Most pharmaceutical organizations do not struggle to explain what their AI programmes do. They struggle to explain, in financial terms, why those programmes deserve sustained investment.
That distinction matters more now than it did even a year ago.
In many pharma companies, the pattern is familiar. AI pilots generate interest. Teams produce compelling demos. Internal sponsors build momentum.
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In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Dan Pratl, founder and CEO of Quadron Inc, about a trust problem emerging as AI moves deeper into pharma: how do organisations preserve confidence when more work is generated, shaped and accelerated by machines?
There are millions of patients and professionals discussing different therapies, drugs, and diseases on various social media, forums, and other websites. In case of pharma companies, such discussions are a treasure trove of patient experience information that is not gained in clinical settings.The issue here is that the social listening technology that was used before wasn’t designed to deliver insights this complex and deep.
In this solo episode of Al For Pharma Growth, Dr Andree Bates explores why many pharma teams do not have an Al problem at all. They have a capability problem.
This infographic represents how AI in Drug Discovery transforms complex data into new medicines by identifying drug targets, discovering promising compounds, predicting safety, optimizing candidates, accelerating preclinical research, and supporting faster development decisions.
For many pharma and biotech leaders, that sounded like relief. In one sense, it is. The industry has been preparing against a difficult implementation path, with legal uncertainty, unfinished standards, unresolved questions about overlap with sectoral rules and uneven readiness across both companies and regulators. More time matters.
The pharmaceutical sector is currently witnessing a revolution as the evolution in technology transforms the way businesses perform research, development, production, marketing, and delivery of healthcare solutions. In such an environment, comprehending the significance of digitalization in the pharmaceutical industry becomes crucial for those businesses looking to enhance efficiency, cater to changing customer demands, and sustain their growth.
Healthcare is evolving quickly as technology transforms how patients receive medical care, physicians make decisions, and pharma companies develop solutions. Large tech companies, including Google, Amazon, Microsoft, Apple, and IBM, have started entering the healthcare industry.
In this episode of AI For Pharma Growth, Dr Andree Bates speaks with Bret Bostwick from Breyer Capital about the rare path from genetics, clinical medicine and drug development into venture capital, and what that perspective reveals about the future of healthcare innovation.
Healthcare is currently experiencing one of its biggest transformations in history, with artificial intelligence in pharma and biotech being the driving force behind this revolution. The impact of this technology spans across the entire healthcare industry from drug discovery to improved patient treatment and care.
In pharma, AI governance sits at the intersection of enterprise technology risk, regulated-process controls, and emerging AI law. The question is no longer just whether a tool is enterprise-approved. It is whether the specific use of that tool is understood, classified, governed, documented, and, where needed, validated for the context in which it is being used.
For many decades the pharmaceutical industry has always struggled with one major issue: the development of drugs takes years of hard work and lots of money to develop a drug, and the majority of attempts fail even before becoming available to patients. However, all these facts have become a norm, “the cost of doing business,” in the industry for many decades.
This infographic showcases how Generative AI is driving smarter drug innovation across the pharmaceutical industry. It highlights Eularis' expertise in accelerating drug discovery, improving clinical development, strengthening AI governance, and enabling future-ready growth through strategic, trusted, and sustainable AI solutions.
For most of modern medicine, biological data has been built around population averages: what people like you might experience, rather than what you personally will. In this episode, Dr Andree Bates speaks with Ken Clark, co-founder and CEO of Enigma Genetics, about moving from population models to personal intelligence, and what it could mean for patients, pharma and the future of biological data.
Functional leaders in regulatory affairs, medical affairs, commercial, and market access have now seen enough AI demonstrations to understand what is possible. They have watched an agent draft a literature summary in a few minutes. They have seen AI support medical information triage, assemble competitive landscapes and pre-populate sections of regulatory submissions.
The goal of all medical practitioners has always been precision. However, up till recently, precision in medicine was achieved through larger scans, more accurate imaging, and medicines that were customized for specific issues. The future of precision medicine lies in technology that is very tiny.
Have you ever thought about tiny robots moving inside your body to identify and combat an illness at its source? Although it sounds like something from a science fiction film, nanobots in medicine are actually making a swift transition from study paper pages to actual healthcare. They are also bringing about an incredible change.
In pharma, the biggest deals are increasingly won or lost before a formal process even begins. In this episode, Dr Andree Bates interviews Andrey Doronichev, co-founder and CEO of Bioptic, about the “intelligence gap” in business development, licensing, and corporate strategy, and why many companies are losing opportunities before they even know they’re competing.
The following infographic shows how AI in Life Sciences is helping accelerate research and development through smarter data analytics, faster drug discovery, better clinical development, informed decision-making, and forward-thinking innovations. Eularis assists life sciences companies use AI to gain growth and transformation.
Enrolment isn’t a “soft” operational risk in drug development – it’s the hard constraint that quietly determines whether a promising asset reaches patients on time or bleeds value in limbo. In today’s clinical landscape, recruitment timelines are missed so routinely that it has become the norm rather than the exception, with widely cited analyses indicating that roughly 80% of trials experience delays driven by enrolment.
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