By Karen Taylor, Director, Deloitte Centre for Health Solutions

The pharmaceutical (pharma) industry is no stranger to innovation; however, GenAI marks a turning point, with the potential to fundamentally reshape how we understand, diagnose, treat, and prevent disease. While GenAI is demonstrating its capacity to learn from massive datasets, and generate novel solutions in areas like drug discovery, it is poised to revolutionise the entire biopharma value chain. In December 2024, I was delighted to chair Deloitte’s first of three webinars exploring aspects of our Life Sciences and Healthcare Predictions 2030 series. The webinar ‘Accelerating the future: The rise of AI powered pharma innovation’ discussed the opportunities and considerations needed to accelerate the adoption of responsible, AI-enabled, solutions in pharma.¹ Our blog this week presents the key insights from this panel discussion.

About the webinar

From accelerated drug discovery to precision medicine and life-extending therapies, the future of diagnosis and treatment is predictive, preventative, precise, participatory and personalised. Our life sciences and healthcare (LSHC) predictions explore what this future might feel like for different stakeholders in the health ecosystem, the evidence today that informs our views of tomorrow and how AI technologies, specifically GenAI, might help bring the future closer.² Two of our predictions provide the backdrop to this webinar: The convergence of AI technologies and human expertise in pharma R&D and Interdependent innovations in science and technology are reshaping treatment paradigms.^3,4

Our eminent panellists were: Mishal Patel, Corporate Vice President, AI & Digital Research at Novo Nordisk, Haseeb Ahmad, former President of Novartis Europe, together with Deloitte Partners Seb Burnett and Hanno Ronte.

Unleashing a wave of innovation

The panellists agreed that AI, and specifically GenAI, is going to revolutionise the LSHC industry, with tangible impacts already emerging, with the real value residing in the data and algorithms that are used by the technology. Importantly, GenAI does not provide mere incremental improvements but drives a paradigm shift, unlocking entirely new possibilities in how pharma companies operate and innovate across the pharma value chain. Use cases include:

• Accelerating drug discovery - GenAI can analyse complex biological data from disparate datasets to generate new insights on human biology, identify the right targets and optimise the design of novel molecules. This is accelerating and improving the speed and precision of the traditionally time-consuming and costly drug discovery process. Moreover, its potential value is in identifying breakthroughs in treatments for currently untreatable diseases.
• Revolutionising clinical trials - GenAI can optimise trial design, by improving efficiency in setting-up trials and in identifying and recruiting suitable patients, as well as being able to collect and collate trial data in real-time, improve dossier submission and deliver faster, more cost-effective, more pragmatic, results. This in turn can bring hope to patients by reducing the time taken to get new medicines to market.
• Developing precision medicine and personalised therapies - by analysing large datasets of patient data, including genetic profiles, medical history, and lifestyle factors, GenAI can develop tailored treatment plans and predict responses to therapies, leading to more effective, targeted healthcare interventions.
• Enhancing patient engagement and adherence - GenAI-powered chatbots can empower patients by providing customised information, medication reminders, and support, improving adherence to treatment regimens and leading to better health outcomes. Furthermore, AI-enabled wearables can act as a ‘health coach in your pocket’ providing continuous monitoring, nudges and guidance.

Navigating the path to value realisation

However, while the potential of GenAI is undeniable, pharma companies need to focus on developing a strategic and measured approach to realise scale and unlock its full value. This involves considering the following six building blocks:

• Define a clear, comprehensive AI strategy that aligns with the organisation’s business objectives, including identifying specific use cases where GenAI can deliver the most impact and prioritising projects based on expected value, feasibility, and the metrics for success.
• Adapt the operating model to be more AI driven and build trust among employees and customers.
• Value realisation is more than just improving productivity; instead it should focus on the benefits that will improve the bottom line, like reducing working capital, increasing sales or saving money through less use of third parties and competitor intelligence.
• Foster a culture of data-driven decision-making including understanding that GenAI is only as good as the data it uses, requiring investment in a robust, platform based, data infrastructure, and building confidence in data quality and accessibility, and create an environment where GenAI insights inform both strategic and operational decision making.
• Developing in-house expertise in AI and data science and embrace new ways of working. This can involve upskilling existing employees, recruiting top talent, and establishing partnerships with leading AI research institutions but also educating customers.
• Embrace collaboration and partnerships as no single organisation possesses all the expertise and resources needed to fully leverage AI. Strategic collaborations with technology companies, research institutions, and even competitors can accelerate innovation and knowledge sharing.

Another important area is leadership and sponsorship, as driving meaningful change requires a strong support throughout the organisation. It can be CEO driven or domain led. Moreover, pharma’s tendency to adopt pilots should be accompanied by quick decision making on what and when to stop.

Addressing ethical considerations and building trust

As with any transformative technology, GenAI raises ethical considerations that needs to be addressed proactively:

• Ensuring data privacy and security - pharma companies handle highly sensitive patient data requiring a robust data governance frameworks, stringent security protocols, and transparent data usage policies to maintain patient trust and comply with evolving regulations.
• Mitigating bias and promoting fairness - GenAI algorithms are only as good as the data they are trained on making it crucial that potential biases in datasets are addressed and that GenAI-driven solutions are fair, equitable, and accessible to all patient populations.
• Maintaining transparency and explainability – ‘Black-box’ AI models can erode trust. It is essential, therefore, that pharma companies strive for transparency in how GenAI algorithms are developed and deployed and ensure that decisions derived from the algorithms can be explained and understood by stakeholders.
• Balancing innovation with regulation - as the regulatory landscape for AI in healthcare is evolving rapidly, pharma companies need to engage proactively with regulators to shape guidelines that foster innovation while safeguarding patient safety and ethical considerations.

The human element means augmenting, not replacing people

Importantly, GenAI is not intended to replace human expertise but to augment and enhance it. To make the most of an AI-enabled workforce requires pharma companies to:

• upskill employees - pharma professionals need to be prepared for a future where they work alongside and with AI-powered tools. Investing in education and training programmes that equip employees with the skills to leverage GenAI effectively is critical to an organsation’s overall success.
• redefine roles and responsibilities - GenAI will automate specific tasks, freeing up human experts to focus on higher-value activities that require critical thinking, creativity, and empathy. Organisations need to reimagine job roles and responsibilities to reflect this shift.

The future of biopharma is in building a collaborative ecosystem

The biopharma industry is on the cusp of a profound transformation, with GenAI the driving force behind this change. Success in this new landscape will require a collaborative approach, bringing together stakeholders from across the healthcare ecosystem, including biopharma companies, technology providers, regulators, and patients. Working together, will enable all stakeholder to harness the power of GenAI to accelerate innovation, improve patient outcomes, and shape a healthier future for all.

Our panellists provided four key takeaways:

• Strategic selection: given the multiple areas in which to apply GenAI, be careful where you choose to apply it and decide which areas have the potential to drive the biggest return on investment in both short and longer term. Be clear about what business challenges you’re trying to solve.
• Prioritising people: you can have the best technology but value can only be realised through winning hearts and minds of employees and customers. Focus on building trust, providing training, and fostering a culture that embraces AI adoption with a clear employee value proposition.
• Intellectual curiosity and adaptability: remain curious about AI's potential to reshape the industry and adapt strategies to leverage emerging opportunities.
• Customer-centric approach: ground AI initiatives in solving real-world problems and delivering value to both patients and healthcare professionals.

For those who would like to listen to the webinar for themselves please follow this link Accelerating the future of health | Deloitte UK.

Karen Taylor - Director, UK Centre for Health Solutions

Karen is the Research Director of the Centre for Health Solutions. She supports the Healthcare and Life Sciences practice by driving independent and objective business research and analysis into key industry challenges and associated solutions; generating evidence based insights and points of view on issues from pharmaceuticals and technology innovation to healthcare management and reform.

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¹ https://www.deloitte.com/uk/en/Industries/life-sciences-health-care/perspectives/accelerating-the-future-of-health.html

² https://www.deloitte.com/uk/en/Industries/life-sciences-health-care/collections/life-sciences-and-health-care-predictions.html

³ https://www.deloitte.com/uk/en/Industries/life-sciences-health-care/research/the-convergence-of-ai-technologies-and-human-expertise-in-pharma-r-and-d.html

⁴ https://www.deloitte.com/uk/en/Industries/life-sciences-health-care/research/interdependent-innovations-in-science-and-technology-are-reshaping-treatment-paradigms.html