Tag: life sciences

Navigating the Generative AI Conundrum in Life Sciences: Insights into Challenges, Implications, and an Adoption Roadmap for Commercial Technology Functions  

The life sciences industry can reap the many benefits of Generative Artificial Intelligence (GAI) by effectively overcoming challenges in this highly regulated industry to responsibly implement the technology. Discover key implications for technology players and a roadmap for enterprises to successfully adopt GAI for commercial functions.  

Help us learn more about the potential of gen AI in the life sciences commercial function by participating in this short survey and receive a complimentary summary of the survey findings.

In the first blog in this series, we explored Gen AI life sciences commercial use cases, shared industry leaders’ skeptical to optimistic perspectives on its potential, and uncovered new technology offerings. Read on for more insights into key risks, repercussions, and recommendations to adopt generative AI in life sciences.

“With great power comes great responsibility.” – Uncle Ben, Spiderman

Undoubtedly, Gen AI has massive potential to disrupt most processes and create new opportunities across industries, including the life sciences commercial function. But the highly regulated nature of this industry brings significant risks and challenges that will need to be overcome to adopt GAI at scale. Let’s explore this further in the illustration below:

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Risks and challenges associated with generative AI in life sciences

Key implications for the life sciences commercial technology ecosystem

“A journey of a thousand miles begins with a single step.” – Lao Tzu

While the Gen AI journey can appear long and daunting, commercial technology players may have a head start over their peers across the life sciences value chain. While certain use cases, such as personalized campaign generation and brand reputation monitoring, will require complex integrations and domain-specific development, other applications like content generation/analytics, market research, and autonomous customer support can be quickly implemented and brought to market.

Next, let’s take a look at six recommendations for life sciences technology providers to seize opportunities that GAI presents.

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Key Implications for life sciences commercial technology players
  1. Take a safety-first approach: First and foremost, commercial technology players need to address the safety aspects of Gen AI adoption and applications – from healthcare personnel/patient data safety and security to compliance with regulations and tackling ethical and legal risks. Providers that successfully address safety questions will instill trust and reliability with customers and gain a foot in the door to discuss and foster responsible Gen AI application across the commercial function
  1. Seize the opportunity to achieve domain specificity at scale: By combining the domain-data trained language models and large language models (LLMs), Gen AI provides a great opportunity for commercial technology players to offer domain specificity at scale across a wider range of solutions and areas. This integration enables the generation of more accurate, relevant, and specific outputs in the life sciences commercial function context, ensuring quicker model training, fine-tuning of responses, and domain-specific prompting
  1. Recognize that speed-to-market is essential: Technology providers must quickly identify, prioritize, and bring viable go-to-market opportunities and use cases to capture market attention, and, ultimately, the enterprise mindshare. While enterprises are still determining next steps with Gen AI, they are eager to learn more, explore potential use cases, and become better educated. Therefore, the velocity of go-to-market initiatives is immensely valuable
  1. Balance incremental and disruptive innovations: To succeed in the market, players will need to balance their bets between simpler quick-to-market propositions that augment existing capabilities and more strategic long-term opportunities that explore new segments, functionality, etc. With the abundance of possibilities, providers should carefully weigh options
  1. Partner with service providers: Service providers can be important allies in ensuring enterprise-wide acceptance and adoption of AI-enabled services. Technology players should look to forge strategic ties with service providers who need to be the flag bearers for technology modernization, data architecture, and process and change management initiatives
  1. Prepare to win the talent war: As demand for new skills (such as generative modeling, data engineering, and ethical AI) rises, the talent war is expected to get more vigorous. Players must proactively plan for strategic hiring and upskilling/cross-skilling initiatives

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Enterprises are still evaluating the Gen AI conundrum across the entire life sciences commercial function, including the risks, challenges, costs, return on investment (RoI), talent, and processes. Our five-step GAI tools adoption guide can help enterprises accelerate this process, as illustrated below:

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While Gen AI holds immense promise for transforming the life sciences commercial landscape, it comes with its fair share of challenges, including ethical considerations, data quality, interpretability, and integration hurdles that need to be addressed to ensure responsible and successful adoption.

Technology providers can proactively develop strategies and solutions to overcome these obstacles. By crafting a thoughtful roadmap, committing to ethical practices, and focusing on continuous learning and improvement, the life sciences commercial solutions supply ecosystem can harness the power of Gen AI to unlock new opportunities, enhance customer experiences, and drive sustainable industry growth. While the journey to adopt Gen AI may be complex, the rewards for successful navigation are boundless.

Help us as we research the possibilities of Gen AI in the life sciences commercial sector by taking part in this brief survey. As a token of appreciation, you will receive a complimentary summary of the survey results.

To discuss Gen AI in life sciences and its impact on the commercial technology landscape, contact Rohit K, Durga Ambati, Panini K.

Life Sciences Clinical Data and Analytics (D&A) Platforms PEAK Matrix® Assessment 2023

Life Sciences Clinical Data and Analytics (D&A) Platforms PEAK Matrix® Assessment

The pandemic accelerated the need to extract value from data as it led to a substantial increase in data generation from various clinical sources, characterized by high veracity, variety, and volume. As a result, there has been a notable surge in the adoption of clinical data and analytics platforms in clinical development. This has sparked considerable interest in assessing their impact on clinical trials, patient care, treatment outcomes, and health system efficiency. Currently, the industry faces a significant challenge in integrating complex clinical data sources, including Electronic Health Records (EHR), Electronic Medical Records (EMR), laboratory data, Clinical Trial Management Systems (CTMS), connected devices, and Real-world Data (RWD).

Clinical data and analytics platforms offer significant benefits in the realm of clinical development. These platforms enhance data management by facilitating centralized and standardized data collection from various sources, resulting in improved data quality and integrity. Real-time monitoring capabilities enable stakeholders to track trial progress, identify potential issues, and make informed decisions promptly. Advanced analytics tools help uncover patterns, trends, and correlations within the data, providing valuable insights for optimizing trial protocols and treatment strategies.

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What is in this PEAK Matrix® Report

In this report, we assess 14 clinical Data and Analytics (D&A) platform providers. The providers are positioned on Everest Group’s PEAK Matrix®, a composite index of a range of distinct metrics related to the providers’ capabilities and market impact. The study will enable buyers to choose the best-fit provider based on their sourcing considerations, while providers will be able to benchmark their performance against each other.

In this report, we:

  • Examine the provider landscape for clinical D&A platforms
  • Assess clinical D&A platform providers on several capabilities and market success-related dimensions

Scope:

  • Industry: life sciences

  • Geography: global

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What is the PEAK Matrix®?

The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.

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Decoding the Generative AI Buzz in Life Sciences | Blog

Advances in Generative Artificial Intelligence (GAI) have sparked interest in its potential to drive growth and innovation in the biopharma and medical devices industries. Despite challenges and regulations, the life sciences industry is actively exploring GAI’s possibilities. Learn about the current state of Generative AI adoption, the supplier landscape, and proactive actions stakeholders should take to stay at the forefront of this technology. 

Contact us for questions or to have a discussion.

Life sciences, just like all other industries, is actively seeking to understand the intricacies of Generative AI (GAI) to gain a competitive edge. Enterprises in this industry are gearing up to embrace this generational shift in AI-enabled technology. Continue reading for the first part of our series on Generative AI adoption.

GAI is a type of machine learning that uses neural networks to learn patterns in the input data. Based on the input data it was trained on, GAI then generates the most appropriate response. GAI’s promise of delivering significant operational and tactical benefits in the short term and hyper-personalization and intelligent decision support over time is pushing life sciences enterprises to evaluate their Generative AI adoption readiness.

Although GAI can potentially disrupt the life sciences technology ecosystem in many significant ways, navigating the various risks and challenges that come with its implementation in this highly regulated industry will be critical.

Generative AI adoption outlook

Let’s take a look at the potential impact of GAI on the life sciences value chain:

  • Building on the solid AI foundations in place, GAI is expected to have the greatest impact on the areas of drug discovery and research, and sales and marketing
  • As organizations prioritize customer experience and new AI-based products emerge, the sales and marketing function is adopting GAI at a solid rate compared to the previous generation of AI applications
  • Due to its huge potential across the development lifecycle – from novel design ideation to final prototyping – GAI is expected to impact medical device design and development, in addition to impacting R&D and commercial functions
  • The biopharma and medical devices value chains have not yet fully embraced GAI because tools/GAI-based solutions trained on good quality LS-specific datasets are limited

The graphic below explores the short and long-term impact of GAI on various life sciences functions:

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GAI can be used to model certain aspects of drug discovery. Some prominent cases include Insilico Medicine’s GAI platform Chemistry42 which generates ideas for novel chemical compounds, and AstraZeneca’s transformer-based model MegaMolBART for reaction prediction, molecular optimization, and de novo molecule creation.

As life sciences enterprises look to unlock GAI’s true value, its various stakeholders have exciting opportunities to collaborate and form next-generation partnerships to successfully drive GAI implementation. The supply ecosystem across the GAI technology stack is illustrated below:

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The following stakeholders have key roles in GAI’s future:

  • Pharmaceutical and medical device enterprises: Enterprises such as Pfizer and Eli Lilly are partnering with independent software vendors to co-develop GAI solutions focused on enhanced efficiency, cost optimization, faster drug discovery, and remote patient monitoring while maintaining compliance with regulations
  • Independent Software Vendors (ISVs): These firms offer highly specialized (pharma value chain element and/or technology-specific) productized GAI-enabled tools and have a wealth of domain expertise (cheminformatics, bioinformatics, genomics, etc.). Some players include Iktos, Yseop, and Huma.AI
  • Hyperscalers: Cloud vendors have built AI/Machine Learning (ML)-specific modules for highly specialized functions such as omics analysis, high-performance computing (HPC) workload optimization, and knowledge graphs. Hyperscalers also offer a comprehensive suite of connectors and services to enable pharmaceutical companies to work with complex datasets
  • IT service providers: Leveraging industry expertise and domain knowledge, these providers offer consulting services, training, and support. They also develop and deploy GAI solutions to pharmaceutical enterprises and ISVs

These stakeholder groups are uniquely positioned to act as catalysts for Generative AI adoption. The exhibit below looks at the actions each group should take to move forward with GAI and the implications:

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Stay tuned for the second part of this blog series, where we will examine the most promising use cases in each area of the life sciences value chain, including their potential risks. We will also present a blueprint to successfully maximize the value of GAI-enabled solutions.

To discuss the future of Generative AI in life sciences, contact Kumar Dhwanit, or Rohit K.

Keep an eye out for our LinkedIn Live, The Possibilities for Generative AI in Sourcing.

Exploring the Potential of Generative AI in the Life Sciences Commercial Technology Landscape | Blog

As the life sciences industry shifts from a traditional model to a hybrid commercial model, Generative Artificial Intelligence (GAI) can potentially be a valuable tool for commercial functions ranging from customer support to lead generation. Read on to learn about the investments providers are making in GAI and leaders’ viewpoints when it comes to embracing this technology.     

Watch the webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences, to learn more about how enterprises can leverage GAI to unlock business value.

Introduction of Generative AI in the life sciences commercial function

While Artificial Intelligence tools increasingly are being used across all industries to revolutionize customer engagement and drive business success, life sciences enterprises historically have been slow to adopt emerging technologies.

However, the life sciences industry is evaluating the potential impact of GAI for commercial functions. Let’s explore whether it will reshape the commercial technology landscape or if GAI will succumb to the inherent risks and challenges present in the life sciences industry.

The latest evolution in AI technology, GAI can create unique content in the form of text, images, audio, graphics, code, and more, in response to given prompts within seconds. Its versatile applications have captured widespread attention, with venture capitalists investing US $2.6 billion in 110 GAI-focused startups in the US last year alone.

One of the noteworthy demonstrations of GAI is the Chat Generative Pre-Trained Transformer (ChatGPT), launched by OpenAI, which has gained significant attention and received substantial investments, including a recent funding round of US$ 2 billion in January 2023.

The pandemic has transformed the life sciences industry’s commercial model, shifting from traditional in-person interactions to a hybrid approach that combines traditional and digital channels.

To attain a competitive edge, life sciences enterprises are prioritizing delivering hyper-personalized experiences to the end user. As a result, enterprises are prioritizing investments in data analytics and AI tools and are seeking domain-centric solutions over industry-agnostic solutions.

The commercial function serves as the customer-facing function for enterprises by engaging customers across multiple channels, potentially making GAI a highly valuable tool across the commercial value chain with a diverse range of applications and use cases.

Potential use cases of Generative AI in the life sciences commercial function

Enterprises’ primary focus is optimizing their commercial processes by leveraging AI tools to analyze large amounts of data, identify patterns, and generate actionable insights for the commercial function, thus driving business growth. However, given the strict industry regulations, human intervention/oversight remains essential for the overall usage of GAI.

Exhibit 1 illustrates the key use cases that enterprises are striving to unlock in the near term.

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Voice of the life sciences industry in adopting GAI tools in the commercial function

As enterprises explore the range of capabilities offered by GAI, the industry reaction is mixed. Some leaders express skepticism about the accuracy of the information generated, while others are optimistic about leveraging GAI tools to revolutionize customer engagement.

Most leaders believe the current state of GAI tools is not fully ready for adoption. But they anticipate it will play a pivotal role in the future in delivering a seamless omnichannel experience (integrating tools on chat, email, social media channels, etc.) and delivering personalized content (relevant content to customer persona). This will make GAI an integral tool to ease the ongoing transition to a hybrid commercial model.

Exhibit 2 highlights the various perspectives shared by industry leaders about using GAI tools in the commercial function of the life sciences industry.

 

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Please note: As ChatGPT is the most utilized GAI tool, we observe enterprises discussing it more, and over time, we will see a clear distinction between GAI tools and ChatGPT.

Recent activities fostering the adoption of Generative AI in the life sciences

Despite concerns raised by industry leaders, GAI-based tools hold significant potential for delivering compelling commercial benefits in the near term. With continuous technological advancements and extensive training of tools on diverse and reliable life sciences data models, these tools can provide enhanced support for the commercial function.

Life science commercial technology providers and services providers have made the following investments to kick off their GAI journeys:

  • Salesforce launched Einstein GPT, a GAI Customer Relationship Management (CRM) technology that delivers AI-generated content across various interactions, including sales and marketing functions at large scale
  • Veeva has integrated a new AI tool specifically tailored for pharmaceutical sales representatives into its platform. This tool enables sales reps to obtain precise information about physicians or hospital practices, empowering them to personalize their pitches
  • Axtria integrated GPT models into its proprietary products. For example, Axtria DataMAx, a cloud-based commercial life sciences data management product, leverages GPT to drive efficiency and productivity
  • Doximity, an online networking service for medical professionals, introduced DocsGPT, which leverages GAI to streamline healthcare personnel’s communication by addressing product-related inquiries, aiming to reduce reliance on sales representatives
  • ZoomRx, a strategic healthcare consulting company, has developed the Ferma platform adopting GAI to analyze data from medical conferences, benefiting pharmaceutical companies’ medical affairs functions. Some of its clients include Amgen, AstraZeneca, Biogen, and Merck
  • Microsoft has integrated ChatGPT into Azure to develop new GAI-based tools. In this collaboration, Microsoft brings its expertise in areas such as natural language processing (NLP), computer vision, and reinforcement learning
  • Cognizant launched the Cognizant Neuro®️ AI platform to assist enterprises in deploying GAI at enterprise scale

Enterprises are widely adopting GAI tools with ongoing efforts to address and resolve concerns related to privacy, potential racial bias in training data, and regulatory compliance.

Stay tuned for the second part of this series on generative AI in life sciences, where we will delve into the challenges enterprises face in adopting GAI tools, analyze the supply landscape, and share Everest Group’s perspectives on the roadmap for tool adoption.

To discuss generative AI in life sciences, contact [email protected], [email protected], and [email protected].

Continue learning about GAI in the webinar, Welcoming the AI Summer: How Generative AI is Transforming Experiences.

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