Healthcare is transformed through digital advancements like virtual care and AI has led to a shift from reactive acute care towards more proactive, personalised care. This includes a focus on specialised care in ‘smart’ hospitals and a rise in cost-effective home care enabled by technology like AI-powered contact centres and wearable biosensors. This data-driven approach, with a focus on population health management, aims to achieve a better patient experience, improved patient outcomes, lower costs, improved clinician well-being, and health equity. 

The world in 2030  
 

• AI-powered healthcare: AI is seamlessly integrated into the fabric of all healthcare technologies, from diagnostics and treatment decisions to administrative tasks like scheduling and resource allocation, aimed at supporting healthcare delivery by cohesive multidisciplinary teams.
  
• Proactive and preventative care: Healthcare systems have shifted more resources to prevention and leverage connected care, remote monitoring, and data analytics to identify potential health issues early, enabling timely interventions and a shift towards preventative care.
• Enhanced efficiency: Hospitals operate or share digital command centres utilising technology to optimise activities such as automated workflows, real-time patient monitoring, and flexible infrastructure, leading to improved productivity, patient satisfaction, and resource utilisation.
• Democratised health data: Healthcare data is digitised, secure and readily available to all care providers with GenAI-powered data insights enabling the delivery of 5P (predictive, proactive, personalised, participatory and precise) healthcare.
  

Overcoming cross-cutting constraints
 

There are several cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers by:

• employing comprehensive training to equip healthcare professionals with the skills to leverage AI, genomics, and virtual care technologies
• prioritising preventative care and incentivising positive patient outcomes, supported by diverse funding models that bridge investment gaps
• embedding ethical AI frameworks and robust audit processes to ensure compliance with regulatory requirements governing the use of AI-enabled medical devices and chatbots. Data science, cloud technologies and distributed ledgers have improved interoperability and the cyber-resilience, quality and completeness of health data.

Evidence in 2024

• Smart hospitals are rapidly growing: The smart hospital market is projected to reach $148.36 billion by 2029, integrating technologies like IoT, AI, and robotics to enhance patient care and operational efficiency.
• Remote patient monitoring is transforming healthcare: This market is expected to reach $78.4 billion by 2032, driven by its potential to improve patient outcomes, reduce hospital readmissions, and alleviate pressure on healthcare systems.

How AI/Gen AI might impact the sustainability of the healthcare ecosystem
 

• GenAI can streamline healthcare operations by automating tasks like electronic health record updates, improving patient flow, and enabling predictive modelling for crisis preparedness.
• GenAI-powered tools can provide continuous support to staff, personalise patient interactions, and simplify healthcare navigation, leading to increased satisfaction and productivity.
• By automating administrative tasks and providing patient support, GenAI frees up healthcare professionals' time, allowing them to focus on direct patient care.
• To effectively integrate GenAI into their workflows, healthcare professionals need to enhance their data fluency, technical skills, and understanding of ethical AI practices.

The healthcare and life sciences sector recognises its significant role in addressing the climate crisis. While healthcare systems vary in their environmental impact, providers are increasingly prioritising sustainability and climate resilience through mature ESG strategies. These strategies focus on science-based targets, net-zero emissions, and climate-resilient healthcare delivery. Efforts include reducing direct emissions, influencing supply chain sustainability, and leveraging data-driven insights to improve environmental performance. This commitment to sustainability strengthens stakeholder trust, attracts and retains talent, and aligns with evolving regulatory requirements.

The world in 2030 
 

• Sustainable care delivery models: Digitally-enabled care models like telehealth and virtual consultations reduce the environmental footprint of healthcare by improving patient triaging and decreasing reliance on carbon-intensive hospitals and patient travel.
• Sustainable supply chain management: Healthcare systems are prioritising sustainable procurement practices, demanding transparency and evidence of environmental responsibility from suppliers while leveraging AI for efficient logistics and waste reduction.
• Transitioning to renewable energy and circular economy: Healthcare providers are actively transitioning to renewable energy sources and embracing circular economy principles by prioritising reusable and recyclable products to minimise their environmental impact.
• Collaborative action and data-driven insights: Healthcare stakeholders are collaborating to achieve net-zero ambitions, using standardised sustainability metrics, AI-powered platforms, and employee engagement to measure, track, and reduce their environmental footprint.
  

Overcoming cross-cutting constraints
 

There are several cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers by:

• equipping leaders with specialised expertise in climate change to drive sustainability initiatives, while developing educational programmes and incentives to empower employees to adopt a net-zero mindset
• recognising capital investments in green energy and sustainability projects as opportunities for growth and attracting investors through green bonds and transparent ESG reporting
• building a comprehensive regulatory landscape, standardised sustainability reporting metrics, and robust data management systems to ensure accountability, transparency, and comparability across the healthcare ecosystem.
  
  

Evidence in 2024
 

• Global impact of climate change. By 2050, climate change could lead to an additional 14.5 million deaths and US$12.5 trillion in economic losses worldwide. Left unaddressed, these losses may exceed US$175 trillion by 2070.
• The global health sector is responsible for 5% of global greenhouse gas emissions. If it were a country, it would be the fifth largest emitter on the planet, according to a study carried out by the NGO Health Care Without Harm (HCWH). Seventy-one per cent of these emissions come from the supply chain, with the remaining 17% coming from the health facilities themselves and 12% from their energy consumption.
  

How AI/Gen AI might impact the sustainability of the healthcare ecosystem
 

• GenAI can enable proactive identification of energy and waste hotspots, optimise energy consumption, and enhance supply chain management, leading to a more sustainable healthcare system.
• GenAI can streamline clinical trials by optimising data collection, improving patient recruitment, facilitating remote monitoring, and reducing the need for travel, thus minimising environmental impact.
• GenAI-powered predictive analytics can optimise resource allocation, personalise treatment plans, and minimise waste in healthcare settings.
• However, it is critical to acknowledge that data collection, storage and analytics have significant energy demands and so contribute to GHG emissions and mitigation of these environmental impacts is essential. 

The biopharma industry has undergone a digital transformation, leveraging AI, automation, and patient-centric technologies to significantly accelerate R&D timelines and improve productivity. This digital-first approach, combined with strategic collaborations and innovative funding models, has led to faster delivery of groundbreaking therapies for previously untreatable diseases; new life-extending treatments for some of the most highly prevalent diseases such as cancers, diabetes, cardiovascular and neurodegenerative diseases; and a greater emphasis on preventative treatments. As a result, the return on investment (ROI) in biopharma innovation in 2030 has increased year-on-year since 2023.

The world in 2030 
 

• AI and advanced technologies accelerate drug discovery: GenAI, in-silico research, and advanced gene editing techniques are expediting and refining the drug discovery process, leading to more personalised and cost-effective treatments.
• Hybrid trials and data-driven optimisation: AI, along with quantum computing, streamlines drug development by optimising trial design, generating drug performance insights, and automating reporting. AI-enabled clinical trials enhance patient recruitment, monitoring, and data analysis, ultimately speeding up timelines. 
• Diversity, real-world evidence, and equitable drug development: The integration of real-world evidence, AI-driven clinical trial recruitment and retention, and a focus on diversity in clinical trials ensures the development of more equitable and representative healthcare solutions.
• Strategic partnerships and collaborations: Biopharma are pursing M&A to acquire innovative pipelines, replenishing assets impacted by the patent cliff, and bolster internal R&D capabilities, particularly in areas like AI-driven drug discovery and gene editing. This is complemented by strategic partnerships and collaborations to access cutting edge-technologies and accelerate the development of novel therapies.   
  

Overcoming cross-cutting constraints
 

There are four cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers, for example by:

• Fostering a workforce skilled in engineering, computational science, and biotechnology, while collaborating internally with clinicians, scientists and the supply chain and commercial functions, and externally with academia, contract research organisations, AI for drug discovery, big tech and metaverse companies to drive innovation.
• Adopting public-private partnerships, subscription-based drug access, and value-based care models to incentivise the development of novel therapies and shift the focus towards patient outcomes.
• Streamlining trial processes, risk-based monitoring, and robust cyber and data security measures to ensure compliance and accelerate approvals, while simultaneously navigating the evolving AI, pricing and access global regulation landscape.
  

Evidence in 2024  
 

• Success rates are higher for AI-discovered drugs: As of December 2023, 24 AI-discovered molecules have completed phase I trials, of which 21 were successful. This suggests a success rate for phase I trial AI-discovered molecules of 80-90%, substantially better than historical industry averages (40-65%).
• The potential of decentralised clinical trials (DCTs): Tufts Center for the Study of Drug Development found that DCTs are associated with reduced clinical trial timelines and substantial extra value to sponsors developing new drugs. If DCT methods are applied to both phase II and phase III trials, the value increased by US$20mn per drug that enters phase II, with a seven-fold increase of ROI. They also have lower screening failure rates and fewer protocol amendments. 
  

How AI/GenAI might impact R&D  
 

• Biopharma companies can unlock substantial financial value in R&D, by scaling up their AI capabilities for drug discovery and clinical trials.
• AI and GenAI can accelerate drug discovery by modelling protein structures, analysing molecule libraries to identify promising drug candidates, and enabling drug repurposing and personalised treatment options based on genetic profiles.
• GenAI can streamline clinical trials by automating documentation, optimising trial design and patient recruitment, and enhancing data analysis, leading to faster and more cost-effective drug development.
• GenAI can enable real-time monitoring of clinical trial data, facilitate the use of digital twins, and automate report generation, improving the efficiency and quality of evidence generated during trials.

Technological and scientific innovations are transforming healthcare delivery. The integration of quantum computing, AI, and diverse health data sources from MedTech devices, wearables, and genomics, enables precise diagnostics and the development of life-extending therapies. Real-time population health profiles, ethically constructed from this data, facilitate the identification of disease drivers and the creation of advanced, personalised treatments, building on earlier breakthroughs in gene therapies and immunotherapy. These advancements, along with innovations in pharmacogenomics, nanotechnology, and implantable devices, have significantly increased survival rates for some diseases.

The world in 2030 
 

• Genomic advancements: Rapid genomic data analysis enables accurate diagnoses, personalised treatment plans, and enhanced survival rates, contributing to improved population health outcomes.
• Multi-omics and microbiome therapies: Technologies like proteomics and metabolomics, alongside microbiome-based therapies, provide a deeper understanding of human biology and offer innovative treatment options for various diseases.
• Targeted therapies and diagnostics: Developments in vaccine technology, drug delivery systems, and liquid biopsy assays allow for more precise diagnoses and more targeted and cost-efficient healthcare interventions.
• Neurotechnology and personalised mental health: Quantum computing, brain-computer interfaces, and customised mental health treatment plans based on genetics and biomarkers are revolutionising neurological and psychological care.
  

Overcoming cross-cutting constraints
 

There are four cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers, for example by:

• attracting talent with expertise in clinical pharmacology, computational biology, AI, and regulatory compliance as well as clinicians who possess strong digital skills and a deep understanding of multi-omics and AI-driven treatments.
• adopting outcomes-based funding models that incentivise innovation and equitable access to healthcare; and establishing platform-based business models to streamline data sharing and collaboration within the healthcare ecosystem.
• enabling regulatory bodies to balance innovation with consumer protection, incorporating real-world evidence into decision-making; and embedding robust cybersecurity measures to ensure data integrity and patient privacy.

Evidence in 2024  
 

• Gene editing and cell and gene therapies (CGTs) are advancing rapidly: The UK's approval of Casgevy for sickle cell disease and β-thalassemia highlights the potential of CRISPR gene editing, while the growing market for CGTs from US$5.3bn in 2022 to $19.9bn in 2027 signals a shift towards personalised advanced medicine, despite high costs prompting innovative business models.
• Advances in neurological treatments: Driven by 23 novel therapies for the treatment of agitation and disease-modifying therapies, the global Alzheimer’s disease market is projected to grow by 20% annually to reach US$13.7bn by 2030. Additionally, Aarhus University have found that it is possible to predict the risk of developing psychiatric disorders using genetic analysis, paving the way for better prevention and treatment.
  

How AI/GenAI might impact treatment and diagnostic paradigms  
 

• GenAI can analyse diverse datasets, including genomics, clinical history, and social determinants of health, to provide deeper insights that can revolutionise healthcare delivery.
• GenAI enables personalised treatment plans by integrating polygenic risk scores with behavioural insights, extending care beyond traditional settings through virtual coaching and remote monitoring. 
• GenAI can accelerate the discovery of new treatments, optimise medication dosages, predict adverse drug reactions, and enhance supply chain management, ultimately improving patient outcomes and healthcare efficiency.
• GenAI can enhance medical imaging analysis, automate radiology reporting, and facilitate the creation of customised educational materials in multiple languages and literacy levels.

The future of health, wealth, and longevity services hinges on a collaborative approach between governments, healthcare providers, health and long-term care insurers, employers, and technology companies. This shift is driven by ageing populations and declining birth rates, prompting governments to explore new funding models and flexible retirement options to improve well-being and reduce economic inactivity. Platform-based technologies will play a crucial role in integrating services, enabling data sharing and exchange of goods and services, and offering tailored solutions that promote healthy ageing and financial well-being. This integrated ecosystem will empower individuals to make informed decisions about their health, wealth, and longevity.

The world in 2030 
 

• Focus on prevention and well-being: Health system partners invest heavily in preventative care and well-being initiatives to promote healthy ageing, reduce demand and costs of healthcare, enhance workforce productivity, and reduce economic inactivity. As employers, they foster inclusive workplace well-being cultures through flexible work arrangements, continuous learning and in supporting physical, mental, and financial health.
• Engaging and retaining employees: Employers identify potential risks like employee burnout and address the root causes of absenteeism and turnover. They utilise predictive analytics and big data to develop targeted well-being initiatives that are crucial for attracting and retaining talent, supporting return-to-work, and enable individuals to remain economically active beyond traditional retirement age.
• Reimagining health insurance and pension policy design: AI and big data have revolutionised health and financial protection enabling the analysis of vast health related data sets, identifying trends, and predicting costs, leading to tailored, technology-enabled solutions, such as InsureTech and AgeTech, lowering costs and improving health outcomes.   
• Public and private stakeholders partner to improve health equity: System partners engage actively with local communities, supporting health and financial literacy initiatives, promoting well-being and narrowing the health-wealth gap between income groups. 

Overcoming cross-cutting constraints

There are several cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers by:

• Investing in continuous learning to upskill and retrain individuals, in digital skills and data security and co-create strategies to support people to remain healthy and financially secure as they age 
• Embracing regulations to improve consumer protection and financial literacy, anti-fraud and discrimination. Regulations also incentivise system collaboration and adoption of tech-enabled innovation between financial, medical and social services
• Building secure and interoperable data platforms, with robust cybersecurity measures, transparent data governance practices and user-friendly interfaces, to deliver personalised and effective health and wealth solutions. 
  

Evidence in 2024  
 

• AgeTech industry is growing rapidly: AgeTech refers to digital and mechanical technologies that aim to prolong physical wellness, housing, insurance, functionality and well-being of older adults and assist their caregivers, and in 2024 involved some 300 startups with the market estimated to reach US$2tn by 2025.  
• Mental health costs to employers: The cost to employers of poor mental health is £51bn per year in 2023, a decrease from £55bn in 2021, but an increase from £45bn in 2019. Presenteeism is the largest contributor (some £24bn annually). Importantly, an analysis of employee mental health interventions found that on average, for every £1 spent on supporting their people’s mental health, employers get nearly £4.70 back in improved productivity. Early interventions, such as organisation-wide culture change and education, provide the highest returns.
  

How AI/GenAI might enable healthier and wealthier ageing  
 

• AI can power customised wellness programmes and tailor insurance coverage based on individual health data. Integrated systems combining health records, insurance data and real time health metrics could provide a holistic view of a patient’s health, enhancing both preventive and acute care.
  
• GenAI models can analyse health data, genetics, and lifestyle factors to predict potential health risks, enabling personalised recommendations for nutrition, mobility, and brain health, leading to better outcomes and reduced economic inactivity.
• AI algorithms can assist individuals with financial planning, investment strategies, and wealth management, ensuring financial security during retirement.
• AI-driven platforms, and devices can combat loneliness by facilitating social interaction and enhancing independent living through tools like virtual companions, smart home devices, and accessibility aids.

Pharma's commercial operations have undergone a complete digital transformation, leveraging AI and data cloud providers, and customer relationship management (CRM) providers to streamline processes and shift from a product-centric to a customer-centric approach. This has led to personalised marketing and support, improved customer experiences, and reduced costs. Pharma companies are also adopting innovative pricing models, outsourcing non-core functions, and prioritising AI-powered pharmacovigilance and patient support programmes to ensure medication safety, equitable access, and better health outcomes.

The world in 2030 
 

• 360-degree customer view: Pharma companies are using AI  to create a holistic view of their customers, integrating internal and external data to develop a deep understanding of buyer needs and behaviours for targeted engagement.
• Data-driven stakeholder engagement: AI-powered CRMs and real-world data (RWD) enable early and effective communication with stakeholders, demonstrating product value, improving launch while optimising commercialisation strategies.
• Personalised omnichannel experiences: Companies leverage data to segment markets effectively and use dedicated customer relationship teams to deliver tailored omnichannel campaigns, ensuring messages resonate with individual stakeholder needs and accelerating time-to-value.
• Patient-centric approach: Marketing technologies and budgets are shifting towards prioritising the patient experience, with dedicated teams using RWD to understand patient needs and deliver superior support through various touchpoints.
  

Overcoming cross-cutting constraints
 

There are several cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers by:

• Cultivating a workforce proficient in data analytics, AI and digital engagement and fostering an agile and entrepreneurial culture than embraces new commercial models and customer-centric approaches
• Optimising commercial strategies with data-driven incentive structures, advanced CRM systems, and a shift towards value-based pricing models that prioritise patient outcomes and affordability
• Balancing innovation with regulatory compliance through robust data privacy measures, proactive risk management, and the use of advanced analytics to ensure transparency and ethical data practices.

Evidence in 2024  
 

• Strategies to improve HCP engagement: a top‑10 biopharma company unlocked 14% higher sales in just 9 months by activating next best action programmes and partnering with Aktana to bridge the gap between strategy and execution. This resulted in sales reps reaching healthcare professionals (HCPs) who had been unresponsive in the past, uncovered opportunities with HCPs who weren’t on their radar before, and pinpointing the right time and right content on digital channels to maximise engagement and proactively identify timely patient alerts.
• Digitalising manufacturing and inventory management: Sanofi has developed an in‑house AI‑enabled yield optimisation solution which learns from experience to achieve consistently higher yield levels. This helps to optimise usage of raw materials, contributing to the company’s environmental objectives, and supporting improved cost efficiency. Adoption within Sanofi’s biopharma supply chain has enabled the team to predict 80% of low inventory positions, allowing them to take mitigating action to quickly address the shortfall.
  

How AI/GenAI might impact pharma’s commercial model
 

• GenAI can analyse patient data to tailor marketing messages, create engaging content, and optimise ad spending by targeting HCPs and patients with personalised, timely information.
• AI can facilitate more successful product launches by analysing market trends and predicting demand; can empower sales reps by personalising outreach to HCPs and patients; and by providing tools like chatbots can enhance interactions with HCPs and patients.
• AI can analyse vast datasets to identify potential drug safety signals faster, enabling proactive risk management and improving pharmacovigilance efforts. 
• GenAI can personalise patient support programmes, leading to better adherence and health outcomes and can also identify individuals who may be at higher risk of experiencing adverse events, enabling proactive intervention and personalised risk management.

MedTech companies play an integral part in most patient treatments, with digital disruption transforming the industry into a more connected, efficient, agile, and customer-centric ecosystem. Connected medical devices generate, collate, analyse and transmit substantial amounts of health data, which is then integrated into electronic health records (EHRs) via cloud computing and AI technologies, enabling more effective diagnosis, monitoring, and treatment. Advances in wireless technology, connectivity, miniaturisation and computing power are a ‘force multiplier’ in unlocking the potential of emerging medical technologies as part of the Internet of Medical Things (IoMT). MedTech companies increasingly focus on personalised and preventative therapies, leading the shift towards value-based healthcare. 

The world in 2030 
 

• Advances in digitalisation and connectivity: Connected devices, including patient-centric technologies such as wearables and smart implants, provide real-time precise and interoperable data to create end-to-end information chains across the entire healthcare ecosystem, enabling care anytime, anywhere. 

• Fast-paced innovation: MedTech companies have invested in building smart factories, integrating and scaling disruptive technologies and using predictive analytics and AI to improve asset and process efficiency, inventory and capacity tracking, preventative servicing, demand management, order fulfilment and supply chain resilience to deliver a strong return on investment. 

• Care is delivered everywhere: Diagnostic devices are smaller (e.g., MRI machines the size of a tumble-dryers, CAT scanners small enough to sit on a table and pocket-size ultrasounds are ubiquitous) and MedTech companies that provide telemedicine augmented by virtual reality technologies support clinicians to deliver more accessible, equitable remote care. 
  

• MedTech companies have adopted ambitious ESG goals: They use eco-friendly and/or durable materials, work with end users to reuse and recycle devices, minimise waste and energy and embrace the circular economy. 
  

Overcoming cross-cutting constraints

There are several cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers by:  
 

• adopting build, buy, and partner strategies to upskill the organisation, including collaborating with consumer, big tech and digital health companies to benefit from their experience of brand and customer-centric engagement 
• implementing value-based pricing, risk and gain-sharing agreements, and adopting subscription and as a service business models to deliver more integrated offerings 
• using ‘regulatory-by-design approach’ to comply with evolving regulations, including digital and AI, the Corporate Sustainability Reporting Directive, and patient data (e.g., HIPAA and GDPR), and submitting real world evidence to accelerate the approval of innovation. 

How AI/GenAI might impact the MedTech industry  
 

• GenAI applied across MedTech value chain can lead to more efficient processes, personalised customer interactions and end-to-end visibility across the supply chain. 

• GenAI can enhance diagnostic imaging and analysis to detect diseases at earlier stages, and facilitate the development of innovative solutions and designs, including new biomaterials tailored to different population groups. 

• GenAI can accelerate software development by automating coding, testing, and data generation, enabling the scaled adoption of Software as a Medical Device (SAMD) business model. 

• AI-powered chatbots and multilingual software can improve digital health literacy and provide personalised support to diverse patient populations. 

Over the past five years, M&A activity has rebounded strongly, driven in part by a step-up in investor activism. M&A has become a critical element in the corporate strategy of every life sciences company, helping to unlock growth and innovation and replenish product portfolios. Divestments of non-core assets, alongside implementing operational efficiencies and streamlining portfolios, released capital to invest in new products, drive growth and help restructure the business. Strategic partnerships and alliances within and between the different life sciences sectors have driven external innovation. For example, partnerships between pharma and technology platform players have become a crucial investment strategy with acquired skills in advanced analytics and GenAI enhancing pharma companies’ approach to M&A.

The world in 2030 
 

• Pharma has used M&A to replenish their portfolios: Growth in acquisitions has helped offset ‘loss of patent exclusivity’ for most large pharma companies. Some pharma companies have focused more on traditional therapeutic areas like non-communicable and rare diseases, whereas others have invested in acquiring new drug classes (building on the success of mRNA platforms, antibody-drug conjugates, and cell and gene therapies).

• M&A activity has led to the consolidation across the life sciences industry: For example, mergers of equals, spin-offs and portfolio consolidation have led to the rise of a dynamic consumer health industry; and busy M&A activity has moved the dial for MedTech from point solutions to end-to-end workflows; while Biotech companies focus increasingly on partnerships with larger players, in order to access resources, expertise and new markets.

• M&A has fuelled cross sector convergence: Dealmakers focus on assets coming to market that blur traditional sector boundaries between industries (such as diagnostics, wearables, subscription-based healthcare and Software as a Medical Device (SAMD)).

• Companies look for assets to improve their ESG profile: Life sciences companies are willing to pay a significant premium for assets that have strong ESG credentials recognising it as a factor for increasing value.

Overcoming cross-cutting constraints

There are several cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by life sciences companies turning the constraints into enablers by:

• acquiring the skills to assess potential targets and external innovations quickly including developing or acquiring talent with AI/GenAI skills to help companies compete effectively in deal-making

• building on private financiers’ appetite for investment in life sciences, and engaging with private equity companies across both the buy and sell side especially in assets that can potentially cure or prevent diseases.

• investing in regulatory specialists to co-manage M&A deals from early in the negotiations through to monitoring compliance across the whole process giving regulators more confidence in the deal.

Evidence in 2024

M&A activity is expected to pick up in 2025: The value of M&A deals in life sciences reached US$163bn in 2023 (deals announced up to the end of October), surpassing the US$135bn figure for 2022; for the pharma segment, the value of M&A activity in 2023 exceeded the same period in 2022 by 35%. Among life sciences suppliers, the value of M&A deals increased by nearly 85% year on year to US$28.3bn. Total deals in MedTech, however, fell by nearly 45% year on year to US$13.5bn, as MedTech companies focused on divestments and reorganisations, although deal volume increased.

Pharma’s ‘patent cliff’ requires a more focused proactive approach: between 2022 and 2030, pharma companies will likely lose more than US$236bn in revenue from the anticipated ‘patent cliff’, as 190 drugs (including 69 blockbusters) lose exclusivity. This represents some 46% in sales at risk for the top ten pharma companies over the next decade. Biopharma is therefore looking for innovative assets to fill the gap in their product portfolios, either by increasing R&D spend or through inorganic growth and M&A. They are also reviewing their portfolios to divest lower margin generic products and non-core facilities.

How AI/GenAI might impact M&A and divestments  
 

• GenAI, can be deployed to make effective use of data, insights and benchmarks from past M&A transactions and divestments optimising value and enabling more precision valuations. 

• GenAI can help source and screen target deals (using scientific literature, clinical trial data, and patent filings to identify promising new therapies and technologies) or conduct due diligence (by analysing large volumes of contracts and financial statements to identify potential risks and opportunities), reducing manual effort and costs and accelerating deal timelines.

• GenAI can identify potential synergies in research capabilities (including human resources), drug development pipelines, manufacturing processes, and sales and marketing infrastructures, maximising the value created through the M&A transaction.

• GenAI can improve strategy development through generating data-driven insights across the company’s financial health, market positioning and growth trajectory, and providing support in developing successful deal strategies.