Technology in 2025

How will advancements in technology affect the pharma sector, from both commercial and R&D points of view?

Chris Moore at Veeva Europe

As we advance through 2025, biopharma companies are increasingly using artificial intelligence (AI) and new data tools to improve commercial performance and make R&D more efficient. AI is changing the way the industry engages with healthcare professionals (HCPs), allowing for personalised interactions, better content strategies and useful insights for field teams. In R&D, new methods for handling data and navigating regulations are speeding up clinical trials, shortening approval times and improving transparency at research sites.

Commercial

On the commercial side, making the most of AI and data-driven solutions depends on having clean, organised data and investing in systems that are scalable and compliant. Companies focusing on data quality, following regulations and using AI-driven insights will be better prepared for long-term success. Key improvements include AI-powered tools for creating and reviewing content, advanced analytics for smarter business decisions, and streamlined operations through integrated quality and regulatory systems.

Prediction 1: A focus on clean data will fuel compliant AI innovation in the EU
 The recent wave of AI innovation has fallen short of transforming commercial life sciences.1 In 2025, European biopharma companies that unlock harmonised internal and external data will start to reap commercial rewards.

Biopharma organisations will combine off-the-shelf AI engines with more harmonised, clean data. The integration of these AI solutions with well-structured, high-quality data sets will enable organisations to achieve deeper insights, enhance operational efficiency and drive evidence-based decision-making. Acquiring data from trusted, internally verified sources will lead to greater confidence in AI-generated outcomes. By ensuring data integrity and consistency across different functions, companies can minimise biases and inaccuracies, ultimately boosting stakeholder trust. This will make it easier to scale pilots from single-market, single-brand solutions across the enterprise. As these scaled solutions prove their value, biopharma organisations will be better positioned to optimise their supply chains, improve patient outcomes and streamline regulatory reporting.

The EU recently introduced the Artificial Intelligence Act – the first comprehensive AI regulation by any regulator, designed to ensure that AI is developed and used safely.2 This landmark legislation establishes clear guidelines on risk categorisation, transparency and accountability, compelling companies to align their AI strategies with ethical and legal standards. Along with existing European data privacy rules, European biopharma companies will have clear principles to support future investment and innovation. These combined regulations will foster an environment where AI-driven projects are not only innovative, but also aligned with societal expectations and legal mandates. Commercial success will come to those that clean up their data, secure new sources and interrogate them within this regulatory framework.

In the long run, organisations that proactively embrace compliance while leveraging AI will gain a competitive advantage, positioning themselves as industry leaders in ethical AI adoption.

Prediction 2: Medical, legal, and regulatory affairs review (MLR) content will be an early AI success story
 Growing AI use cases for content creation, hygiene and quality checks will drive record-high content volume, making it more difficult to get relevant messages to market. This influx of content will challenge teams to maintain consistency and alignment with brand messaging across multiple channels. As a result, content teams and agency partners that focus AI investments on both targeting high-value content creation alongside improving MLR review will be the first to see return of investment (ROI). Leveraging AI to identify content gaps and optimise messaging strategies will further enhance engagement and impact.

By decreasing review cycles and reducing cycle times, AI-empowered MLR teams will accelerate compliant, accurate commercial content, despite the growing volume. Automating routine tasks within the MLR process will allow teams to allocate more time to strategic decision-making and innovation. These efficiencies will help move MLR review from the last stop in the content cycle to a proactive role with greater visibility and input into the content creation engine, further reducing rework. This shift will foster greater collaboration between content creators and reviewers, leading to higher quality outputs and faster go-to-market timelines.

Organisations that eliminate complexity in their content supply chain by classifying content using standard taxonomy, removing bespoke solutions and harnessing hosted large language models will move faster and more efficiently.

Standardisation efforts will also enhance interoperability across global markets, ensuring regulatory compliance while maintaining flexibility. Content and MLR teams that prioritise content quality over content quantity will be early commercial leaders in delivering value from AI. By focusing on meaningful, impactful content, these organisations will build stronger connections with their audiences and achieve sustainable growth.

Prediction 3: Advanced analytics AI wins will put people first
As commercial organisations consider AI use cases for advanced analytics, they will also be forced to confront AI’s limits. Despite its potential, AI systems often struggle to adapt to the nuanced realities of commercial operations, where human judgment and experience play a critical role. For example, an AI-generated ‘next best action’ would need to consider a wide range of variables that are not easily accounted for, from a rep’s call plan to challenging HCP access to incentive compensation plans. If these contextual factors are overlooked, AI recommendations may fail to align with on-the-ground realities, leading to inefficiencies. If the recommended next best action is one a rep can’t, or won’t, act on, AI is just adding more noise to the system.

As a result, early leaders will focus on investments in people to get the best ROI from carefully selected initiatives. Success will depend on equipping teams with the skills to interpret AI-driven insights and incorporate them into their daily workflows. AI use cases in advanced analytics will be most successful when companies spend time upfront defining problems, structuring data and training users to act on insights. Early leaders will build a change management culture that addresses knowledge and skills gaps. This approach will help foster greater acceptance of AI, and ensure its recommendations are actionable and relevant.

But AI tools that are bolted onto disconnected systems or need complex integrations with business intelligence tools will also slow down insights and discourage user adoption. Seamless integration into existing workflows will be key to driving adoption and maximising efficiency. AI that augments decision-making and is embedded in users’ workflows – similar to how navigation apps guide drivers through traffic in real time – will see wider use and set the stage for long-term ROI. By focusing on AI solutions that complement human expertise rather than replace it, organisations can unlock new levels of productivity and business value.

Research and development

In R&D, the spotlight is on speeding up approvals through simultaneous submissions, sharing data more openly with contract research organisations (CROs) and automating safety monitoring. Simultaneous submissions can cut approval times significantly, while better data sharing with CROs leads to faster, more informed decisions. Advanced automation, supported by reliable safety data, will simplify pharmacovigilance (PV) and reduce operational challenges.

Prediction 1: Simultaneous submissions will shave years off approvals
Sequential submissions, once standard, are now seen as an outdated obstacle to timely patient access. Regulatory agencies and pharmaceutical companies are increasingly recognising the inefficiencies of this approach and are turning to more agile solutions. Although digitalisation has streamlined some aspects of regulatory submissions, core markets often receive approvals first, delaying access in smaller markets. This staggered approach results in significant gaps in global market penetration.

New methods, such as active dossiers, will allow teams to reuse prior submissions more effectively. By leveraging modular approaches and cloud-based platforms, companies can ensure consistency and efficiency across multiple jurisdictions. As more companies and health authorities embrace simultaneous submissions, timelines that once exceeded five years could be reduced significantly. This acceleration will provide patients with faster access to life-saving treatments and reduce operational burdens. These advancements will improve access for underserved patient populations while alleviating regulation bottlenecks.

Prediction 2: CRO data transparency will boost trial success
Sponsors are increasingly prioritising CROs that provide continuous data transparency. Real-time, centralised data access is becoming a key differentiator in CRO selection. In 2025, the industry will see a shift toward end-to-end data ownership, fostering more fluid collaboration between sponsors and CROs. This shift will empower sponsors to make faster, more informed decisions throughout the trial process. Transparent, real-time data sharing will improve decision-making in protocol design, site onboarding, rare disease participant identification and endpoint adjustments. By leveraging AI-driven analytics, sponsors can further refine trial parameters and reduce inefficiencies. Emerging biotech organisations will benefit from enhanced oversight, enabling more agile operations. Greater data transparency will build trust across the clinical development ecosystem, improving trial outcomes and accelerating access to new medicines.

Prediction 3: Comprehensive and reliable safety data will fuel advanced automation
Safety professionals continue to grapple with an age-old question: how to handle growing data volumes with fewer resources while maintaining high quality? AI holds promise to do more with less, but inconsistent and disconnected data creates risk.

To effectively support AI, safety teams will strengthen their data foundations with standardised, end-to-end PV processes. Cross-functional workflows will eliminate manual data transfers and provide clear data traceability back to the source. By simplifying and standardising their systems landscape, companies will lay the groundwork for accelerating automation and AI innovation. This end-to-end data flow also opens the door for improved collaboration across organisations. For example, processes like timely reporting of serious adverse events from clinical electronic data capture systems can be done automatically with more complete data.

References:
1. Visit: emcap.com/thoughts/ai-s-curve-plateau-proprietary-businessdata-breakthrough/

2. Visit: artificialintelligenceact.eu/

As president of Veeva Europe, Chris Moore is responsible for growing the business in the region. Chris has over 30 years in the life sciences industry, starting his career at ICI Pharmaceuticals (now AstraZeneca). Chris then joined start-up Kinesis, building a team delivering document management solutions for pharmaceutical companies. Through a series of mergers and acquisitions, Kinesis became PwC; Chris was made a partner with PwC in 2001. Chris went on to run both European and US (West Coast) life sciences businesses for IBM before leading the IBM global life sciences consulting Business Analytics and Optimization unit. Most recently, Chris was the lead partner for life sciences for Europe, the Middle East and Africa at EY. Chris holds a Bachelor of Science degree in information technology from the University of Salford, UK.