Generative AI in healthcare: Examples, benefits, use cases
Generative AI promises to transform the healthcare industry by streamlining tedious work and improving patient care.
The life sciences industry has shown us just how adaptable and innovative it can be. From going into hyperdrive during COVID-19 to re-architecting operations with AI, the industry has been nothing but agile.
Today, while the industry tackles challenges like increased regulatory scrutiny, it’s focused on operational excellence, and digital transformation, as reflected in life sciences 2025 trends.
The top trends include companies making strategic investments in areas like CDMO partnerships, end-to-end supply chain visibility, and generative AI applications while advancing personalized therapies and precision medicine.
Five years after the pandemic, nothing’s gotten easier in life sciences. Wars and political unrest around the world have all industries on edge and grappling with unpredictable markets.
All the uncertainty has sharpened industry leaders’ focus on operational efficiency, where they’re implementing new technologies like generative AI for help. They’re exploring ways gen AI can streamline workflows and drive value, from supply chain management to clinical trials and personalized therapies.
1. Collaboration with CDMOs in R&D: Pharmaceutical companies will continue to team up with contract development and manufacturing organizations (CDMOs) to bring therapies to market faster.
2. Supply chain resilience: Life sciences companies will focus on modernizing and standardizing R&D supply chain communications.
3. Regulatory compliance: As global markets expand and regulations grow, companies will be under intense pressure to ensure compliance without compromising quality or speed.
4. Personalized therapies: The shift towards personalized medicine will force the industry to rethink supply chain processes, adapt to new business models, and integrate new types of pricing.
5. Usage-based billing: Usage-based billing is emerging as a flexible and customer-centric approach that aligns costs with actual utilization.
6. Customer satisfaction + improved outcomes: As therapies become more personalized, patients and healthcare providers expect not just quality and reliability, but solutions that deliver tangible improvements in health and well-being.
7. Clinical trials + tech transfer in manufacturing: Life sciences companies will work to improve clinical trials as well as the transition from clinical trials to full-scale manufacturing—known as tech transfer—to bring therapies to market efficiently.
8. Governance of initial data in R&D: Successful life sciences innovation starts with strong data governance. From early-stage R&D to clinical trials and beyond, data governance ensures that insights are accurate, secure, and accessible.
9. Revenue management + customer service: Companies will focus on improving the way they manage revenue and customer service.
Generative AI promises to transform the healthcare industry by streamlining tedious work and improving patient care.
Contract development and manufacturing organizations (CDMOs) have become invaluable partners for pharmaceutical companies. By providing specialized expertise, infrastructure, and resources, they help bring new therapies to market faster.
During the COVID-19 pandemic, for example, researchers turned to CDMOs to streamline the production of vaccines, such as the “mosaic-8” nanoparticle vaccine. These life sciences partnerships helped make development and production of affordable vaccines possible.
With the growth of biopharmaceuticals—a sector now accounting for 20% of the pharmaceutical market and growing at 2x the rate of traditional drugs—this collaborative trend will grow. Developing biologics, which rely on living cells rather than synthetic compounds, requires advanced bioengineering capabilities and cutting-edge facilities. For many companies, doing this in-house isn’t feasible.
The benefits of working with CDMOs go beyond speed and cost savings. They also improve the reproducibility and reliability of drug development protocols, and often provide regulatory support.
In the life sciences industry, a fragile supply chain doesn’t just impact operations—it impedes patient care. Persistent problems like cost increases and transportation delays are forcing companies to find ways to bolster their supply chains.
Companies have made improvements to their operations, thanks to ERP systems and other tools. But those tools don’t always cooperate with the systems used by CDMOs and other third-party partners. Many have addressed this by creating custom APIs, or using spreadsheets and emails, but these ad-hoc solutions are inconsistent and can lead to information gaps and delays.
This is leading to a focus on modernizing and standardizing R&D supply chain communications.
Digital tools like end-to-end orchestration platforms and track-and-trace systems can help. These technologies give companies insight to make data-driven decisions at every stage of the supply chain, from production to delivery.
In a field where timing and precision are everything, strong supply chains are vital. Companies investing in these capabilities support operational excellence, but also protect their ability to deliver high-quality care to patients.
In a volatile business environment, companies must identify supply chain risks and act proactively to reduce the impact of disruption. Here are four key strategies.
As a highly regulated industry, compliance has always been a priority for life sciences companies. But as global markets expand and regulations grow, companies are under intense pressure in 2025 to ensure compliance without compromising quality or speed.
A law firm’s survey of life sciences and healthcare compliance and risk leaders found that only 31% feel well prepared to meet future compliance challenges and 42% feel compliance issues make it hard for them to maintain high quality of care.
Regulatory frameworks—like the Drug Supply Chain Security Act (DSCSA) in the U.S.—are pushing companies to implement advanced tracking and documentation systems. The DSCSA mandates that all trading partners in the U.S. drug supply chain—manufacturers, repackagers, distributors, and pharmacies— verify that their counterparts are licensed and registered. Drugs must be accompanied by product tracing documentation, which is stored for six years for accountability and transparency.
Compliance with rules like DSCSA is both a challenge and an opportunity to demonstrate commitment to safety and quality. For manufacturers, the challenge lies in integrating compliance into their operations without creating inefficiencies. Specialized platforms can help automate compliance checks, making sure that products meet quality and regulatory standards before entering the market.
With the increasing complexity of drugs and devices, ensuring consistent quality across production lines requires both advanced analytics and automation.
At its core, regulatory compliance isn’t just about following the rules—it’s about safeguarding patient health and trust.
The shift toward personalized medicine is major life sciences trend, projected to grow to a $167 billion market by 2031, up from $64 billion in 2022.
Personalized therapies, like cell and gene treatments, require a level of precision and coordination that traditional supply chains aren’t equipped to handle. Every step—from the collection of patient samples to the delivery of the final therapy—must be carefully tracked to ensure safety and efficacy.
Companies are adopting technology platforms specifically designed to manage this complexity, integrating patient and product data across the supply chain so that the right treatment reaches the right person at the right time.
Managing these therapies also means adapting to a new B2B2C (business-to-business-to-consumer) model, where life sciences companies interact not just with healthcare providers, but also directly with patients. This requires coordination between manufacturers, distributors, and treatment centers. Standardizing processes and leveraging centralized platforms can help companies scale these operations while maintaining a high level of care.
The growth of personalized therapies also is driving new types of pricing and reimbursement models. Outcome-based reimbursement, for example, is a way to address the high costs of these treatments while aligning incentives across stakeholders. These models shift the focus from volume to value, rewarding therapies that deliver measurable improvements in patient health.
Medtech combines innovation, commerce, and compassion by addressing patient and provider pain points with solutions that benefit both.
As life sciences companies face growing demand for efficiency and transparency, many are rethinking their operational and financial models. Usage-based billing is emerging as a flexible and customer-centric approach that aligns costs with actual utilization.
Usage-based billing models enable companies to offer customized pricing structures, particularly in sectors like medical devices or specialized therapies where resource utilization varies a lot.
By shifting from a flat-rate approach to one that adjusts based on usage, companies can provide more equitable access and align incentives with customer needs.
Achieving this level of flexibility depends on digital systems that integrate billing, supply chain, and operational data. Cloud-based platforms and scalable analytics tools allow companies to track real-time usage and adjust pricing dynamically. For example, advanced analytics can help predict demand patterns for better resource allocation and less waste.
Getting better visibility into planning and operations factors into this by helping companies spot bottlenecks, track inventory, and quickly respond to changes in demand.
In the life sciences industry, customer satisfaction is directly tied to outcomes. As therapies become more personalized, patients and healthcare providers expect not just quality and reliability, but solutions that deliver tangible improvements in health and well-being.
The emphasis on personalized therapies reflects a broader trend toward meeting these heightened expectations. These treatments, tailored to individual patient needs, inherently prioritize outcomes, aligning the interests of life sciences companies, healthcare providers, and patients.
But delivering this level of personalization demands a sharp focus on product quality, supply chain stability, and innovation. Advanced quality assurance systems help companies maintain rigorous standards and improve efficiency. This is particularly important in an industry where even minor lapses can have serious consequences for patient health.
Of course, customer satisfaction depends on more than the therapy itself. It’s impacted by the holistic experience—from timely delivery to providing transparency and accountability. By adopting tools that improve visibility and communication, companies can better deliver the outcomes and experiences patients expect.
Clinical trials are the proving ground for life-changing therapies, but far from easy to manage. And the transition from clinical trials to full-scale manufacturing—known as tech transfer—requires flawless coordination to bring therapies to market efficiently.
One of the biggest challenges in clinical trials is aligning supply chain planning with the unpredictable nature of trial needs. Intelligent tools are designed to boost visibility, helping companies synchronize inventory, manage demand spikes, and reduce waste.
Tech transfer presents its own set of challenges, especially when scaling production from lab environments to full-scale manufacturing. This involves translating small-scale processes into reproducible, high-volume outputs while maintaining quality and compliance.
Collaboration is key here. Stakeholders across R&D, manufacturing, and supply chain teams must work together for smooth handoffs.
Co-innovation also plays a role in tech transfer. By fostering collaboration among industry stakeholders, companies can streamline quality assurance processes, standardize protocols, and improve overall efficiency. For instance, automated QA systems can reduce the time required for review and approval, helping therapies move from trial to market faster.
Successful life sciences innovation starts with strong data governance. From early-stage R&D to clinical trials and beyond, data governance ensures that insights are accurate, secure, and accessible.
In R&D, the volume and complexity of data generated are staggering. Governance begins with standardizing data exchanges between sponsors and contract manufacturers. This streamlines the sharing of regulatory submissions, production forecasts, and quality assurance data, helping teams operate more efficiently.
Platforms that set standardized processes for sharing R&D data reduce delays and ensure that all stakeholders work from a single source of truth.
Managing contingent labor and associated expenses adds another layer of complexity. Many life sciences companies rely on temporary staffing for specialized roles in R&D and manufacturing. Effective management of these resources requires visibility into spending and alignment with project needs.
By combining a strong data governance program with disciplined expense management, companies can maximize their R&D investments to speed innovation.
Revenue management is more than a financial strategy—it’s a key driver of customer satisfaction.
Technology is transforming how companies manage revenue and customer service. Platforms that integrate supply chain and financial data allow for dynamic pricing models (like usage-based billing). This trend improves access to therapies, but also supports revenue growth by tailoring pricing to market needs.
Customer service hinges on delivering high-quality products on-time and with full transparency. Once again, this is supported by real-time supply chain visibility, which helps companies anticipate delays, fix bottlenecks, and provide a seamless customer experience.
Revenue optimization extends to easing regulatory compliance and operational workflows. By reducing inefficiencies in processes like batch release or quality assurance, companies can bring products to market faster while maintaining high standards of safety and quality.
The combination of smarter pricing models, operational excellence, and strong customer service boosts customer trust and loyalty for a better bottom line.
The industry’s top trends share one overarching theme: resilience and adaptability. Whether it’s using generative AI to streamline processes, increasing supply chain visibility, or driving innovation in personalized therapies, the future of the industry lies in its ability to balance cutting-edge technology with a deep commitment to patient care.
The stakes are high—but so are the opportunities. Those that prioritize collaboration, embrace digital transformation, and focus on delivering meaningful outcomes will not only meet today’s challenges, but also set the standard for the healthcare of tomorrow.