We caught up with Siamak Baharloo - the CEO of Labviva, an AI-powered e-procurement solution for supply chain transparency. With a PhD from UCSF in genetics and over 20 years of experience in sales and operations, Siamak has dedicated his career to improving the efficiencies and economies of science.
Life sciences is a traditionally conservative industry. How progressive has it been in applying technology automation?
The life sciences industry has made significant strides in embracing automated technologies. This shift is driven by the need to enhance efficiency, reduce costs, and accelerate R&D. In fact, laboratories are increasingly adopting automated systems for routine tasks such as sample prep, data analytics, and procurement.
For example, automation in laboratory workflows has allowed scientists to reduce manual errors, streamline operations, and increase throughput. Automated systems for liquid handling make sure measurements are precise and repeatable. Additionally, robotics in high-throughput screening has significantly accelerated drug discovery, allowing for a faster translation from bench to bedside.
Despite the industry’s cautious nature, the benefits of automation have been too significant to ignore, leading to increased adoption of these technologies.
How does AI implementation help automate processes in life sciences?
AI has been transformative in automating various processes within the life sciences, and this is just the beginning. In drug discovery, AI algorithms can analyze vast datasets to identify potential drug candidates much faster than traditional methods. Imagine sifting through millions of compounds to predict which ones will be most effective. AI-powered platforms can do this, significantly reducing the time and cost associated with the initial screening processes.
In clinical trials, AI can streamline patient recruitment by matching patients with suitable trials based on their medical history and genetic information. This expedites the recruitment process and ensures a higher likelihood of trial success by selecting the most appropriate candidates. AI also plays a crucial role in the future of personalized medicine, by analyzing patient historical data to tailor treatments to that specific individual. This historical data also becomes more accessible for population level studies to reveal previously unnoticed correlations and trends, driving future discoveries.
AI-powered tools can also enhance diagnostic accuracy by analyzing medical images and predicting disease outbreaks through epidemiological data analysis. Machine learning algorithms can detect patterns in medical images like x-rays, MRIs, and histology samples that might be missed by the human eye, leading to earlier and more accurate diagnoses. Predictive modeling using AI can help track and forecast the spread of infectious diseases, allowing for timely interventions and better resource allocation.
What is the benefit to scientists? Procurement professionals?
For scientists, AI and automation significantly reduce time spent on repetitive tasks, allowing them to focus on their core mission of research and innovation. Previously, scientists had to review various papers on multiple sites before finding what they needed for research, search multiple supplier websites to compare pricing and availability, and sift through pages to identify which chemicals were most suitable. With AI, scientists no longer need to take on the role of purchaser or inventory manager; they don’t need to stay on top of expiration dates or worry about compliance and making the economical choice: tasks that distract from their primary focus on R&D. AI-driven platforms can handle all of this by aggregating and tailoring scientific content, providing consolidated pricing and availability from all suppliers, and offering chemical catalogs with structural data all in one place. This shift enables scientists to restore their focus on research and experiments that open new avenues for exploration.
Procurement professionals benefit significantly from AI by optimizing supply chain management, predicting inventory needs, and automating procurement processes. By analyzing purchasing patterns, AI can forecast future requirements, ensuring that labs are always stocked with necessary supplies without the risk of overstocking or shortages. This precision is particularly valuable to life sciences procurement, where materials may have limited shelf lives, require specific storage conditions, and occupy significant space. Additionally, AI-driven platforms can help reduce costs and achieve DEI spending goals by promoting purchases from diverse suppliers. These platforms can also support sustainability initiatives by highlighting environmentally friendly options and reducing waste through better inventory management. Ultimately, implementing AI in life sciences saves procurement professionals time, money and space, allowing them to focus on strategic sourcing, discover the latest innovations, and drive overall efficiency in the lab.
