One of the most striking, and somewhat paradoxical, findings was that, while pharmaceutical companies are highly data-driven compared to other industries, they still face significant struggles in realising value from that data. More than 7 in 10 respondents say that production or maintenance is data-driven, and a remarkable 78% report that asset management is integrated with real-time monitoring systems.

Yet despite this digital maturity on paper, the results tell another story. Two in three respondents say that data silos hinder their organisation’s full potential. Half consider their digital transformation still in its early stages. Another half cite “collaborating and sharing data within the organisation” as one of their most pressing challenges.

If I were to interpret these findings, I’d say that pharmaceutical companies—like many others in sectors such as oil and gas or chemicals—are grappling with a familiar issue: the gap between digitisation and value from digitisation.

In my experience, the more digitally mature an organisation becomes, the harder it is for new initiatives to close that gap.

Companies starting from a lower standard of digital maturity often find it easier to identify use cases with immediate, high-impact results, as there is simply more low-hanging fruit. Replacing pen-and-paper or reducing non-value-added activity time such as administration, are examples of how value can be unlocked rapidly across the operation and management of assets.

But as organisations climb the operational maturity curve, high-value use cases become harder to identify, and it’s easier to fall into the trap of no-value digitisation.

No-value digitisation can take several forms, including these three common ones:

• Tick-the-box thinking: An initiative that delivered early success is rolled out indiscriminately to lesser-value use cases. Teams prioritise rapid deployment over actual value realisation or user feedback.
• Technology-first thinking: A shiny new tool is selected first, and teams scramble afterwards to justify its value, often without an appreciation of what success even looks like
• Organisational myopia: The organisation struggles to identify valuable opportunities. One example is maturity myopia, where teams pursue advanced capabilities or metrics that are either disconnected from business value or that simply do not have the foundational building blocks (Stakeholder engagement, optimised processes, access to valuable data) in place that would allow them to realise tangible value.

So how do businesses avoid these pitfalls and escape the spiral of no-value digitisation?

1. Start with Value

Adopt practices that help to identify value from the outset. Within Hexagon we recommend and support our clients with a value-driven approach organised into three essential pillars:

• Value Discovery: work together to Identify a transparent, defensible business case that aligns with organisational objectives.
• Value Delivery: Match the business case with the adoption of the right technology solution.
• Value Realisation: Measure and track the actual value created by the technology adopted and measure those results against the expectations created during the value discovery phase.

This approach helps to rapidly identify goals, expected benefits, and the gaps that need to be closed to achieve them.

2. Build Feedback Loops

Top-down, all-or-nothing digital transformation efforts are among the most likely to fail. Executive sponsorship and strategic direction are crucial but they shouldn’t result in digitisation by decree or backseat driving from the top.

In industrial settings especially, input and feedback from the field are critical for successful adoption. A phased approach not only promotes real-world alignment but also enables you to measure value and adjust along the way.

3.  Evolve digitisation and mature at a pace that suits your organisation

Finally, invest in tools that are interoperable and built to evolve with your organisation. This is a core principle at Hexagon as we preach a ‘crawl, walk, run approach’ to meeting digitisation goals. Our products and solutions are designed with flexibility and scalability in mind, recognising that clients operate in complex environments with multiple vendors, systems and differing levels of maturity.

Value often emerges in unexpected ways. If you’re locked into a standalone solution, one that requires manual data uploads, doesn’t integrate with other systems, and only works within a closed ecosystem, you might have today’s fix, but you’re also creating tomorrow’s problems.

Article by Edgardo Moreno, CISSP, GICSP, an Executive Industry Consultant at Hexagon’s Asset Lifecycle Intelligence division.

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New ESMP platform accelerates move to single view of product information

Improving patient access to life-enhancing treatments is a central mission of companies ranging from the world’s largest biopharmas to early-stage biotechs. Each pursues its own path to get there. For some, the focus is responsibly-priced medicines. Others are amplifying patient voices during the medicine life cycle or using ESG bonds to reach underserved communities.

Patient access to life-enhancing treatments is a struggle without being able to ensure the timely delivery of medicines. Between 2000 and 2018, Europe experienced a 20-fold increase in drug shortages. On average, each pharmacy in the European Union spends more than six hours a week dealing with scarce supplies of medicines; in some countries, they spend as much as 20 hours per week.

The European Medicines Agency (EMA) has responded to the lack of a standardized EU-wide registry by launching the European Shortages Monitoring Platform (ESMP). In reality, the failure to meet patient demand for specific drugs is not limited to Europe. Healthcare systems worldwide are straining under rising costs. In the U.K., intermittent supplies are causing a medicine shortage crisis that risks harming patient outcomes as physicians ration medicines in short supply or switch to less effective alternatives.

Biopharmas have limited influence over many of the contributing factors obstructing patient access like transportation issues, physician availability, or healthcare funding. Although these may be out of companies’ scope of impact, what they can improve is how quickly they communicate and make decisions when managing supplies. Robust product definitions used consistently across functions, greater control of their data and documents, and an organization-wide understanding of regulatory approval status in each market would all help. Companies that centralize their product information will not only ensure regulators receive timely indicators of imminent shortages but also improve their own capacity for internal and external collaboration.

Earlier warning signs on critical shortages

Drug shortages have many complex and interdependent causes, ranging from biopharma sector consolidation and a limited number of suppliers to government pricing strategies and patent laws for innovative medicines. Such complexity makes it difficult for regulatory authorities to anticipate shortages, with few warning signs when stocks of critical medicines run low.

Having launched the ESMP in January 2025, EMA should soon be able to monitor drug supply, demand, and availability continuously. Marketing authorization holders (MAHs) are playing their part by providing product supply forecasts, availability, manufacturing details, and production plans to both national competent authorities (NCAs) and the ESMP[1].

To ensure that the product data currently held by EMA is accurate, biopharma sponsors will have to correct and enrich their authorized product datasets, either through direct data entry into the Product Management System UI (PMS) or by completing data loading templates with relevant information. As product data still sits across different functions (including clinical, regulatory, and pharmacovigilance), sponsors are getting ready to share information with ESMP by locating relevant data sources within their organizations — and, sometimes uncovering too late that their product information is inconsistent.

Sponsors need full transparency and control of their data to provide accurate sales and supply forecasts for critical medicines to ESMP. Data used to be copied and pasted as text when shared between departments; it should now be captured once at the source and then stored securely in the right format and place. In many instances, this means taking data out of documents and converting it into structured formats. As sponsors increasingly rely on outsourcing partners such as contract research organizations (CROs) and contract development and manufacturing organizations (CDMOs), they need to connect seamlessly when exchanging information externally.

Single source of product information for regulators

In the past, major stockouts in key markets were more common than they should have been, partly because some biopharmas did not know which regulatory information management (RIM) systems held the correct dates for approval and supply. Some companies tried to compensate for the limited connectivity between regulatory, packaging, and logistics by preparing product supplies before receiving the regulatory go-ahead, which could mean an extra step of reworking labels and product packaging if only partial approvals were eventually obtained.

Recent regulatory developments are making a single source of product information a priority for sponsors. Modern RIM platforms can centralize registration data: including marketing status (and dates), product information, active substances, pack sizes, packaging details, and packaging medicinal product identifiers (PCID). Once companies license a product in a market (done by pack), its registration data will be recorded in RIM and become easier to share with ESMP. This can mitigate potential shortages. For instance, if a manufacturer has issues with a product, the regulator can see alternatives containing the same active pharmaceutical ingredient (API).

Post-approval manufacturing changes also lead to drug scarcity. Typically, a large biopharma may manage as many as 200 post-approval changes per product a year — or thousands across its global portfolio. Processing and preparing each change submission can take six months to two years for a company to complete because key systems (QMS, RIM, LIMS, ERP) and data are disconnected. Bringing together the systems underpinning quality and RIM would make it easier to identify which countries and internal documents are affected across multiple markets during a post-approval manufacturing change.

During a drug shortage event, manufacturing sites would not lose time trying to find which specification is registered in each market for each product. Because regulatory and quality teams would see the same product data and documents, quality change controls automatically trigger when a regulatory event occurs affecting multiple markets. Market authorizations in each country would be tracked in real time, ensuring quality teams learn of Health Authority (HA) approvals as soon as they are received.

When different functions and authorities can efficiently exchange the latest data, they can make confident decisions for faster delivery of medicines to patients. As Juhi Saxena, associate director of regulatory and clinical platforms at Moderna Therapeutics, explains: “After connecting quality and regulatory, the data and information required for change control doesn’t have to be requested or sit in someone’s inbox for two days. This has significantly reduced the time required to perform regional impact assessments and send that information on to supply chain and quality departments.”

Centralizing access to data and documents would also improve external collaboration between sponsors, CROs, and CDMOs. Given accountability lies with sponsors, some are consolidating their system landscape and prioritizing partners that can provide immediate access to live data. Contract partners are also doing their part by eliminating manual activities and non-secure external communication (such as email and shared drives) for greater traceability. For example, CDMO Forge Biologics moved toward a connected quality management platform for better compliance and faster turnaround on reviews and approvals with its clients.

Finally, sponsors with a good handle on data quality, ownership, and governance will drive business benefits beyond ESMP. At one global enterprise that initiated regulatory change through its master data management initiative almost a decade ago, the result is that the organization “now speaks one language.” Data integration means quality, regulatory, and safety will all work from the same set of product definitions across the value chain. Having standardized product definitions sets the stage for accelerated batch release decisions by making them traceable to quality and regulatory data.

One shared record, systemic benefits

EMA’s enhanced monitoring of drug availability through the ESMP has rightly shifted the focus to accurate and consistent product data. Getting this data in order sets the foundation for the strategic use of predictive analytics. Sponsors, their partners, and regulators will be capable of predicting shortages and mitigating their impact proactively.

For this to work, greater automation when interacting with regulatory bodies will be essential, both for ESMP and CTIS (the platform underlying EU CTR). That’s because automation supports data integrity by minimizing the chance of human error during data entry or other manual activities.

Seemingly intractable problems can be overcome by breaking them down into their constituent parts. By focusing on what they can control, biopharma companies and regulatory authorities will do their part in helping the industry meet its patient access goals and ensure timely delivery of medicines to those waiting for them.

By Stephan Ohnmacht, Vice President, R&D Business Consulting, Veeva

References

[1] ISPE, ‘European Shortages Monitoring Platform (ESMP): Essentials and Industry Reporting Requirements Webinar’, June 2024

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Yet, without proper oversight, the risks are substantial. For instance, the EU’s AI Act imposes severe penalties on life sciences companies deploying non-compliant systems. In this context, data is no longer merely a resource but a core strategic asset requiring active protection and management.

The growing data opportunity

The scale of data in life sciences is staggering. Pharmaceutical firms partner with thousands of study sites and tens of thousands of trial participants. A study by Tufts University found that Phase III clinical trials now generate an average of 3.6 million data points, tripling the volume collected a decade earlier. Amid this deluge of information, ensuring timely access to the right data is crucial to optimise R&D efforts, while reducing the time spent on data preparation and management.

Fragmentation compounds the challenge. A 2024 survey by Informatica shows that 41% of organisations have 1,000 or more data sources and nearly 60% use an average of five tools to manage them. As data increases in volume and becomes more fragmented, the need for a single consolidated solution to manage it becomes even clearer.

By consolidating data, companies can unlock numerous benefits. Sales teams can better understand trends and factors influencing their target accounts, procurement can leverage improved data visibility to collaborate more effectively with vendors and partners, and corporate M&A teams can achieve faster time to value by integrating an acquired company’s data, systems and applications more efficiently. As a result, drug discovery becomes more efficient, outcomes become more predictable and the insights from analytics become more reliable. At its core, success depends on robust data management practices that prioritise quality, accessibility, governance and protection.

Overcoming regulatory challenges

Life sciences is a highly regulated industry, requiring compliance with international standards, such as the CIOMS International Ethical Guidelines, ICH E6 Guideline for Good Clinical Practice and PhRMA Principles for Clinical Trials and Communication of Results. The introduction of the European Union’s AI Act, which came into force on 1st August 2024, adds another layer of complexity. Managing requirements across overlapping regimes makes establishing a single source of truth for patient data not just best practice but a business imperative. Effective data management is especially crucial to meeting the AI Act’s stringent governance and traceability requirements.

Consolidating data management under one platform allows life sciences firms to define clear responsibilities, policies and processes while streamlining workflows. A unified approach to metadata management ensures a consistent application of data protection and privacy measures, enabling companies to automate compliance and navigate the complexities of evolving regulations with confidence.

Preparing for AI adoption

Research from Cognizant and Oxford Economics forecasts that enterprise AI projects will leap from today’s experimentation to a period of confident adoption by 2026. The life sciences industry must prepare for this. Proper stewardship of patient and intellectual property data is a foundational element of any digital healthcare initiative, particularly when using AI, where transparency and reliability of outputs remain ongoing concerns.

AI can also play a transformative role in data management by automating complex tasks such as data extraction, classification and validation. This improves accuracy and accelerates compliance. In this sense, AI and data are interdependent—AI needs high-quality data, and modern data management increasingly relies on AI-driven efficiencies.

Scalable software with integrated AI engines can take on the heavy lifting of data ingestion, integration, governance and quality management. Advanced tools also allow non-technical users to access data products tailored for specific use cases like drug discovery, fostering greater collaboration and innovation.

For example, we worked with a global pharmaceutical brand who needed to advance its multi-year strategy to migrate its on-premises solutions to the cloud. Moving to the next phase required a single, trusted source of data for analytics that could be leveraged across the enterprise. The firm implemented an AI-powered SaaS data management and governance solution to consolidate data management and shift their legacy supply chain analytics to the cloud. As a result, development processes were simplified, delivering notable reductions in cost and time. Five million records could be loaded in 4 hours, vs. 19 hours required by the previous system. Since deployment, the new cloud-based data management solution has helped cut the time to manage new orders by 50%.

Establishing trust and taking control

Life sciences is no stranger to innovation, but today its eureka moments depend on data — data that is high-quality, compliant, secure and governed. Data management in a highly regulated global industry remains complex and costly. Companies must break down silos, integrate external data and comply with evolving regulatory landscapes. They also need to handle growing complexity in data types, formats and storage, while ensuring integrity and security. This is essential to foster trust and an environment of innovation.

Achieving this requires modernising data management to empower R&D teams to unlock new possibilities. From streamlining drug discovery processes to precisely targeting discreet patient populations, robust data systems can drive impactful change. As advanced analytics and generative AI drive a new wave of innovation, it’s essential that data management systems rise to meet the industry’s rising demands for speed, quality, compliance and innovation.

About the author

Rohit Dayama is a Global Client Partner within the Life Sciences practice at international professional services company Cognizant. With over 20 years of experience in consultancy services, he has specialised in the design and execution of complex digital transformation initiatives, leveraging AI and tech innovation to help leading pharma companies bridge the gap between technology and sciences, grow their businesses, and improve patient outcomes.

At Cognizant since 2017, Rohit has been serving multi-national clients as a strategic partner in establishing world-class digital capabilities, managing a variety of business transformations and achieving strong account growth.

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Up to now, local (country- or region-specific) literature monitoring has been seen as a necessary but inefficient part of pharmacovigilance (PV). The practice is expected if not mandated by many health authorities, being the only sure way to establish that adverse drug reactions and safety signals published in such journals, web sites and/or print sources, are identified and reported. If gaps in routine monitoring are discovered during inspections or audits, this could have implications for ongoing licensing and sales, not to mention market confidence.

At a strategic level, local safety insights allow pharmaceutical companies (as well as healthcare systems) to respond proactively to emerging concerns in particular populations. They might inform labelling or guidance at a country level as appropriate, and help guide ongoing product development.

Critical, but onerous

Despite its criticality, local literature monitoring is tremendously inefficient in its current form. It usually requires dedicated staff at an affiliate or regional level, potentially with local language capabilities and local journal access. Despite the considerable volumes of content being reviewed, local monitoring may yield only limited safety information; errors/omissions are commonplace too.

Because of its resource intensity, local literature monitoring is frequently outsourced to clinical research organisations. The process typically involves monitoring a diverse range of designated sources, listed in multiple, unwieldy Excel spreadsheets where findings are also logged. Assigned teams are expected to monitor several thousand different web sites on a weekly or monthly basis.

The challenge is compounded by substantial variances in the literature format, literature access issues and language barriers. Local sources tend to lack consistency in format, indexing and language, making it difficult to implement a simple unified process, while many local journals require paid subscriptions or may be only available in print.

Additionally, regulatory reporting timelines tend to vary by country, something else that has had to be tracked manually to ensure respective adherence.

All of these challenges above present a regulatory risk, as well as a risk to patient safety. This is due to the potential to miss safety events.

It is a situation that will only intensify, too. With the growing focus on specialty drugs including more personalised and targeted treatments in oncology and for rare disease, including new therapeutics such as CAR T-cell therapies, strong drug safety/PV oversight is essential.

It is for all of these reasons that the pharma industry and its service provider community are looking to next generations of automation technology for an answer.

Advanced automation offers a big part of the solution

So what is prompting process innovation? New technology enablement, involving large language models (LLMs), is proving instrumental firstly in structuring data – enabling “normalisation”, unification, centralised management, and governance of materials, as pharma transitions to “data-first” ways of working.

In parallel, advanced “crawling” techniques are transforming automated browsing, “scraping”, and indexing of content from target web sites and publications. AI can then add a further layer, making it possible to search all of that content very quickly and identify all relevant safety events on demand.

Of course, where patient safety is concerned, there will always be an important role for human oversight and process governance. (Technology-assisted human ingestion is another option, where companies are more hesitant about immediate technology reliance.)

Persisting without automation is hard to justify now, though. Most pharma companies have come to accept that, unless they buy into technology-enabled process innovation, they will struggle with continued operational viability in this challenging world economy. With easy, rapid access to the data they need, on the other hand, PV scientists could focus on the higher-value activities that form the core of their role, even as medical literature volumes explode.

The bigger picture beyond PV

As companies transition away from laborious manual processes in their literature monitoring, the strategic potential lies in the new, richer, data-driven insights they will gain about safety trends.

More broadly, this is an opportunity for companies to better understand the safety trends around their drugs – and at a more discrete level. As well as informing ongoing drug discovery and development, early population-specific insights could also inform the respective healthcare system and patient journey in a given region, with wider societal benefits.

A Biologit white paper further exploring this topic, can be downloaded here

About the author

Jean Redmond, PhD, is COO at Biologit. She is a scientist with 10+ years’ experience in consulting, strategy and general management gained by working with multiple life science offerings and teams at a global clinical research organisation (CRO). Biologit is a specialist in advanced, technology-enabled safety surveillance solutions for life sciences.

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AI use cases are rippling across commercial biopharma, helping companies make faster, more informed decisions. Yet almost 70% of top generative AI (GenAI) users cite poor data quality as their most significant obstacle in unlocking AI’s full potential. As the adoption of applications grows, the true competitive edge lies in the quality of the data fuelling them.

To fully harness AI, commercial leaders are establishing a scalable, seamlessly connected data foundation across markets, functions, and disease areas. Without it, companies’ AI pilots could amount to isolated experiments. Those who focus on creating standardized and well-integrated data can unlock AI’s full potential to gain a competitive advantage and drive long-term success.

Data consistency and connectivity: the foundation of AI

Commercial biopharma teams are uniquely positioned to strategically leverage AI as they collect vast amounts of data, including customer, sales, medical engagement, and social media activity. The next step is to harmonize the data — essentially to “speak the same language” to generate accurate and scalable insights,

Consider a common scenario: One system lists a healthcare professional (HCP) as “John Smith” and another as “J. Smith.” Or perhaps “cardiology” is recorded in one database while “heart medicine” appears in another. AI may fail to connect the variations, leading to errors, duplication, and unreliable insights. These inconsistencies often stem from diverse data sources that don’t speak to each other, creating friction for AI and significantly reducing its ability to provide value.

In another example, a biopharma’s HCP database had over 25,000 specialty classifications, rendering AI-driven insights nearly impossible. The company resolved the issue by implementing global data standards, significantly improving accuracy and scalability.

While AI continues to improve in handling inconsistencies, its success still hinges on the quality of the data it’s trained on. This is especially critical in commercial biopharma, where data is often fragmented, sparse, and inconsistent, disrupting AI’s ability to generate meaningful insights.

Bayer AG’s journey to AI-ready and globally standardized data

Overcoming data consistency challenges requires an organization-wide approach. Some biopharma leaders are already making strides by prioritizing global data standardization to connect data and run advanced analytics initiatives.

For example, Bayer AG sought to create a 360-degree customer view to provide its field teams with comprehensive insights before engaging with HCPs. However, data silos across geographies made it challenging to achieve a unified view.

Stefan Schmidt, group product manager at Bayer AG, led the company’s data harmonization efforts. Schmidt understood that AI insights would remain unreliable without a centralized, accurate data foundation. “Our global data landscape was fragmented — different countries relied on different sources. To see the full picture, we needed a unified customer master,” Schmidt explains.

By harmonizing data across geographies and functions, Bayer eliminated inconsistencies and improved accessibility. The company consolidated key data sources — CRM, engagement history, and customer profiles — into a single, intuitive platform for its sales teams.

“In just weeks, we developed a solution that our teams genuinely valued,” Schmidt shares. With a single, connected source of truth, Bayer AG is now positioned for scalable, AI-driven insights across the organization.

How commercial leaders are scaling AI

Bayer AG’s experience demonstrates the power of a globally standardized data foundation and the importance of making it a strategic priority for scaling the impact of AI.

To avoid the common pitfalls commercial leaders must address three key data challenges:

1. Business: Moving AI pilots from isolation to execution

A clear AI strategy, aligned with business priorities, is the strongest predictor of success. Many organizations run local pilots without considering scalability, repeatedly building country-specific solutions based only on country data. This approach prevents data from being connected across countries and limits AI’s ability to generate cross-country insights.

To effectively scale AI efforts, commercial leaders should:

• Align AI priorities with long-term business goals to ensure they address high-impact opportunities rather than short-term experimentation.
• Collaborate across functions — data, analytics, digital, and IT — to build a scalable AI roadmap with defined resources, timelines, and investments.
• Establish governance structures that support AI adoption at an enterprise level, ensuring consistency and alignment across regions, when scaling AI.

2. Data and analytics: Establishing global data standards

Once a strategic direction is set, data and analytics teams can ensure access to high-quality, globally standardized, connected data. Piecing together country-specific data will make deploying initiatives across different markets challenging.

To overcome fragmentation, organizations should:

• Standardize data structures globally, ensuring that AI models trained in one region can be applied seamlessly worldwide.
• Invest in connectable data assets that integrate customer, sales, and engagement data across the organization.
• Continuously refine data quality, ensuring AI models are built on accurate, harmonized data that supports enterprise-wide decision-making.

3. Digital and IT: Reducing integration complexity

Technology teams play a pivotal role in making AI scalable by reducing data friction, eliminating costly integrations, and breaking down data silos.

To support AI efforts, technology teams should:

• Align data models across systems to prevent inefficient data mapping and redundant integrations.
• Evaluate process inefficiencies such as third-party access (TPA) agreements that slow down data flow and require unnecessary administrative work.
• Implement scalable data governance frameworks that streamline AI deployment across multiple markets.

Your data defines AI’s possibilities

AI adoption in commercial biopharma is accelerating, increasing the need for high-quality, connected data for more personalized engagement.

Approaching data standardization with the same urgency as defining AI strategy and infrastructure is critical. After all, the real question isn’t, “How can I use AI?” but “How can I make my data work for AI?”

By Karl Goossens, Director, OpenData Strategy, Veeva Europe

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Over the past few years, Generative AI (GenAI) has emerged as a powerful catalyst for accelerating this healthcare revolution. These advanced systems can predict drug responses and side effects, generate synthetic data to fill critical gaps, boosts efficacy, and speed up discovery – ultimately making personalised treatments faster and more accessible to patients.

For instance, two patients with the same cancer diagnosis may receive different treatment plans based on the mutations in their specific tumours or how their body is going to likely respond to the different drugs based on their genetic profile, maximising effectiveness while minimising adverse affects.

Tailoring treatment responses with the power of generative AI

GenAI is enabling better predictions as to how individual patients are likely to respond to specific treatments as well as identifying potentially adverse events before they happen. By training on biomedical data like genomic, transcriptomic, and clinical records, these models can assess a drug’s likely efficacy and toxicity based on a patient’s unique profile. For example, a 2023 study by Wang and colleagues leveraged Multi-Omics Integrated Collective Variational Autoencoders (MOICVAE), an AI model capable of accurately predicting drug sensitivity for 25 drugs across seven kinds of cancer. In a separate study, Shi and colleagues proposed CSAM-GAN, a generative adversarial network based on sequential channel-spatial attention modules able to predict patient prognosis in lower-grade glioma and kidney renal clear cell carcinoma.

Models like these are helping clinicians match the right therapies to the right patients, while minimising risks through earlier, data-driven insight.

GenAI steps in: a new frontier in single-cell research

The rapid growth of single-cell sequencing data is creating new opportunities. So-called single-cell foundation models based on generative pre-trained transformers are able to distill biological information about genes and cells and be fine-tuned for biomedical tasks, like cell identification, even when data is incomplete. Their broad cellular knowledge makes them highly generalisable across a range of biomedical applications. Relatedly, GenAI can also be used to create synthetic biomedical training data that closely mimics the statistical variation found in real world patient data. In the case of rare diseases with few patients, for example, researchers can train models on thousands of AI-generated examples that match the underlying condition – creating more powerful and accurate models. Additionally, these datasets can be stripped of personally identifying information and are thus suitable for publication without fear of violating patient confidentiality.

Advancing the future of clinical development

GenAI offers new ways to identify patient subgroups and the relevant biomarkers, crucial for clinical development and maximising drug efficacy. In cancer and other complex diseases, different patients frequently respond differently to the same therapy. Uncovering which biomarkers (or combination of biomarkers) differentiate treatment responders from non-responders is a key part of precision models.

GenAI excels at finding patterns across complex datasets that are hard to analyse using more traditional statistical methods. CSAM-GAN, for instance, is able to integrate a patient’s DNA profile, RNA profile, and histopathology images to predict outcomes — and crucially, pinpoint which biomarkers drive those predictions, whether a gene, pathway, or tissue feature.

Looking ahead, GenAI could even enable the creation of “digital twins” for each patient. Every available treatment plan could then be simulated with the digital twin to identify how each one is likely to play out. While still an emerging concept, this could one day transform personalised medical care.

Cutting the drug discovery timeline

Perhaps most strikingly, GenAI is accelerating the traditionally slow and costly process of new drug development. Bringing a new drug to market can take well over a decade and costs billions. GenAI has the potential to dramatically cut both timelines and expenses.

GenAI is streamlining and automating stages that traditionally would take researchers months or years. Instead of physically synthesising and testing thousands of possible drugs, GenAI models can generate novel drugs that have a high likelihood of meeting a specific criterion. For example, these models can identify compounds that have a high chance of binding to a particular receptor (say, serotonin receptors) while having a low chance of toxicity.  This narrows down the list to the most promising candidates, reducing early-stage development from years to mere weeks or months.

Helping precision medicine become faster and more accurate

Generative AI is rapidly establishing itself as a cornerstone of precision medicine, fundamentally transforming healthcare from standardised protocols to truly personalised approaches.

More than just a technological advancement, GenAI represents the future for making medicine more individualised, predictive, and efficient for doctors and patients. What we are seeing today is merely the beginning of a profound transformation – Already, generative AI is already helping to identify patient-specific drug responses individually, uncover novel biomarkers, generate synthetic datasets and accelerate drug discovery.

These AI models will continue to grow and adapt to clinical needs, and will become even more capable and impactful across the healthcare ecosystem. The future of medicine sees a doctor and an AI colleague working together running virtual trials based on a patient’s biomedical data, predicting drug responses to personalise available therapy.

The result is transformative: the right treatment, for the right patient, at the right time – through the deliberate application of advanced AI working in harmony with human medical expertise. This convergence promises to reduce adverse effects, improve outcomes, and ultimately deliver on the long-sought promise of precision medicine and truly personalised healthcare.

By Dr. Ilya Burkov, Global Head of Healthcare and LifeSciences Growth at Nebius

The post The AI-Powered Revolution in Precision Medicine appeared first on .

Despite these advancements, the sector still faces challenges in fully realising the potential of digital maturity compared to other industries.

Looking ahead to 2026, several key trends will shape the future of biotech, driven by the integration of digital technologies and advanced analytics.

1. Artificial Intelligence (AI) in Drug Discovery

AI is accelerating the discovery of novel therapeutics by streamlining the identification of promising drug candidates.

Machine learning algorithms analyse vast biological datasets to identify viable molecules, significantly reducing R&D costs and timelines. AI-powered platforms enhance target identification, lead optimisation, and preclinical testing, improving efficiency in biotech research.

2. Cloud Computing for Biotech Innovation in 2026

Cloud and edge computing are revolutionising the scalability and innovation potential of biotech firms.

With enhanced data sharing, real-time collaboration, and seamless AI integration, cloud computing enables faster drug development cycles and robust data security. Companies leveraging cloud-based platforms will gain a competitive advantage in operational efficiency and scientific breakthroughs.

3. Machine Learning (ML) for Drug Development

Industrialised machine learning is transforming every stage of drug development. From predictive modelling in clinical trials to optimising biologics formulations, ML enhances data-driven decision-making. Advanced algorithms refine predictions, minimise trial failures, and accelerate regulatory approval processes for new therapies.

4. Real-Time Analytics in Clinical Trials

The demand for more efficient and effective clinical trials has led to greater adoption of real-time data analytics. AI-powered data processing enables biotech companies to monitor patient responses, detect anomalies early, and optimise trial designs. This trend is particularly critical in rare disease research, where patient recruitment and retention remain key challenges.

5. Investment in Digital Health Technologies

Venture capital is flowing into digital health solutions, particularly those that enhance patient engagement, remote monitoring, and commercialisation strategies. Biotech firms are increasingly partnering with health tech start-ups to develop wearable devices, mobile applications, and AI-powered telemedicine solutions that improve patient outcomes and treatment adherence.

6. Data-Driven Decision Making

Biotechnology companies are leveraging big data to optimise research, clinical development, and commercial operations. Advanced analytics provide deep insights into patient behaviour, biomarker discovery, and market dynamics, enabling more precise business and scientific strategies. Organisations that successfully utilise data-driven decision-making will drive innovation and maintain industry leadership.

7. Synthetic Biology and Precision Medicine

Synthetic biology is rapidly emerging as a disruptive field for engineering novel biological systems. By designing customised treatments for genetic disorders, regenerative medicine, and vaccine development, synthetic biology offers unprecedented potential for addressing unmet medical needs with precision and efficiency.

8. Decentralised and Virtual Clinical Trials

The shift towards virtual and decentralised clinical trials is improving patient accessibility, recruitment, and trial efficiency. AI-driven analytics, remote monitoring tools, and telemedicine solutions allow biotech companies to conduct trials with greater flexibility while ensuring data integrity and regulatory compliance. This trend is redefining the clinical trial landscape, making drug testing more patient-centric.

9. Quantum Computing in Drug Discovery

Quantum computing is poised to become a game-changer for biotech. By simulating molecular interactions at an unprecedented scale, quantum computers could dramatically accelerate drug discovery. While still in its early stages, this technology holds immense promise for solving complex chemical and biological challenges beyond the capabilities of traditional computing.

10. AI-Powered Diagnostics and Personalised Medicine

AI is transforming diagnostics by enabling early disease detection and precision medicine. AI-driven imaging, pathology analysis, and predictive algorithms are revolutionising how diseases are diagnosed and treated. As healthcare shifts towards personalised medicine, AI-powered diagnostics will play a crucial role in advancing targeted therapies and improving patient outcomes.

11. AI-Driven Scientific Research Assistants

AI-powered research assistants are becoming indispensable tools in biotech and life sciences. These digital assistants automate data analysis, literature reviews, and experiment documentation, significantly enhancing productivity. By integrating with cloud computing and real-time analytics, AI-driven assistants foster collaboration, accelerate discoveries, and reduce the workload for human researchers.

Biotech 2026

As we move towards 2026, the integration of digital and AI-driven solutions in biotech is not just a trend—it is a necessity. Companies that invest in these innovations will lead the charge in scientific and medical advancements, driving faster drug development, improving patient care, and optimising research operations. The future of biotechnology is digital, and those who embrace this transformation will be at the forefront of innovation and discovery.

By Kevin Cramer, CEO, Sapio Sciences

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As witnessed during the pandemic, packaging providers play a crucial role in the delivery of lifesaving medicines. By building relationships with these providers now, logistics can adapt to ensure continued effectiveness of cold chain distribution as we prepare for the increase of extreme weather.

Cold chain needs to be smarter, not just stronger

Supply chain disruptions can easily cause issues with the delivery of supplies and treatments. Global warming will create unpredictable conditions, with flooding, landslides and storm damage. These extreme weather fluctuations will impact routes and mean that future packaging may need to handle freezing temperatures, extreme heat and humidity all in one journey.

Ensuring reliability and efficiency is vital. Availability of packaging solutions must be successfully managed, and the industry must position themselves to be able to predict and prepare for all disruptions. The extreme weather fluctuations will mean a one-size-fits-all approach will no longer be viable. Instead, data-driven risk analysis and route-specific adaptation will be key. Manufacturers will need to factor in seasonal and regional climate risks when planning distribution.

One solution to these evolving challenges is integrating AI into the cold chain. AI-driven insights can help optimise routes, reduce waste and lower costs. By analysing historical data and predicting climate patterns, the most efficient, reliable and unaffected delivery routes can be determined. This not only cuts costs but also supports the timely and reliable delivery of medicine and minimises environmental impact. AI will need real-time data on transportation conditions, such as weather patterns and temperature fluctuations, to determine the correct route and solution type needed for a successful delivery.

Sustainability matters, but it must be balanced with efficiency

Although reducing the environmental impact of cold chain logistics is essential, it cannot come at the cost of efficiency and patient safety. AI also plays an important role here, through not only determining the best routes and solution choice, but by unlocking efficiencies to help meet sustainability requirements. There must be a focus on minimising waste through forever-use packaging, making sure it is returned and re-used wherever possible. Adopting lighter, space-efficient packaging can lower fuel consumption and reduce emissions, as well as optimise the amount of product shipped to reduce cost. However, to truly have an impact, sustainability requires collaboration across the entire supply chain.

Global warming’s impact on cold chain infrastructure

As temperatures increase, so will the demand for enhanced cold chain infrastructure. Packaging solutions with significant autonomy will be required to maintain temperature and safe delivery, even in the face of extreme conditions. Additionally, climate-related supply chain disruptions may call for alternative backup routes, meaning redundancy will need to be built into distribution systems.

More frequent extreme weather events, such as heatwaves, flooding, wildfires, and landslides, will significantly disrupt supply chains. These events can lead to road closures, disrupted shipping lanes, and airport shutdowns, making it difficult to maintain consistent transportation routes. To address this challenge, it is crucial to collaborate with partners who have a wide global network to mitigate the risk of delays. These partners must also demonstrate agility and proactivity in adjusting plans as needed to ensure that patients receive their vital medicines without disruption.

Handling global warming requires a delicate balance

As learnt from any previous crisis, collaboration is key. Only by fostering collaboration between manufacturers, logistics partners and packaging providers can the pharmaceutical industry hope to balance sustainability, cost and reliability in the face of global warming. New technology and AI will be key drivers of this, along with the agility to react fast to any potential disruption. Companies that prepare now and find the right balance will be more efficient and gain a competitive edge in a market that demands both resilience and responsibility.

By Niklas Adamsson, COO at Envirotainer

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Still, today, the life sciences industry’s readiness to implement and harness ISO IDMP standards still varies considerably. The same is true of companies’ relative maturity in supporting FAIR data principles, geared to making data more Findable, Accessible, Interoperable, and Reusable.

These are goals that are actively promoted by Pistoia Alliance, a non-profit industry coalition working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration. Its IDMP-Ontology (IDMP-O) project aims to create a shared ontology (a representation of data properties and the relations between them), to encourage uniform adoption of the IDMP standards and, by extension, consistent information exchange.

With renewed momentum around EMA’s IDMP implementation in Europe, FDA’s own related plans in the US, as well as the cross-industry initiatives outlined above, MAIN5 recently partnered with Pistoia Alliance and data registry specialist Accurids to conduct new benchmark research to determine companies’ latest progress and planning around IDMP implementation.

Silos & poor standardisation still hamper bigger ambitions

Large pharma companies now generally have good awareness of the value of IDMP-based product data standardisation as part of wider process digitalisation ambitions, the survey confirmed. More than 70% of those surveyed identified IDMP’s value as an enabler of cross-functional data integration; only 11% saw compliance as the primary goal of IDMP projects.

Companies generally plan to integrate IDMP data from Regulatory, Manufacturing, Pharmacovigilance, Supply Chain, and Quality functions within the next three years. Research, (pre-) Clinical, and Commercial data integration will follow in the mid-term (within five years). This phased approach indicates that companies are initially prioritising data that supports regulatory submissions and compliance, followed by broader data integration to support product development and commercial strategies to maximise the benefits of IDMP.

As things stand, however, product data management continues to pose a challenge for companies across the board. The benchmark study identified particular issues with manual data collection, data silos, and a lack of data integration across systems. An unclear source of truth and insufficient use of trusted external sources were also flagged as barriers to harnessing product data more strategically.

Those actively striving towards more seamless data integration across and between functions felt that a lack of resources and issues with ‘ownership’ were the main barriers to achieving this (indicated by 44% and 41% of respondents), beyond a current lack of data standardisation (the main obstacle, cited by 56%). Surprisingly, the quality of data (and therefore its usefulness) was ranked below these factors (cited by 33%).

Growing IDMP-O interest

When asked if companies currently use IDMP as the master data model for their product information, many respondents were unsure how well aligned their existing model is. Just 40% felt confident that they possess an IDMP-compatible model, although 75% use IDMP to guide product information. This is one of the gaps addressed by Pistoia Alliance’s IDMP-O project, in that it allows the exact measurement of how compatible existing data models and ambitions are with IDMP.

Promisingly, 43% of the large pharma companies taking part in the benchmark research expressed a willingness to take IDMP-O into production within their organisations. Although an encouraging observation, many of the organisations that participated in the survey are inherently closer to IDMP-O than others in the industry, so the finding may not be representative.

Respondents were then invited to express, in their own words, where they anticipated deriving the most value from IDMP-O. Their open-ended responses confirmed good awareness of the ontology’s strategic benefits, including the associated scope to enhance the integration and exchange of product data – with regulators and industry partners, among other stakeholders.

Operationally, respondents recognised that the Pistoia Alliance ontology supports cross-functional alignment on data ownership, standardisation of data definitions, and adoption of a shared data model to enable system interoperability, and improve overall data quality. These factors pave the way for improved efficiencies in data management, decision-making, submissions, and compliance. (The IDMP-O can drive and facilitate master data management, automation, and AI – positively impacting analytics, and ultimately reducing costs.) There is still work to be done before companies can harness those benefits, however.

Promising signs of new progress

Where early enthusiasm around IDMP programs had waned in response to slow progress from EMA in Europe towards clarifying specific requirements, reigniting momentum behind IDMP-based projects should be a priority now – both among life sciences companies, and the supporting vendor community.

A raft of recent developments will help companies define concrete next steps and avoid potential rework. These include the EMA’s go-live of the Product Lifecycle Management portal (with Product Management Services and electronic application forms), as well as improved clarity on implementing SPOR services and integrating with EMA systems and processes.

Certainly, for companies with larger product portfolios, advanced technological capabilities will be needed to efficiently prepare data in bulk for what could be thousands of registrations. Manual updates per product by re-entering data in the PMS system is not feasible.

Defining the right strategy, implementing supportive system capabilities, recruiting and training a workforce to collect, transform, and submit data according to specific requirements is a significant undertaking that requires careful planning and execution.

Encouragingly, the survey does suggest that many companies are now actively working towards enterprise-wide integration of data and IDMP-related processes. Harnessing Pistoia Alliance’s IDMP-Ontology offers them their best chance of cross-functional alignment on data ownership, standardisation, and adoption of a common data model to enable interoperability and improvement of data quality in line with FAIR data principles.

All of this brings an opportunity to revolutionise how pharmaceutical data is managed and used, towards a more sustainable future for healthcare.

The full report, Accelerating Digital Transformation in Pharma with IDMP: An industry benchmark report on the status of IDMP standards implementation in Pharma and the role of the IDMP Ontology for accelerating digital transformation, is free to download at https://marketing.pistoiaalliance.org/hubfs/IDMP%20Pistoia%20Alliance%20Report%202024%20(5).pdf

About the Author

Michiel Stam is a management consultant and senior regulatory expert at MAIN5 with 15 years of experience in Regulatory Information Management (RIM) and IDMP. MAIN5 is a European consulting firm specializing in digitally-enabled change for Life Sciences R&D organizations. Its customized, high-value services and solutions span the product lifecycle – from regulatory affairs and data governance, to quality management and systems validation.

References

The IDMP benchmark survey of 18 pharma companies was conducted in Q3 2024 by Pistoia Alliance, MAIN5, and Accurids, and supported by the IDMP-Ontology project with participants from Abbvie, Amgen, AstraZeneca, Boehringer Ingelheim, Bayer, and Novartis.

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In today’s dynamic healthcare landscape, where scientific information evolves rapidly and access to medical experts is crucial, the need for highly trained MSLs is paramount. Yet, while the vast majority of MSLs want training, only two-thirds report that these opportunities are available to them, and almost 40% state that they lack time for professional upskilling.²

Current Pain Points in MSL Training

In addition to a lack of time, MSL training is plagued by a range of issues, including geographical barriers for global companies, which can make it challenging to deliver consistent training if opting for in-person delivery. Traditional in-person training also comes with substantial cost barriers and logistical hurdles, as well as the risk of information overload if delivered as multi-hour or multi-day live events. In turn, this can reduce MSL engagement and negatively impact knowledge retention.

Further, with rapidly evolving scientific information, data, and guidelines, there is a clear need for continuous learning and knowledge reinforcement. However, considering how busy MSLs are with other priorities, this can be challenging if opting for a traditional approach to training.

Finally, accurately measuring and demonstrating the effectiveness of training in terms of its impact on MSL performance and knowledge retention can prove a challenge.

Training Needs for MSLs in Today’s Evolving Landscape

For training to be effective, the Medical Affairs Professional Society (MAPS) identified that it needs to be:³

• Practical and able to be used immediately.
• Technology-based, flexible, and available whenever, wherever the trainee wants to access it.
• Available in multiple formats.
• Consumable as individual, low-commitment offerings but placed within a larger framework.
• Collaborative and engaging, with learning enhanced by mentoring, gamification, accountability partnering, collaborative case study reviews, and more.
• Relevant and streamlined to reduce the cognitive load and improve the learning experience.

With these points in mind, the importance of leveraging digital tools and cutting-edge technologies becomes evident.

Digital Tools for MSL Training

Luckily, there is no shortage of digital tools available for MSL training. Some important examples include:

1. Asynchronous e-Learning Platforms
   These ‘over-time, anytime’ platforms offer versatile learning opportunities for MSLs: from hosting on-demand videos, podcasts, and written materials to interactive infographics and posters, digital journal clubs, quizzes, online case studies, and more. Adding a well-maintained resource center to serve as a knowledge repository/central information hub further helps ensure consistent and accurate information dissemination.

2. Mobile Learning and Microlearning

Mobile apps and micro modules—short, focused learning modules—are ideal for providing on-the-go knowledge reinforcement and just-in-time learning. Having this option available alongside other, more comprehensive tools enhances accessibility and convenience for busy MSLs.

3. Virtual Reality (VR), Augmented Reality (AR), and Holovision Technology

VR, AR, and Holovision technology represent novel tools that can be used to create a variety of immersive training experiences, both for MSL- and external healthcare provider (HCP) education. Examples include simulation of patient or HCP interactions, including objection-handling, as well as disease state, mechanism, and clinical data visualizations. These technologies are also ideal for case-based learning.

4. Artificial Intelligence (AI)-powered Tools

AI-powered learning paths can be used to tailor education to individual needs and preferences. For example, AI chatbots can provide instant answers to MSL queries, and AI avatars can be used to enhance case-based learning. While still in its infancy, there are vast opportunities for AI-based MSL training.

Benefits of Digital MSL Training

Leveraging digital technologies for MSL training comes with numerous benefits, including:

1. Increased Accessibility and Flexibility

Digital platforms eliminate geographical barriers, allowing MSLs to access the same high-quality training irrespective of location or time zone. This ensures consistency in knowledge and skills across global teams. The asynchronous nature of virtual training technologies means that MSLs can access training materials at their convenience, without the need to travel to a central location for training. This flexibility is crucial for MSLs—who often spend substantial time out in the field—to fit learning into their busy schedules.

2. Enhanced Engagement and Knowledge Retention

Interactive training modules that incorporate simulations, quizzes, and gamified learning experiences are known to increase engagement and improve knowledge retention compared to passive learning formats. The digital learning format also allows for the incorporation of videos, animations, and interactive visuals, which further enhances the learning experience and caters to different learning styles. In addition, by leveraging novel tools such as VR, AR, and holovision technology, MSLs can practice real-world scenarios, such as objection handling or presentations at medical conferences, in a safe and controlled environment, leading to enhanced learner confidence.

3. Improved Training Effectiveness and Performance

Digital platforms can track MSL progress over time and identify areas where they need additional support much easier than what can be done with traditional learning approaches, allowing for more personalized learning and continuous support. Likewise, data analytics can be used to assess the overall effectiveness of the training, enabling easy identification of potential areas for improvement.

4. Up-to-date, Consistent Information

Digital platforms and centralized knowledge hubs allow for the rapid dissemination of new scientific information, clinical trial data, and treatment guidelines, ensuring that MSLs are always up-to-date with the latest developments. The use of digital tools also ensures that all MSLs in a company receive the same consistent messaging, regardless of their location.

5. Enhanced Collaboration and Communication

Through the use of online forums and discussion boards, MSLs can seamlessly share best practices, ask questions, and collaborate with colleagues on their own schedules. Virtual meetings and webinars can be added as needed to enhance knowledge-sharing and collaboration further.

6. Reduced Training Costs

Lastly, digital training largely eliminates the need for physical training materials, travel, accommodation, and venue rentals, thereby significantly reducing training costs. Digital platforms can easily be scaled to accommodate large MSL teams, making them a cost-effective solution for global pharmaceutical companies.

Implementation Considerations

Before implementing a new digital training course, life science companies should first evaluate and select the appropriate digital training platform and technology based on their specific needs. Depending on the technology chosen, there may be a need to proactively promote user adoption and address potential resistance to digital training. After its launch, it will be key to conduct ongoing evaluations of the program and technology to optimize the training. Companies may also want to consider working with a single, one-stop provider for all training needs—technological, strategic, and content—to further streamline the process.

By Natalie Yeadon, President & CEO Impetus

References

1. https://themsls.org/what-is-an-msl/
2. https://www.onemsl.com/wp-content/uploads/2022/05/One-MSL-Global-Survey-2022-Findings-Report-MSLs.pdf
3. https://medicalaffairs.org/training-medical-science-liaisons-msl/

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