How Pharma can Use AI for Innovation

I've presented our AI Integration Framework -- My Work | "With Me" Work | "For Me" Work -- a number of times recently and see it being an unlock in helping anyone, in any role, imagine how to partner with a digital collaborator. Having just wrapped up a call about bringing #AI to research scientists in pharma, here is output my #AIIntegration Analyzer generated right on the call as the #AIMap for Pharma Research Scientists. ### Role Overview - Pharmaceutical scientists in a collaborative research environment aim to design, conduct, and interpret experiments to discover and optimize new drugs. Their work spans molecular modeling, clinical trial design, lab testing, and regulatory strategy. AI presents transformative opportunities to speed up data analysis, simulate outcomes, and support complex decision-making while preserving human-led insight and ethical judgment. "My Work" – Human Exclusive Tasks Ethical Oversight of Trials: Interpreting ethical dilemmas in clinical trial design or patient treatment requires empathy, context sensitivity, and moral reasoning. Creative Hypothesis Generation: Scientists generate novel hypotheses based on gaps, intuition, and pattern-breaking thinking—something AI still cannot replicate well. Stakeholder Collaboration and Communication: Presenting findings to regulators, peers, or funding agencies demands persuasion, contextual framing, and relationship-building. "With Me" Work – AI Collaboration Opportunities Drug Discovery Simulations: AI can simulate molecular interactions at scale, identifying potential candidates faster than traditional trial-and-error approaches. Scientific Literature Review: AI tools can quickly summarize recent findings, highlight contradictions, and suggest areas of unexplored potential. Clinical Trial Design Optimization: AI can propose inclusion/exclusion criteria or simulate trial outcomes to help design better, more efficient studies. Data Visualization and Pattern Recognition: AI helps uncover trends across large datasets—gene expressions, patient responses, or assay results—guiding deeper human analysis. Drafting Grant Proposals and Protocols: AI can create first drafts of documents, enabling scientists to focus on refining arguments and adding critical insights. "For Me" Work – AI Automation Potential Data Entry and Preprocessing: Cleaning, labeling, and structuring lab data for analysis is time-consuming and error-prone—perfect for automation. Routine Report Generation: Weekly experiment summaries or compliance documentation can be automated with templates and data inputs. Lab Inventory Monitoring: AI can track chemical usage, alert shortages, and auto-order supplies based on trends and usage patterns. Conclusion - In pharma research collaborations, AI is a force multiplier. Scientists remain essential for guiding research, making ethical judgments, and interpreting results, while AI can dramatically speed up analysis, documentation, and design iterations.

🚀 Drug launches are getting smarter. Here's how AI is transforming the game. The pharmaceutical industry faces unprecedented challenges today. Remote sales interactions are less effective than in-person meetings. Stakeholder expectations are evolving faster than ever. And the complexity of bringing new drugs to market continues to grow. But here's what's changing everything: AI. I've been working in pharma AI for over 20 years, and I've never seen such potential to revolutionize drug launches across four critical areas: ⚖️ Regulatory Compliance - 25% reduction in compliance time through AI automation - Enhanced pharmacovigilance that turns data overload into actionable insights - Streamlined processes for digital therapeutics and privacy regulations 💰 Market Access - Predictive pricing models that navigate complex reimbursement landscapes - Accelerated approval processes through intelligent stakeholder engagement - Better management of increasingly fragmented payer ecosystems 📊 Sales & Marketing - AI-powered market segmentation using data from prescribing patterns, CRM systems, and even social media behavior - Real-time message optimization that delivers the right information at the perfect moment - Personalised experiences that today's customers expect 🤝 Practitioner & Patient Engagement - Modern, streamlined interactions (no more stacks of business cards!) - Sophisticated chatbots for direct patient engagement - AI-powered apps that improve medication compliance The bottom line? Companies that embed AI early in their launch process see compounded benefits throughout the entire lifecycle. From market definition to Phase IV and beyond. Three keys to success: ⏰ Start early (delays cost millions) 💎 Leverage your data goldmine 🎯 Get your team excited about AI tools Drug launches may be complex, but that's exactly why AI is so powerful here. The companies that master this will have a massive competitive advantage. What's your experience with AI in pharma? Are you seeing these changes in your organization? #PharmaAI #DrugLaunch #ArtificialIntelligence #PharmaceuticalInnovation #DigitalTransformation

This white paper from Strategy& discusses the potential impact of artificial intelligence (AI) on the pharmaceutical industry, highlighting how AI can generate significant value across the pharma value chain. The key points are: AI Value Potential: •Pharma companies that fully industrialize AI use cases across their organizations could potentially double their operating profit by 2030. •The total additional operating profit is estimated at $254 billion worldwide by 2030, with $155 billion in the US and $33 billion in Europe. Value Distribution Across Functions: •Operations: 39% of the AI impact, focusing on production, material, and supply chain costs. •Research and Development (R&D): 26% of the impact, increasing efficiencies in developing new medicines. •Commercial: 24% of the impact, opening new ways of interaction. •Enabling functions: 11% of the impact, increasing speed and efficiency in supporting processes. AI in Research and Development: •AI can accelerate drug discovery, as demonstrated by platforms like Merck's AIDDISON and Chemprop. •Optimizing disease area focus (2-29% of overall AI potential) and AI-target discovery (5-14%). •Enhance clinical trials through synthetic data creation and patient experience improvement. AI in Operations: •Operations has the highest AI potential (39% of overall AI potential) due to its impact on a large part of pharma companies' costs. •Key use cases include automating catalog maintenance (3-5% of overall AI potential), AI indication of procurement risks (3-6%), and optimization of manufacturing scheduling (5-11%). •Quality management can benefit from AI through predictive quality management (5-13%). Steps to Realize AI Potential: •Assess and build organizational structures to execute priorities quickly. •Create processes for incubating innovation and set up dedicated teams for experimentation. •Implement top-down programs to address concerns and drive adoption as AI products are delivered. Future Outlook: •The impact of AI is expected to extend beyond 2030, particularly in R&D. •AI-enabled products and services may disrupt and complement current pharmaceutical business models. •Rise of personalized and digital healthcare, with blurred boundaries between prevention and treatment. Challenges and Considerations: •The adoption of AI in operations across industries remains relatively low due to data collection needs and infrastructure changes. The complexity of AI-powered R&D and the level of disruption it brings to internal processes remains high.  •Validating the impact of AI-discovered drugs will take time across pharmaceutical companies' portfolios. This suggests importance of strategic implementation and adaptation to leverage AI's capabilities in reshaping the future of healthcare and drug development. #artificialintelligence #r&d #drugdiscovery #innovation #operations #clinicaltrials #precisionmedicine Source: www.strategyand.pwc.com Disclaimer: The opinion are mine and not of employer's

8 Examples where Pharma is Using AI to Enhance Clinical Trials >> Pharma’s greatest use of AI is in drug development, but optimising clinical trials is an important and growing focus, as these recent examples illustrate 🔘 Bristol Myers Squibb extended its partnership with Medidata Solutions to enhance clinical trial management. BMS will adopt Medidata Clinical Data Studio and explore AI, advanced analytics, and data tools to optimize trial efficiency. This builds on their 2016 collaboration supporting cancer and other trials 🔘 Eisai US also partnered with Medidata Solutions on an AI-driven platform to streamline clinical trial management, reduce errors by 80%, and accelerate treatment development for cancer and Alzheimer's. The platform replaces spreadsheets with integrated data sources, aiming to improve patient experience and data accuracy 🔘 Eli Lilly and Company's Digital Health Hub in Singapore leverages AI tools like Magnol.AI to advance drug discovery for Alzheimer’s, autoimmune diseases, and cancer, while supporting Phase 1 clinical trials and real-time monitoring 🔘 AstraZeneca partnered with Immunai to enhance cancer drug trials using its AI platform, which maps the immune system. The collaboration leverages Immunai's machine learning and single-cell biology to improve clinical decision-making and accelerate immunotherapy development 🔘 AstraZeneca's new business Evinova launched, offering AI and health-tech solutions to enhance clinical trials, with support from Accenture and AWS 🔘 AbbVie collaborated with ConcertAI and Caris Life Sciences to enhance precision oncology by utilizing AI for clinical trials and patient enrollment. 🔘 Sanofi partnered with COTA to use real-world data and AI to enhance clinical trials for multiple myeloma, aiming to speed up the development and improve the design of future studies 🔘 Sanofi in collaboration with OpenAI and Formation Bio introduced Muse, an AI tool to streamline patient recruitment for clinical trials by identifying ideal profiles, generating materials, and ensuring regulatory compliance 👇Links to source articles in comments #DigitalHealth #Pharma #AI #ClinicalTrials

What if your next drug launch strategy didn’t just target doctors… but actually thought like them? Pharma has always been focused on what doctors do. The future however lies in understanding why they do it. A revolutionary AI model called “Centaur” is now simulating human cognition — and it could transform how we engage with HCPs, going forward. A recent study in Nature (https://lnkd.in/eTiAUFEj ) introduces a powerful foundation model “Centaur” designed to predict and capture human cognition across diverse experimental settings. By fine-tuning advanced language models on the extensive “Psych-101” dataset, researchers created a computational framework capable of accurately simulating human decision-making processes. Although this is still in early stages ( the model was trained on WEIRD - Western, Educated, Industrialized, Rich, Democratic population which is not representative , and it focussed only on learning and decision making aspects of cognition), it does hold significant implications for commercial efforts in Pharma , which largely is dependent on collating large amounts of historical data to predict future behavior of health care professionals. Combining cognitive modeling with predictive approach can help augment Personalized Communication Strategies for demand generating efforts : 1. Use cognitive modeling to predict specific healthcare provider preferences and biases, allowing for personalized marketing messages. 2. Utilize cognitive insights to craft precise, impactful messaging aligned closely with physicians’ decision-making frameworks. 3. Reduce generalized promotional tactics, focusing instead on tailored communications that resonate more deeply and effectively. This approach can also help in customizing educational programs for disease awareness through personalized scientific exchange of information : 1. Customized educational content informed by cognitive modeling, which addresses specific informational gaps and learning preferences of healthcare providers. 2. Use cognitive modeling insights to design clearer, evidence-based medical messaging strategies that effectively support clinical decision-making. As AI models begin to mirror not just what we do but how we think, the commercial engine in pharma must evolve — from reactive to predictive, and from generalized to precision influence. #Pharma #AIinHealthcare #CommercialStrategy #MedicalAffairs #CognitiveAI #DigitalTransformation #BehavioralScience