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Hyper-Automated AI in Life Sciences: Top 5 Insights that Will Shape the Future

26 Jun 2024
Automation is no longer the benchmark; life sciences companies are making a move toward hyper-automation. It comes as no surprise that hyper-automation was identified as one of the top 10 strategic technology trends by Gartner. But is hyper-automation just another tech buzzword or, a game-changing ecosystem that can accelerate and transform the life sciences value chain?
At the recent PMSA Annual Conference 2024, Vikas Mahajan from Indegene and Ravi Shankar from Novartis delved into this emerging trend during their presentation. Here are the top five insights from their talk on how companies can harness hyper-automation to drive significant business outcomes.
What is Hyper-automated AI (HAI)?
Before we get started, let’s understand the building blocks of Hyper-automated AI. Think of Hyper-automated AI as an interconnected framework that leverages the best of artificial intelligence (AI), machine learning (ML), and automation technologies along with Robotic Process Automation (RPA) and Natural Language Query (NLQ) capabilities to automate and accelerate the processes of data extraction, data quality management, data transformation, and action-oriented insights and recommendations generation—all through a single platform.  
HAI not only ensures a single source of truth but also empowers end-users to generate, visualize, and interact with data without hassle.
You can discover more about the Hyper-automated AI ecosystem here.
Top 5 takeaways
Here is a quick summary of the key talking points.
1. Do not view challenges from a singular lens
Notably, 48% of life sciences leaders face challenges in utilizing data due to its incompleteness, complex formats, and fragmentation across sources. This warrants an approach taking into account unique concerns of each persona and developing a solution that is beneficial for both technical and business users.
IT Operations
Diverse, fragmented data sources resulting in data connectivity issues
Complex and siloed IT landscape
Higher operational costs due to manual activities
Business user
Longer data analysis and interpretation processes delaying insights due to inadequate data structuring
Lack of technical proficiency limiting the use of self-service platforms
Data management
Inconsistent data quality and standardization impacting accuracy and reliability
Unscalable systems and multiple handshakes resulting in data integrity issues and operational inefficiencies
So, how can life sciences companies address these heterogenous data challenges and get to insights sooner with just one platform? This leads us to our next takeaway.
2. Make an informed move from an automated framework to an end-to-end hyper-automated ecosystem
Life sciences companies are already investing in an automated framework. According to our recent commercial IT report, 56% of Commercial IT leaders in life sciences have directed their investments towards automation, and 62% in AI/ML applications. However, in a business landscape where an enormous volume of data is produced every minute and action-oriented insights and recommendations are needed at speed and at scale, hyper-automation holds the key—it is not an option anymore but a strategic and competitive imperative.
3. Supercharge your value chain with Hyper-automated AI
Hyper-automated AI can address numerous use cases across the life sciences value chain, accelerating and transforming processes that would otherwise take much longer. In short, opportunities are limitless.
Commercialization

Access personalized recommendations on HCP experience, market segmentation, scenario planning, and intelligent forecasting

Patient support services
Predict patient risks, remotely diagnose patients, analyze adverse effects, match patients with precise drugs, and build personalized patient support programs
Clinical trial design
Optimize trial designs and predict outcomes. Deliver quick insights into markets and competition by synthesizing real-world data (RWD)
Patient insights
Access clinically-rich patient insights such as medical adherence, brand switching patterns, and more at an accelerated pace
Drug supply chain
Monitor inventory levels, anticipating demand, and proactively managing the supply chain
R&D
Sift through vast biomedical data for potential therapeutic targets, accelerating drug discovery and paving way for effective and personalized treatment
4. Enable faster decisions and robust business outcomes
By integrating hyper-automated AI, life sciences companies can expect accelerated insights generation, improved operational efficiency, optimized resource utilization, and reduced costs. But there’s more.
Process improvement and efficiency: Hyper-automated AI empowers real-time data analysis for swift decision-making by automating end-to-end data transformation processes in organizations.
Cost savings: Automation of routine tasks decreases the need for manual labor, leading to cost savings in terms of operational expenses.
Self-serve/user-friendly: HAI makes it easier for business users to access and visualize insights effortlessly through easy-to-use, customizable dashboards.
Agility: It leverages AI/ML models for data-driven decision-making, better anticipating and responding to market changes and customer dynamics.
Enhanced customer experience: It optimizes campaign and content strategies based on real-time data, enabling personalized experiences to patients, HCPs and payers.
Holistic view: HAI provides a single platform for consolidating, accessing, and viewing actionable insights and next-best action recommendations.
5. Utilize ready-to-use implementation checklist
To guide your initial steps for setting up a robust tech stack for HAI, here’s a quick checklist to ensure that the hyper-automated AI system grows with your business needs.
Emphasize interoperability
Opt for technologies that facilitate smooth data flow across various platforms
Identify skill gaps within the organization that might impede the integration of the AI tech stack
Consider the scalability of tech stack to accommodate future business needs
Select use cases based on their potential ROI, consider factors as user adoption, cost savings and efficiency gains
Steer clear of rigid automation processes that hinder the ability to address issues like anomalies promptly
Incorporate change management strategies that involve comprehensive training, introducing new technologies to complement existing skills, covering technology gaps, and others
Embrace an incremental approach over a big bang approach
Be cautious about automating processes that are easy to automate but may require an extensive change management process
Prioritize end user needs and skills when designing automated solutions
Conclusion
With transformative potential of hyper-automated AI across the life sciences value chain, it is clear why industry is making strides beyond automation. From drug discovery to supply chain, the use cases are as varied as they are impactful. For life sciences companies, poised to stay at the forefront of innovation and maintaining competitive edge and relevancy: it is time to embrace the future of hyper-automated AI.

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