Augmented intelligence: The next-gen engine for biopharma innovation
Small and mid-sized biotherapeutic companies are the primary engine of the biopharma industry, driving significant medical innovation. Large pharmaceutical companies increasingly view these smaller firms as their innovation laboratory. However, while these nimble firms benefit from being faster and more agile, they often lack the capital and scale of big pharma.
In today’s challenging investment landscape, this means their valuable scientific insight is routinely constrained, forcing executives to seek out analytical precision to reduce risk and maximise every dollar. With limited resources and little margin for error, the stakes are existential. This new reality demands a technological imperative.
The new biotech funding reality
While overall investment levels have remained steady, venture capital (VC) has become increasingly concentrated, allocating the same amount of capital among fewer companies. The post-COVID environment has further shaped investor priorities, with investors now prioritising deeper, more defensible bets and demanding stronger validation much earlier in the development process.
In this new reality, cash preservation has become a strategic imperative for biotech C-suites. Since capital is scarcer and expectations are higher, companies must maximise the efficiency of every dollar. This forces them to dramatically reduce risk in their development pathway – from trial design and patient selection to overall development strategy.
To meet these demands, the companies that thrive will be those that effectively use AI to amplify human judgement, providing the analytical precision needed to demonstrate that their clinical path is both scientifically and financially sound. In this context, speed is not only about increasing investment, it’s a matter of survival.
From artificial to augmented intelligence
Pure-play AI often prioritises full automation, prediction, and activity replacement. In the biopharma market, this autonomous approach is often insufficient. What is required is augmented intelligence: a model based on human-machine collaboration that enhances human intellect and decision-making, rather than seeking replacement.
Unlike the popular, and often feared, concept of fully autonomous AI taking over jobs, augmented intelligence systems are designed for a collaborative working environment. By offloading tedious, data-heavy, or repetitive work to the machine, the human worker is freed to focus on high-value tasks that truly require human insight. This approach allows organisations to optimise their clinical expertise. Clinical teams are empowered to rapidly bench test hypotheses, optimise trial design, and validate strategic assumptions using massive datasets.
This collaborative process is the confirmation approach: a combination of human intuition with rigorous data-driven validation. In this human-centric model, the AI serves as an assistant and apprentice, suggesting options and executing computational analysis, rather than acting on its own behalf. By keeping the human mind firmly in the driver's seat, this partnership optimises resource allocation and spending while significantly reducing the risk of human error.
For biopharma, augmented intelligence dramatically accelerates innovation by transforming the initial, decades-long phases of drug discovery into a rapid, data-driven hunt. Researchers can instantly identify novel disease targets and predict the efficacy and toxicity of millions of potential compounds in silico by analysing massive, multimodal datasets.
This human-guided analysis refines candidate selection faster than ever before. Critically, by integrating real-world evidence (RWE) and patient-specific data, augmented intelligence is also essential for advancing personalised medicine, enabling researchers and clinicians to quickly discover biomarkers and tailor treatments to specific genetic profiles, ensuring the right therapy reaches the right patient in an expedited process, all while upholding rigorous safety and regulatory standards. With AI support learning on the job, the ability to enhance human intelligence also increases drastically over time. The sooner you start, the greater the advantage.
The strategic value of augmented intelligence
Augmented intelligence is strategically valuable because it strengthens both the scientific and financial case for every decision. This intelligence will allow companies to show investors that their clinical path is grounded in both human insight and analytical precision.
The outcome is a reduction of risk, preservation of capital, and the building of investor confidence. Biotherapeutic companies can use augmented intelligence to communicate evidence-based confidence to their investors, partners, and boards, reinforcing the scientist’s expertise with data insights.
Augmented intelligence is set to become central to VC strategy. It won’t just be used for data analysis, but rather as a crucial validation mechanism for making smarter scientific investment decisions. With numerous VCs already using AI-based decision making in their investment strategy, they will eagerly support a similar usage in biotech and biopharma.
The path forward
The fundamental truth is that AI in biotech isn’t about replacing scientists, it’s about amplifying their insights and capabilities.
By utilising augmented intelligence early, small and mid-sized companies can maximise their innovation potential, minimise risk, and accelerate progress. The next wave of biotherapeutic breakthroughs will not come from AI alone, but from the powerful fusion of great science and augmented intelligence.
About the authors
David Lazerson is co-founder and CEO of Briya, a healthcare technology company supporting vital medical and biopharma research and innovation through proprietary AI and data solutions. He has over a decade of experience in cybersecurity and health IT leadership. Prior to co-founding Briya, Lazerson served as CEO of Axioma Cyber Services. Before that, he was engagement manager and team leader at Sygnia, providing elite cyber consulting, incident response, and managed detection and response services. Lazerson served as a special cyber operations officer with the SIGINT National Unit and studied Physics and Cognitive Science at the Hebrew University of Jerusalem.
Stefan Moch is VP of health at Arvato Systems, where he leads initiatives aimed at digitalising the health sector, focusing on integrating AI solutions for stakeholders and customers. He specialises in the digital transformation of the pharmaceutical and healthcare sectors and represents Arvato in major industry bodies addressing digital challenges across the industry. Prior to joining Arvato, Moch was a project manager at Verlag C.H. Beck, one of Germany's oldest publishing houses, and served as an officer in the German Federal Armed Forces. He holds an MBA from Universität Educatis and a law degree from the University of Augsburg.
