Accelerating life-saving innovations: How Polish software engineers support global pharma and biotech R&D

Pharma and biotech are racing to deliver new therapies faster, yet the journey from idea to approved treatment remains long, costly and uncertain. With vast datasets, strict regulations and complex R&D pipelines, innovation today depends as much on biology, as on software engineering.  Increasingly, Polish engineers are stepping into this space – designing solutions that connect science with digital infrastructure and make research faster, data more reliable and innovation easier to scale. 

Innovation under pressure 
Pharma and biotech face growing pressure to deliver new therapies, while development cycles remain lengthy and expensive. Demand – driven by ageing populations, chronic disease and the promise of personalised medicine – is rising. According to Precedence Research, the biotechnology market was valued at $1.55 trillion in 2024 and is projected to reach $5.71 trillion by 2034, growing at annual rate of 13.9%.¹

The financial challenge is equally striking. Deloitte estimates that the average cost per drug asset reached $2.2 billion in 2024, with clinical trials consuming as much as 70%.² McKinsey notes that although the number of pipeline assets has doubled in the past decade, annual FDA approvals have remained stable at around 43 – evidence of persistent inefficiencies in the system. ³ 

These pressures explain why digitalisation and automation, as well as data science and advanced analytics are no longer optional but essential. To accelerate discovery, companies must learn to extract value from enormous datasets, integrate evidence across different stages of R&D, and navigate increasingly complex regulatory frameworks.  

This shift has created a natural space for technology companies who can combine engineering expertise with an understanding of scientific workflows. 

Technology at the core of life sciences 
Every field of life sciences today generates vast amounts of data. Genomics, oncology research, personalised medicine and molecular biology generate terabytes of structured and unstructured information every day – far beyond what scientists can handle unaided. Advanced cloud platforms, machine learning models and data visualisation tools have become as fundamental to research as pipettes and microscopes. 

The COVID-19 pandemic provided a clear example. mRNA technology, studied for decades, became the basis for vaccines in less than a year. This acceleration was not only a triumph of biology, but also of IT: Digital systems enabled the rapid processing of genomic sequences, modelling of molecular interactions and management of vast volumes of clinical trial data. Without such tools, compressing development timelines from the usual 10–15 years to under 12 months would have been unthinkable.

AI has also reshaped the landscape. DeepMind’s AlphaFold 2, recognised by the Nobel Committee, has predicted the structures of more than 200 million proteins – a breakthrough that has provided researchers with a reference library for understanding disease mechanisms and identifying potential drug targets on an unprecedented scale.

Even so, complexity remains the defining feature of pharmaceutical and biotech R&D. Each new therapy must navigate clinical protocols, lab systems, regulatory requirements and organisational silos. Technology cannot remove this complexity, but applied with domain awareness, it connects the dots – streamlining workflows, reducing duplication and creating transparency across research networks. 

AI is often seen as the solution, with generative models already applied in drug discovery, clinical development and regulatory operations. Yet these systems are only as reliable as the data they use. Fragmented datasets, inconsistent quality and legacy infrastructure remain serious obstacles. Without robust validation, outputs risk being unreliable – making data governance and data validation frameworks essential. This is where software engineers play a decisive role: By designing data architectures, building validation pipelines and embedding AI-agents into research platforms, they ensure that scientific innovation is supported by systems that are accurate, compliant and fit for purpose. 

For pharma and biotech companies, the challenge is no longer adopting technology but ensuring it supports – rather than disrupts – scientific practice. Increasingly, the engineers who make this possible are being found in Poland. 

Poland on the global map 
Poland is home to one of the largest technology talent pools in Europe, with over 400,000 software developers. Polish engineers consistently score highly in international rankings and benefit from a strong STEM education. What sets Polish teams apart is their technical expertise and their ability to operate in regulated, data-intensive environments such as pharma and biotech. Their competence is recognised internationally: at the first International Olympiad in AI in 2024, the Polish national team won two gold medals, a bronze and a special award, finishing second overall.  

Alongside its technology expertise, Poland’s biotech sector is also gaining momentum. According to Statistics Poland (GUS), companies invested 2.38 billion złotys in biotechnology R&D in 2023, an increase of 22% compared to the previous year. At the same time, the country’s R&D biotechnology market is valued at around 14.7 billion złotys, with forecasts suggesting growth to 21.5 billion złotys by 2027. ⁴   

Geography and regulation provide further advantages. As an EU member state, Poland follows the same compliance frameworks as Western Europe, including GDPR – crucial for managing sensitive research data. Shared time zones, cultural proximity, and English proficiency make collaboration straightforward.  

This position is reinforced by wider European sourcing trends. Surveys show that 71% of Nordic IT leaders and 61% of organisations in the UK & Ireland plan to maintain or increase outsourcing over the next two years, with scalability, access to talent and innovation among the top drivers.⁵ Reports by ABSL and PAIH further highlight Poland as one of Europe’s leading nearshoring destinations, thanks to its large IT talent pool, cost competitiveness and EU regulatory alignment.  

Together, these strengths – a large and skilled workforce, expertise in AI and cloud, regulatory alignment, cultural compatibility and strong nearshoring potential – position Poland as a strategic technology partner for global pharma and biotech innovation.  

What it looks like in practice – examples from the field 
The move toward digital-first research is evident in how biotechnology companies redesign their processes. At Holisticon Connect, I’ve seen how software development teams collaborate with international biotech and pharma partners to tackle some of the industry’s toughest challenges. The projects delivered are more than code – they are part of the innovation journey that makes research faster, safer and more effective.  

One US-based biotech, for instance, set out to use AI to accelerate the discovery of new medicines. What they lacked was not more algorithms, but a single research environment that could bring together laboratory processes, cheminformatics applications and machine learning models into one connected system. Holisticon Connect supported the client in building and optimising a cloud platform that started on AWS and later has been seamlessly migrated to Google Cloud. It connected laboratory automation with retrosynthesis and compound scoring tools, and gave scientists access to AI services capable of processing millions of inference requests each month. Designed with usability in mind, it let researchers focus on experiments rather than technical complexity. 

Another case: In oncology, a global pharma company faced bottlenecks in sequencing workflows. Manual steps and fragmented pipelines slowed research and made results harder to reproduce. By automating and standardising key processes, these workflows became faster, more reproducible and scalable – an essential step in accelerating cancer research. 

Genomics presented another challenge: The ability to analyse vast volumes of data in real time. To support precision medicine, engineers at Holisticon Connect developed platforms that allow researchers to explore genomic signals live, enabling them to spot therapeutic targets much more quickly. This shift from static to dynamic analysis gave research teams the agility to refine hypotheses on the fly, moving closer to the vision of responsive, data-driven drug discovery. 

Finally, the issue of scaling:  Genomics projects routinely generate terabytes of data, overwhelming conventional IT systems. Scalable infrastructures were therefore designed to handle immense volumes of information while supporting the identification and validation of new molecules. These platforms ensure that data growth does not become a barrier but instead a resource – allowing scientists to explore more possibilities without being constrained by technical limits. 

Across all these initiatives, the common theme is clear: Technology is not just a support function, but an enabler of innovation. By bridging the gap between IT and science, Polish software teams have helped transform isolated tools into integrated research ecosystems – accelerating the pace at which new therapies can be discovered and developed. 

 
Collaboration: Science meets software 
Breakthroughs in life sciences rarely happen in isolation. They emerge when scientists and software engineers manage to bridge two very different ways of working. Researchers are validation-driven, focused on testing hypotheses and building knowledge step by step. Developers, in contrast, are product-driven, aiming to create robust and usable systems. When these perspectives come together, the result is software that is both scientifically and technically reliable. 

Making this collaboration work requires more than goodwill. Clear communication, shared terminology and regular feedback loops are essential. Teams that hold structured meetings, use visual tools to explain complex concepts, and work iteratively in short cycles are far more likely to succeed. Agile methods, adapted to the realities of research, help ensure that solutions can evolve alongside the science they support. This approach enables developers to build tools that genuinely fit research needs, while scientists see their ideas translated into functioning, compliant systems.  

In this sense, developers are not just writing code – they act as catalysts for innovation, creating digital environments that free scientists from technical obstacles, speed up discovery, and ensure that findings can be scaled into real-world therapies. 

What’s next: Precision medicine, AI and data-driven healthcare 
Trends indicate that over the next decade life sciences will shift further toward precision medicine, AI-supported diagnostics and data-centric research. Analysts suggest that healthcare systems in developed countries are moving toward more fully digitalised models, with AI expected to play a central role in diagnostics and treatment planning.  

These trends promise faster and more precise treatments – but their success will depend on how well technology is embedded into real clinical and research practice.  

Generative AI is becoming one of the most prominent tools in this shift. The global market for AI in life sciences is projected to grow from $3.6 billion in 2025 to $11.1 billion by 2030, at a compound annual growth rate of nearly 25%.  ⁶  

Pharmaceutical companies are already investing billions of dollars into its use in areas such as drug discovery, clinical development and regulatory operations. Yet the rapid adoption of these tools also brings challenges: Data in pharma and biotech is often fragmented, inconsistent and incomplete, and without robust validation, generative AI outputs risk being unreliable or unusable in regulated environments. 

This is why data validation frameworks and AI agents have become critical components – helping ensure accuracy, compliance and trust at scale.  The real opportunity – and challenge – lies in making these innovations human-centered and clinically relevant. AI may dominate headlines, but its true impact will be measured by better outcomes for patients and wider accessibility of care.  

Poland’s technology ecosystem is well positioned to play a strategic role – as contractors delivering code, and  partners supporting global life sciences in the next phase of innovation. 

The road ahead 
 Accelerating life-saving innovation in pharma and biotech requires more than breakthroughs at the lab bench. It depends on connecting science with technology in ways that respect regulation, ensure scalability and, above all, improve outcomes for patients. 

With a large talent pool, strong STEM foundations and expertise in AI, cloud and data, Polish software teams are helping global organisations shorten R&D timelines, improve reliability and open new paths in genomics and precision medicine. What makes these contributions effective is not only technical capability but also close collaboration between engineers and scientists. Rather than building one-off tools, Polish teams are co-creating integrated research ecosystems – platforms that evolve with science and enable researchers to move faster and more confidently toward discovery. 

As the industry shifts toward data-driven, personalised and AI-enabled healthcare, such partnerships will become even more critical – ensuring that innovation reaches patients with greater speed and reliability. 

Sources:

1. Precedence Research: https://www.biospace.com/press-releases/biotechnology-market-size-expected-to-surpass-usd-5-71-trillion-by-2034-biologics-regenerative-therapies-chromatography-and-tissue-engineering-in-focus
2. Deloitte: https://www.deloitte.com/us/en/Industries/life-sciences-health-care/articles/measuring-return-from-pharmaceutical-innovation.html
3. McKinsey: https://www.mckinsey.com/industries/life-sciences/our-insights/accelerating-clinical-trials-to-improve-biopharma-r-and-d-productivity
4. https://bioinmed.pl/en/press-office/news/diversification-industries-and-areas-recipe-investment-success-biotechnology
5. Whitelane Research: https://whitelane.com/nordics-2021/, https://whitelane.com/uki-2025/
6. Mordor Intelligence, https://www.mordorintelligence.com/industry-reports/artificial-intelligence-in-life-sciences-market