. Secure, private data builds confidence in new systems.

Beyond security, ethical AI requires strong governance. Both Microsoft and HCLTech employ comprehensive AI governance frameworks. Dr. Rhew described Microsoft’s rigorous review of AI models for safety, fairness, transparency and reliability. Microsoft also offers customers tools to inventory, evaluate and monitor AI models for bias and ongoing performance. AI isn’t “set and forget”—it requires continuous oversight and retraining.

Explainability is critical in clinical settings. Healthcare professionals and patients must understand why AI makes certain suggestions. HCLTech’s Responsible AI Council ensures that its healthcare AI solutions provide human-interpretable justifications. Many organisations are establishing oversight committees—composed of doctors, scientists, ethicists and patient advocates—to guide AI use and approve high-stakes applications.

Gartner predicts that by 2025, most healthcare organisations will have formal AI oversight boards or ethics committees in place. Responsible AI is not a barrier to innovation, but a foundation for sustainable progress. Ethical guardrails—security, privacy, fairness, transparency, human oversight—must be embedded from the start, ensuring technology enhances care without compromising trust. As regulations evolve, those with strong AI governance will be best positioned for compliance.

Innovating for a democratised healthcare future

Looking forward, cloud and AI have the potential to democratise healthcare—making it more accessible, personalised and efficient. Some AI-driven improvements are already here, such as ambient clinical intelligence: systems that listen to doctor-patient conversations (with consent) and automatically generate notes, freeing clinicians from administrative burden and improving both patient and provider experiences.

AI also optimises hospital staffing, predicts patient no-shows and manages critical supply chains, leading to operational efficiency and savings. But the promise goes further. AI can address grand challenges like accelerating scientific research, expanding access to care in underserved areas and containing healthcare costs.

For example, Microsoft and partners have applied AI to retinal scans, enabling rapid, advanced screening for diseases like diabetic retinopathy and even early signs of heart disease or Alzheimer’s. These AI systems can deliver results in minutes without a specialist present, making them accessible in rural clinics or primary care offices. This “doctor in a box” innovation broadens access to advanced diagnostics.

High-performance cloud computing and AI also speed up research. Complex drug discovery and genomics analyses can now be conducted in days, not months, accelerating treatment development. Emerging applications combine AI with augmented reality—imagine an expert surgeon in one city guiding a procedure remotely via AR glasses and AI assistance. AI algorithms can also help manage medical devices, such as adjusting a neurostimulator for epilepsy remotely, reducing the need for frequent clinic visits.

Agentic AI—autonomous AI agents that can execute well-defined tasks and assist humans dynamically—holds promise for handling routine processes. Such agents might compile regulatory documents, monitor data streams from wearables, or triage patient queries, freeing clinicians for higher-level care and problem-solving. For example, an AI agent could quickly gather symptoms and direct patients appropriately, vastly improving efficiency across a health system.

However, this future requires robust governance and workforce evolution. Dr Rhew emphasised that Agentic AI must be monitored and designed for collaboration with humans. Tomorrow’s healthcare workers must be equipped to manage and work alongside AI assistants, so workforce training is essential. The goal is not to replace human touch, but to enable technology to help deliver the right care at the right time, everywhere.

Conclusion and call to action

Cloud and AI have become strategic pillars for thriving in the modern life sciences landscape. The experts agreed: To elevate organisational perspectives and unlock new value, modernise with purpose—use cloud and AI as instruments for better outcomes, not just digital transformation for its own sake. And do so responsibly, in alignment with your mission to improve lives.

If you’re a leader embarking on or accelerating this journey, focus on these three priorities:

• Start Small, Think Big: Identify a high-impact pilot project to implement cloud and AI, learn from its results and build momentum. But keep an eye on broader transformation—plan for how multiple initiatives will connect and scale over time.
• Embed Governance and Ethics from Day One: Form an AI governance working group, create clear policies for privacy, security and oversight and ensure all AI tools are tested for bias and audited for performance. Educate your staff on Responsible AI use, treating AI outputs as guidance, not gospel.
• Invest in People and Partnerships: Upskill your workforce to thrive in the cloud and AI era, fostering a culture of continuous learning. Partner with expert providers to gain capabilities and share innovation. Choose allies committed to your mission, not just the contract.

The opportunity to transform healthcare and life sciences with cloud and AI is here. Challenge your teams to identify pain points and opportunities for intelligent cloud solutions and foster an environment of responsible experimentation and learning. Most importantly, anchor your strategy to a vision of improving lives—patients, customers and employees alike.

The future of healthcare will be shaped by those who act. It’s not about becoming an “AI company” or a “cloud company” but about using these technologies to deliver greater human value. The time to turn bold ideas into outcomes is now—whether it’s launching a pilot, establishing an AI ethics board, or forging a new partnership. Modernise with purpose and elevate your organisation’s perspective. Those who do will not only transform their legacy businesses but also help define the future of healthcare.

Dr. Andy Packham is the Chief Architect and Senior Vice President at HCLTech. With nearly 30 years of experience in the industry, Dr. Packham drives innovation and strategic technology incubation within the Microsoft ecosystem unit.

Additional Resources:

• HCLTech Research – “Cloud Evolution: Mandate to Modernize” – This HCLTech report (2024) provides key insights from a global survey of 532 senior executives on cloud strategy, application modernisation and the rise of generative AI. It highlights trends like multicloud adoption, the importance of refactoring apps during migration and how partnerships improve outcomes. (HCLTech Cloud Research)
• HCLTech Blog – “Generative AI in Healthcare: Establishing Strategy and Governance for Successful Implementation” – This March 2025 blog discusses how generative AI is revolutionising healthcare and outlines the ethical, legal and governance frameworks necessary for its responsible adoption in life sciences organisations. (HCLTech Blogs)
• HCLTech Case Study – “Transforming pharma business with Microsoft 365 Copilot” - This resource provides additional context on the latest trends in cloud adoption and generative AI, offering data-driven insights and industry perspectives that complement the findings cited above. The link features commentary from industry experts and highlights successful strategies for leveraging cloud and AI in enterprise environments.