From Discovery to Delivery: Transforming the Life Sciences Value Chain
adaptable applications that operationalize AI across the
product lifecycle, from research to commercialization.
How life sciences organizations use AgilePoint to speed innovation
Accelerating Drug Discovery
Deterministic:
Streamline data management and analysis workflows to support high-throughput screening and lead optimization. Implement standardized protocols for experiment design, data capture, and reporting to ensure reproducibility and regulatory compliance.
Non-Deterministic:
Dynamically adjust drug candidate screening using AI, overcoming limitations of traditional methods that miss complex interactions, while supporting human-in-the-loop decisions for novel designs where rule-based systems fall short.
Enhancing Clinical Trial Efficiency
Deterministic:
Automate patient recruitment and enrollment processes to reduce trial timelines and costs. Implement digital platforms for remote patient monitoring and data collection, ensuring data quality and integrity.
Non-Deterministic:
Adaptively modify trial designs with AI algorithms, addressing shortcomings of fixed structures that can't respond to real-time data, while enabling expert intervention for complex safety signals beyond predefined parameters.
Optimizing Manufacturing and Supply Chain
Deterministic:
Establish standardized processes for inventory visibility and order fulfillment across channels. Implement rule-based systems for routing orders to the most appropriate fulfillment location based on predefined criteria.
Non-Deterministic:
Flexibly recalibrate production and inventory using AI-powered forecasting, surpassing traditional methods that struggle with volatility, while supporting human decisions for critical maintenance where scheduled interventions fail.
Personalizing Patient Engagement
Deterministic:
Develop patient portals and mobile apps to provide personalized education and support. Implement standardized communication protocols to ensure consistent and compliant interactions with patients.
Non-Deterministic:
Intelligently evolve patient support using AI chatbots and machine learning, addressing inadequacies of one-size-fits-all models, while enabling healthcare provider intervention for complex scenarios beyond automated capabilities.
Enhancing Commercial Effectiveness
Deterministic:
Implement customer relationship management (CRM) systems to track and manage healthcare provider interactions. Establish standardized processes for sales force training, territory alignment, and performance tracking.
Non-Deterministic:
Rapidly adjust customer strategies using AI-driven analytics, overcoming static CRM limitations, while supporting human decisions for complex relationships where predefined processes fall short.
Strengthening Regulatory Compliance
Deterministic:
Automate regulatory document management and submission processes to reduce errors and improve efficiency. Implement standardized workflows for adverse event reporting and post-market surveillance.
Non-Deterministic:
Seamlessly update compliance monitoring using AI and NLP, addressing manual review shortcomings, while enabling expert analysis for complex scenarios where rule-based systems fail to interpret nuanced changes.
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