There is a radical shift underway in how businesses operate and their relationship with society. Across economies, sustainability has grown in importance, climbing from the periphery to the core of business strategy. As a critical sector for society, the Life Sciences sector faces the simultaneous challenges of being significantly affected by changes in our natural environment, social demographics, and geopolitics; facing intensifying scrutiny from the public and regulators; and competing with upstart companies that are disrupting healthcare with increasingly personalized solutions.
When it comes to sustainability, executives are being asked to step up, lead on key issues, and come up with solutions to help society address present and future challenges by contributing expertise and resources from their respective sectors. Companies are expected to demonstrate purpose and measurable value to both the economy and society. There is increased emphasis on the non-financial elements of annual reporting processes, and ‘time and resources must be invested into ensuring that non-financial data is as robust as financial data.’4
These shifts are catalyzing business transformation in the Life Sciences industry. Pharmaceutical, medical devices and technology, and biotechnology companies are at varying stages of pursuing more purpose-driven models, providing affordable therapies and products, improving ecological and environmental performance, and operating in a transparent and responsible manner.
The stakes are high, but now is not the time to hold back. Hesitancy risks a discordant response to sustainability challenges, exposure to more motivated competitors, reputational damage, and increased regulatory oversight. We are in a decade of not only hyper-connectivity, but hyper-interdependency, and the strategies and approaches that got us to this point need to be updated simultaneously to account for the greater sophistication of these systems. A confident, coordinated approach that involves all aspects of the business is the clear way forward for companies that are serious about the future.
Sustainable and digital transformations naturally complement one another and should be engaged together to maximize their value and impact. This dual transformation is especially important as incoming environmental, social, and governance (ESG) reporting requirements necessitate new and improved data solutions and architecture for collecting and analyzing information about Life Sciences companies’ activities.
While the Life Sciences industry is in transition, there is significant variance in the maturity level of different areas of data collection and measurement. Non-financial data architecture is nowhere near as sophisticated and robust as its equivalent in financial and accounting frameworks.5 For example, while there are clear metrics to assess carbon emissions, other social and environmental data can be more challenging to collect and assess, such as the company’s direct and indirect impact on biodiversity or contributions to improving the health outcomes of an un(der)served community.
That’s why it’s imperative for strategic investment in digital systems and technologies to include built-in processes to support non-financial reporting from the outset. Not only is this important from a compliance perspective, but insights gained from these processes also serve a critical strategic business function: helping companies to invest in ways that improve resource-use efficiency, increase profitability, and identify risks and opportunities. This can further bolster credibility when it comes to contributing solutions to global challenges faced by society and the environment.
With improved non-financial data architecture, companies can gain valuable insights into how their business is performing so that they can more precisely target areas of improvement and invest more effectively. This will also satisfy the expectations and needs of various stakeholder groups, such as regulators, investors, creditors, and top-level talent.
When it comes to sustainability, digital transformation also offers distinct benefits that go beyond meeting regulatory criteria. Having access to real-time data insights and leveraging new technologies can enhance Life Sciences companies' capabilities to act as a force for good. Among others, the applications for such information include finding new ways to improve medication adherence or efficacy, as well as being able to deliver more precise information to demonstrate transparency and build trust when engaging with patients and other stakeholders.
What’s more, the competitive landscape for digital transformation in Life Sciences is being enlivened by the increasing use of artificial intelligence (AI), with multiple potential benefits for society and the environment.
4 KPMG. (2023). Navigating CSRD Reporting in Life Sciences. Retrieved from: https://assets.kpmg.com/content/dam/kpmg/be/pdf/2023/ Navigating-CSRD-Reporting-in-Life-Sciences-July-2023.pdf
5 KPMG. (2023). Navigating CSRD Reporting in Life Sciences. Retrieved from: https://assets.kpmg.com/content/dam/kpmg/be/pdf/2023/ Navigating-CSRD-Reporting-in-Life-Sciences-July-2023.pdf
AI applications for Life Sciences are a direct response to market pressures and demand for faster innovation. AI and modelling-based clinical trials, for example, can lead to lower impact on the health of patients – such as by reducing adverse negative effects of a trial going wrong - and can reduce the environmental footprint of the entire trial taking place, while accelerating the development-to-launch solution life cycle.
The list of use cases for AI in the Life Sciences sector is already long, ranging from drug discovery and design, to clinical trials, medical device development, personalized medicine, biomarker identification, medical imaging and electronic healthcare record (EHR) analysis, among others.6
6 Burke, H. (2023). Top 20 ways Artificial Intelligence is advancing life sciences | Proclinical Recruitment Blogs. Proclinical. Retrieved from: https://www.proclinical.com/blogs/2023-4/top-20-artificial-intelligence-life-sciences
This list will only grow as AI technology becomes more sophisticated and scientific innovation develops alongside it. For example, using AI to trace and combat counterfeit drugs, or AI-enhanced data analysis to identify the right patient populations – or different patient populations with unmet needs – so that drugs and other medical interventions can be more efficiently developed than using the traditional ‘trial-and-error' method, so they can be made available to patients faster.7
AI can also improve efficiency in discovering therapeutic targets for small molecules, biologics (primarily antibodies), and gene therapy. It accelerates small molecule discovery by aiding in faster compound development, efficient compound selection, and reducing the overall number of compounds synthesized, potentially uncovering molecules overlooked by traditional high-throughput screening methods.8
Just as AI-enhanced data processes can lead to better product and service outcomes, it can help companies to design, measure, manage, and report on their sustainability activities, while helping to spot unexplored data patterns that can reveal opportunities for future investment in high-impact actions that contribute to the environment and society.
All of this also has implications for recruitment and retention, amid fierce competition for highly skilled talent. There is already a shortage of technical expertise, not just in the Life Sciences sector, but across the global labor market. To get the most value, it’s important for companies to be laser-focused on developing a workforce strategy that envisages the twin development of AI-driven innovation and sustainability capabilities from the outset, to avoid costly and time-consuming restructuring later.
When it comes to workforce planning, this means not just looking at recruitment and retention, but also how to responsibly leverage AI in a way that can support existing workforce output optimization amid this talent shortage, with due attention paid to regulatory, ethical, and HR-related concerns.9 Given this complexity, it makes sense for the incorporation of AI and the wider digital transformation to be considered in the context of a larger, systemic approach.
7 Whelan, S. (2024). Using AI to get medicines to patients faster. Drug Discovery from Technology Networks. Retrieved from: https://www. technologynetworks.com/drug-discovery/blog/using-ai-to-get-medicines-to-patients-faster-3838148 Whelan, S. (2024). Using AI to get medicines to patients faster. Drug Discovery from Technology Networks. Retrieved from: https://www. technologynetworks.com/drug-discovery/blog/using-ai-to-get-medicines-to-patients-faster-3838149 Price, B. (2023). Life Sciences: KPMG EMEIA Sector Growth Opportunities. Source. [Presentation].
Successful transformation requires an understanding of the complexity and interconnectedness of the regulatory, economic, social, and natural systems in which we work and live. This holistic ‘systems thinking’ is rooted in ‘thinking in terms of connectedness, relationships, patterns, and context.’10
Applying systems thinking to the challenge of sustainable business transformation, successful change management relies on coordinated communication and transparency between stakeholders in the organization (connectedness and relationships), while implementing a strategic combination of key initiatives
throughout the portfolio of business units, product portfolio, operations, and people management (patterns and context). It’s a highly dynamic process that requires focused and consistent leadership to choreograph and deliver, but – when coordinated as a whole system change – is key to shaping a leading position with stronger financial and impact returns.
10 Definition of systems thinking by Fritjof Capra, as quoted by Wayne Visser in Thriving. Source: Visser, W. (2022). Thriving: The Breakthrough Movement to Regenerate Nature, Society, and the Economy. Fast Company Press. ISBN-13: 978-1-63908-007-6
Develop and distribute affordable therapies and products to both support business objectives and have a more positive impact on society;
Improve ecology and environmental performance through better waste management and increased circular practices, shorter supply chains (e.g., positioning manufacturing and distribution centers closer to key customers and communities);
Implement robust and transparent sustainability governance to ensure that the organization’s activities are aligned with its sustainability objectives, including due diligence and monitoring of value chains to reduce risks related to Human Rights, animal welfare, and product stewardship; and
Ensure that relevant, coherent data can be collected and processed on these activities to comply with non-financial reporting and assurance requirements and help stakeholders understand how the company is performing.
Yes – we have an integrated digital and sustainability approach
Not currently - we are actively working on putting this in place
Not currently – we are interested in this approach but unsure how to proceed
No – this is not a priority for us
