Artificial intelligence is shaping the future of life sciences, offering opportunities to streamline processes, enhance regulatory compliance, and improve operational efficiency. However, despite the potential, AI adoption in the industry remains cautious, with organisations carefully assessing security, compliance, and return on investment before taking the next step.
To explore the current landscape of AI in life sciences, we spoke with Peter Smedegaard Andersen, Partner & Consultant at DAQUMA. In this interview, Peter shares insights on the key considerations for AI adoption, the challenges organisations face, and where AI is already delivering tangible value. He discusses how DAQUMA is supporting companies in making informed AI investments and how the CARA Life Sciences Platform is incorporating AI in a way that is both practical and risk-conscious.
Q: What key factors should organisations consider before implementing AI?
Peter: Before implementing AI, organisations need to adopt a positive mindset and a willingness to experiment. Rather than aiming for a full, end-to-end replacement of human roles, it is more effective to focus on specific tasks where AI has a high chance of success. We propose approaches starting with small use cases; tasks where AI can provide value based on human input, such as translations and information gathering. While this may seem like a simple workflow, the amount of repetitive effort involved in such tasks is significant, making them ideal for AI-driven automation.
It’s important to recognise that many of the leading out-of-the-box AI solutions are not designed specifically for life sciences. Hyperscalers like AWS, Azure, and Google Cloud Platform offer AI capabilities that organisations can use as a starting point, but integrating these into the life sciences landscape is a major challenge. AI must be anchored within existing quality management systems (QMS), as well as safety, regulatory, and clinical systems. So it is essential to leverage the capabilities of suppliers already operating within the industry and industry-specific technology providers who understand the regulatory and operational requirements.
Q: What concerns are we currently hearing in the market about AI adoption?
Peter: The primary concerns we’re hearing in the market are around security and compliance rather than AI’s actual usage. Adoption is still relatively low in the industry, so organisations aren’t discussing limitations in detail. Instead, the focus of most conversations is on AI’s potential, which inevitably leads back to the fundamental questions: Is this secure? Can this be made compliant?
Another key concern is transparency in AI investments. Organisations are asking whether AI is a risk-averse investment, whether it will drive cost reductions or future gains, and, critically, how to accurately calculate that investment. One of the biggest challenges is the mistrust in the ability to quantify AI’s financial impact.
This is where DAQUMA can provide support. We help organisations structure their AI investment calculations by ensuring all relevant variables are accounted for, making adjustments for risk-based factors, such as the degree to which users will replace existing processes with AI, and continuously monitoring real-time data on rate of adoption, response accuracy and end-user trust. Our approach provides tangible backing for the business case, helping organisations to continuously make informed decisions.
Q: In which areas can life sciences organisations gain the most value from AI today?
Peter: If we consider immediate quick wins; areas where AI can be implemented with minimal disruption while delivering a clear, positive impact on business operations, then there are several key opportunities right now.
One significant area is training, particularly in enabling real-time interaction with SOPs. AI-powered chat interfaces can allow employees to quickly access and understand quality procedures, ensuring that compliance knowledge is always available and up to date.
Another high-value application is in regulatory review procedures. AI can assist in evaluating the impact of regulatory changes on submitted information and product labels, helping organisations identify potential compliance risks across product portfolios. This can significantly streamline regulatory processes and reduce manual effort.
Translations are also an essential area where AI can provide immediate value. Global pharmaceutical organisations must meet local regulatory requirements across multiple regions, whether in clinical trial operations, regulatory interactions, or product releases. AI-driven translation tools can help manage this efficiently, ensuring accuracy and compliance while reducing the time and cost spent on manual translation.
Q: What’s a typical timescale on an AI project? What can companies do to prepare and make adoption quicker?
Peter: So far, most AI projects we’ve seen in life sciences have been relatively small in scope. They typically start as PoC engagements, where organisations test a specific use case, evaluate whether the expected outcomes are met within a set timeframe, and then scale further.
Most are searching for a productised approach to AI adoption. Rather than implementing AI as a one-off project, they are looking for solutions they can continuously improve and build upon internally. This focus on product closeness, ensuring that AI solutions integrate seamlessly with internal systems and workflows, plays a crucial role in long-term success.
Attempting a full-scale AI transformation all at once is a massive undertaking. Instead, companies should focus on achieving many small incremental wins through use-case-based initiatives. These projects should be tightly integrated with existing data and document environments, allowing AI to enhance current operations rather than requiring a complete overhaul
Q: What kind of support do DAQUMA offer to customers wanting to invest in AI?
Peter: DAQUMA provides a range of support services to help organisations make informed AI investment decisions and successfully implement AI solutions.
One key area of support is business case development, helping companies structure their AI investment by calculating costs, assessing risk factors, and providing real-time monitoring to validate ROI. Similar to our approach for broader digital transformation initiatives.
We also offer a structured evaluation of AI models. DAQUMA has a matrix that provides a standardised score for how well different LLMs perform for various tasks. This, combined with consumption-based pricing analysis, helps organisations assess whether a particular model’s capabilities align with their budget and needs. Additionally, we can evaluate custom-built or company-specific AI models, comparing them to open-market solutions to determine their effectiveness.
Beyond assessment, we support the build of specific AI use-case implementations, such as GPT-style chatbots, AI-driven document review procedures, AI-powered translation for quality, clinical, and regulatory documents, and response generation for health authority (HA) queries.
Q: How is the CARA Life Sciences Platform utilising AI to benefit Life Sciences companies today?
Peter: Generis is currently working on a number of different AI initiatives. The first AI application on the CARA Life Sciences Platform is the chat with data – enabling users to interact with their data and documents using natural language in an intuitive, AI-powered way. This capability is already being tested in a POC with a large customer and has been designed for quick deployment with minimal risk. This functionality will be made available to all CARA customers in the next release in March.
A major challenge in the industry today is that many vendors are rushing to integrate AI into their products simply so they can claim to have AI capabilities. CARA, however, has taken a different approach. The platform ensures that any AI functionality added is not just a marketing feature but practical, valuable, and easily achievable for customers.
Another key benefit is how CARA helps reduce the risk factor associated with AI adoption. Instead of promising a complete transformation overnight, CARA enables companies to take realistic, incremental steps toward AI-driven improvements. While AI may not yet deliver everything companies imagine when discussing its potential to revolutionise life sciences, CARA allows organisations to test, refine, and implement AI solutions pragmatically, aligning progress with their acceptable level of risk.
AI is poised to bring significant advancements to the life sciences industry, from improving regulatory efficiency to enhancing compliance and operational workflows. However, as Peter highlights, successful AI adoption requires a strategic approach—one that balances innovation with security, compliance, and measurable ROI. By starting with targeted use cases, leveraging industry-specific AI solutions, and integrating AI into existing quality frameworks, organisations can unlock real value while mitigating risks.
Platforms like CARA are leading the way in responsible AI adoption, ensuring that new capabilities are not just cutting-edge but also practical and risk-conscious. With the right mindset and expert guidance from partners like DAQUMA, life sciences companies can confidently navigate the AI landscape, making informed decisions that drive both short-term wins and long-term transformation. To learn more about CARA AI contact us today : sales@generiscorp.com.
Peter Smedegaard Andersen is a digital leader with extensive experience in Pharmaceutical and Life Science, specializing in innovation, data and AI. Peter started the data consultancy DAQUMA, which supports digital innovation and implementation in highly regulated domains of the Life Science industry. To learn more about DAQUMA visit www.daquma.com
