Delivering Great Customer Experience in Life Sciences with Data, Analytics, and AI Automation
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Delivering Great Customer Experience in Life Sciences with Data, Analytics, and AI Automation

The pharmaceutical landscape is swiftly evolving, necessitating a continuous learning approach. The traditional physical interaction model is merging with digital engagement, requiring seamless integration between physical and digital touchpoints. While AI-driven customer experiences show promise, they bring complexities that challenge Life Sciences’ goal of becoming more customer-centric.

Key Challenges

  • Evolving Customer Segments
    Identifying customer segments and information needs remains a gap. Life Sciences can enhance customer understanding, improving the quality and speed of delivering relevant content to HCPs and patients.
  • Connectedness Across Touchpoints
    Life Sciences organizations need to enhance their ability to assess audience responsiveness, strategically leveraging this insight for a seamless, integrated customer journey across diverse touchpoints like online platforms, in-person interactions, and telemedicine.
  • Speed and Personalization
    Scaling capabilities for speedy and personalized content remains an area of opportunity.

Implementing a test-and-learn capability, with MLOps as the critical link, in using data, technology and AI effectively can help mitigate several challenges in the Life Sciences Customer Experience (CX):

  • Continuous Testing and Learning
    MLOps refines communication strategies by analyzing vast data and understanding content resonance with specific audiences.
  • Fast Integration of Data
    MLOps ensures unified customer views by integrating data from various touchpoints, promoting a connected customer journey across channels.
  • Streamlined Processes
    Automation in MLOps embeds insights at decision points, enhancing speed and responsiveness in customer interactions.
  • Continuous Testing for Personalization
    MLOps finetunes personalization efforts, balancing customization while respecting privacy boundaries.

By implementing MLOps for test-and-learn capabilities, pharmaceutical companies can harness the power of data-driven insights to optimize their CX strategies. However, it’s crucial to approach this implementation with a focus on ethical data usage, transparency, and aligning strategies with regulatory compliance to ensure that the benefits of MLOps are maximized while maintaining trust and integrity in customer interactions.

From Possibility to Chaos: Several AIML Models Created, with Few Embedded in Decision-Making

In the dynamic realm of Life Sciences, the exploration of advanced analytics, AI, and now GenAI, for optimizing business strategies has reached unprecedented levels. Despite ingenuity in creating AI/ML models, only a few make it to production, influencing day-to-day operations.

  • The “Use and Throw” Approach
    Short-lived AI/ML models pile up in a “Use and Throw” cycle, wasting resources and stalling innovation. Rapid data shifts, coupled with a build-and-abandon mentality, leave promising models underutilized and hinder revenue potential. This inefficient experimentation creates a cluttered landscape, slowing down the deployment of valuable AI solutions.
  • Inherent Challenges in AI/ML Projects
    AI/ML projects encounter challenges stemming from unpredictable data patterns, dynamic business requirements like changing strategic objectives and evolving datasets, and common pitfalls such as the absence of standardized practices, poor scalability, and operational challenges. These factors contribute to the intricacies faced in the successful development and deployment of AI/ML solutions.
  • Navigating Daily Challenges: A Realistic Data Scientist’s Struggle
    Challenges emerge during the operational phase due to the absence of standardized practices, version control, model monitoring and many other factors. Poor deployment leads to extended periods of inaccurate predictions or biased predictions, defeating the overall purpose of model.
  • Operations Excellence is the Answer:
    Implementing Operations Excellence, mirroring DevOps’ impact in software engineering, is essential. MLOps (Machine Learning Operations) ensures a seamless process from model development to deployment, addressing inherent AI/ML project challenges.

This shift isn’t just a remedy for existing challenges; it’s a pathway to maximizing AI/ML capabilities, especially within the life sciences industry and beyond. MLOps is crucial to bring method to the madness.

From Chaos to Value Realization: Unlocking High Returns on AIML Investments Through MLOps

MLOps, or Machine Learning Operations, is a strategic approach enhancing the entire ML lifecycle, ensuring efficiency, standardization, and automation. It’s the bridge that spans the gap between the chaos of model development and the realization of tangible value for the organization.

Assessing the Maturity Level of Your Management of the AI/ML Solution Portfolio

Assessing the maturity level of your AI/ML solution portfolio management is crucial for successful MLOps implementation. Whether handling ML models manually, incorporating DevOps without MLOps, or partially integrating MLOps components, understanding the present maturity level is key.

In initiating the MLOps journey, a well-defined vision serves as the guiding North Star. Establish goals that align seamlessly with organizational objectives and technology initiatives, ensuring strategic coherence and targeted actions for fulfilment of the objective set.

Key Components of MLOps

After establishing the North Star or desired state, the next step is defining the components necessary within the MLOps framework. Seamless transition of ML models from development to deployment requires the integration of various pivotal components. These include IaaC, Version Control, CI/CD, Feature Store, Model Monitoring, and Feedback Mechanism.

MLOps in Action: Unveiling the Value

Leading companies in the pharmaceutical space have embraced MLOps to navigate complexities, ensuring precision and maximizing the impact of their tactics. The table below explores how MLOps components contribute to overcoming challenges and realizing business benefits in the context of pharmaceutical marketing.

 

Existing State

MLOps Components

Business Benefits

Inefficient Run: Data Scientists spend significant hours on execution

Automated Pipelines/IaaC

Efficiency Gains: Up to ~60% reduction in run time

Automation Hurdles: Lack of code versioning and refactoring practices hinder automation

Code Versioning/Refactoring

Proper packaging and reusability of code

Recreating pipelines for managing and serving features even for similar models

Feature Store

Accelerated time-to-market for new models, up to ~20% time reduction for deploying similar models

Minimal Monitoring: Lack of components leads to unnoticed data and model drift

Model Monitoring

Robust AI/ML Portfolio: Models perform as expected in real-world scenarios

Manual Deployment: Dependence on manual steps for model deployment

CI/CD, Automated Deployment

Streamlined Operations: Increased efficiency through automated deployment processes

 

In the dynamic landscape of AI and ML, Course5 Intelligence stands as your strategic partner, offering expertise and solutions to navigate the complexities and unlock the full potential of your AI/ML investments. Embrace the power of MLOps and embark on a transformative journey with Course 5 Intelligence. Partner with us and, together, let’s turn possibilities into reality.

Author: Kamal Kasi
Contributions by: Shubham Kansal and Harshit Sundriyal

Winning The Customer Experience (Cx) Battle In Life Sciences: The Role of AI Automation with MLOps as the Key Enabler
The authors of this paper define,

  • A roadmap to optimize AI/ML investments for Operational Excellence in Life Sciences
  • Strategies to optimize the Life Sciences Customer Experience – across physical and digital touchpoints – using emerging patient and industry data, advanced analytics, AI, and MLOps
  • Ways to use AI automation to reduce wasted investments, tap new opportunities, and drive growth and revenue impact

Kamal Kasi

Kamal Kasi

Kamal Kasi is a Senior Client Partner in the Life Sciences Data & Analytics practice. Over the last 19 years, he has advised global Life...

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