The Importance of AI in Pharma
The pharmaceutical industry is currently witnessing a transformative era with the advent of artificial intelligence (AI). With its remarkable potential to revolutionize drug discovery, development, and delivery, AI is rapidly emerging as a powerful tool that promises to reshape the way medicines are developed and brought to patients.
The integration of AI technologies such as machine learning (including generative AI), natural language processing, and deep learning can help pharmaceutical companies to extract insights from vast amounts of data, identify new drug targets, and design more efficient clinical trials.
The impact of AI in the pharmaceutical industry cannot be overstated, as demonstrated by the escalating number of AI-driven projects being undertaken. However, a significant number of these initiatives encounter obstacles and fail to progress beyond the pilot stage. It is often humorously remarked that the pharmaceutical industry has more pilots than the airline industry.
One of the primary reasons behind these challenges lies in the approach taken by many pharmaceutical organizations when implementing AI. Often, AI is treated as an ad hoc pilot, or impressive AI tools are acquired without a thorough assessment of their alignment with the company’s strategic objectives and the goals of individual business units. Consequently, substantial financial resources are squandered on unsuccessful pilots and shiny new AI tools that fail to address critical organizational needs. This results in ineffectiveness and a lack of alignment with strategic goals.
To ensure the successful implementation of AI in the pharmaceutical industry, a clear and comprehensive strategic blueprint is indispensable. It examines corporate and business unit strategies, stakeholder needs, ecosystem impacts, and relevant technologies. By identifying optimal AI solutions and assessing them against various criteria, the strategic blueprint enhances operational efficiency, facilitates revenue generation, and ensures alignment with overarching strategic objectives.
This whitepaper shed light on the critical significance of a strategic blueprint in ensuring the successful implementation of AI in the pharmaceutical industry and provides insights on why to develop and execute such a blueprint.
The Critical Need for a Strategic AI Blueprint
The complexity of the pharma industry, coupled with the risks associated with AI implementation, underscores the need for a strategic blueprint. With a plethora of data pouring in from diverse sources like clinical trials, electronic health records, and genomic data, identifying relevant information for specific tasks can be challenging. The high dimensionality and heterogeneity of pharma data further exacerbate this challenge.
AI techniques such as machine learning and natural language processing can help manage the complexity of data and extract insights from it.
However, these techniques require a well-defined strategy to apply them appropriately.
1. Alignment with Organizational Strategy: A strategic blueprint ensures that the AI initiatives align with the overall organizational strategy. This alignment is critical for ensuring that the AI initiatives contribute to the overall goals and objectives of the company, avoiding isolated and disconnected efforts. By having a clear strategic blueprint, organizations can avoid the pitfalls of “technology for technology’s sake” and focus on driving tangible value and impactful outcomes
2. Stakeholder Alignment and Understanding their Pain Points: Successful AI implementation hinges on understanding the pain points of relevant stakeholders. Depending on the company, these stakeholders could be commercial teams, medical affairs, market access, regulatory, R&D, or the organization as a whole. A strategic blueprint provides a transparent roadmap for strategic AI initiatives, enabling alignment with stakeholder needs. This alignment ensures that AI initiatives meet the strategic objectives of the respective teams, fostering a cohesive and collaborative environment.
3. Mitigation of Risks and Challenges: Implementing AI in the pharma industry comes with inherent risks and challenges. These include data quality issues, lack of standardization, regulatory compliance, and ethical considerations. A strategic blueprint identifies these risks and challenges proactively, allowing organizations to develop mitigation strategies. By addressing these concerns early on, organizations can mitigate potential roadblocks, minimize delays, and pave the way for successful AI initiatives.
4. Maximizing project success: An alarming number of AI projects in pharma fail to deliver the desired outcomes. Gartner reported that only 15% of AI projects succeed. That is 85% that fail which is in line with what McKinsey reported. The reason for this is that the majority were done without a strategic AI blueprint in place. Many projects are conducted in an ad hoc and experimental manner, lacking the necessary strategic alignment. This leads to suboptimal impact and disappointing results. By adhering to a comprehensive strategic blueprint, pharma organizations can increase the chances of project success by planning for strategic alignment from the outset.
5. Efficient Resource Allocation: Implementing AI initiatives in the pharma industry demands significant investments of time, capital, and talent. A strategic blueprint provides a systematic analysis of resource requirements for various AI initiatives, aiding in prioritization and strategic fit assessment. By optimizing resource allocation, organizations can make informed decisions about where to invest their AI-related resources, ensuring maximum return on investment (ROI).
The potential benefits of artificial intelligence (AI) in the pharmaceutical industry are immense. McKinsey’s research highlights that AI has the potential to reduce drug development timelines by 25-30%, leading to faster delivery of innovative treatments to patients.
Moreover, AI can help cut clinical trial costs by up to 75% by enabling more efficient patient recruitment, optimizing trial design, and facilitating data analysis. AI can also write Clinical Study Reports that are 90% complete in less than an hour saving 6 weeks of team time. Moreover, the application of AI-driven predictive models and insights can enhance the success rate of clinical trials by up to 50%.
The potential is not limited to these areas. Eularis implements AI throughout the pharma value chain resulting in substantial productivity improvements. In the field of medical affairs, AI has enabled productivity gains of around 100% by significantly reducing the time spent on literature monitoring (up to 92%). Furthermore, AI can automate Global Value Dossiers, ensuring they are continuously updated to stay abreast of the latest scientific literature.
Pharmacovigilance is another area that benefits stronger from AI. In addition, we have automated many regulatory processes such as changes in guidance documents and more. AI applied in market access results in stronger value pricing, faster reimbursement, and automated insights.
Furthermore, AI in sales and marketing offers tangible benefits. For example, AI-driven landscape assessments and go-to-market strategies have led to measurable revenue generation increases of 10-37%. AI enables precise targeting of healthcare professionals (HCPs), facilitates the identification of patients with rare diseases, and assists in the discovery of emerging Key Opinion Leaders (KOLs), among other valuable applications.
Some of the sales and marketing applications can be found in this white paper.
https://www.linkedin.com/pulse/how-embed-artificial-intelligence-pharma-sales-marketing-bates/
Despite the immense potential, many pharmaceutical companies face challenges when it comes to identifying the AI projects that will effectively provide a strong impact and then scaling AI initiatives beyond pilot projects. According to the McKinsey report, a mere 13% of AI projects in healthcare advance beyond the testing phase, highlighting the necessity for strategic guidance and implementation frameworks. A survey conducted by Deloitte further unveils that merely 13% of pharmaceutical companies presently possess a comprehensive AI strategy, with a meager 29% allocating a dedicated budget for AI initiatives.
These statistics underscore the critical need for an AI strategic blueprint that not only guides AI implementation but also ensures scalability and long-term success. An AI blueprint offers a structured approach to identify the most suitable AI applications, allocate resources effectively, and align AI initiatives with corporate goals. It facilitates the integration of AI into various aspects of the pharmaceutical value chain, including research and development, manufacturing, supply chain management, marketing, and patient engagement.
The Benefits of a Strategic AI Blueprint
Implementing artificial intelligence (AI) in the pharmaceutical industry can offer significant benefits, such as improving decision-making, increasing efficiency, generating significantly greater revenue, reducing costs, increasing innovation, improving the HCP experience, and ultimately improving patient outcomes.
However, realizing these benefits requires a well-designed AI blueprint. that outlines the goals, objectives, and key performance indicators.
1. Alignment with Corporate Goals: By defining a clear strategy for AI implementation, pharmaceutical companies can ensure that their AI initiatives are focused on addressing the most important business challenges and opportunities.
One pressing challenge faced by many pharmaceutical companies is the patent cliff, where the expiration of patents on existing products can lead to a significant revenue decline. To mitigate this impact, companies must prioritize the efficient introduction of new drugs to the market and maximize revenues before patent expiration. Leveraging AI in this context becomes crucial. An AI strategic blueprint provides a strategic framework to identify areas where AI can be applied strategically.
For example, AI can analyze vast amounts of data to accelerate drug development timelines, identifying promising drug candidates and predicting their safety and efficacy. Moreover, AI enables real-time data analysis from clinical trials, empowering companies to make informed decisions about optimal patient cohorts and the development and commercialization strategies of their drugs.
2. Real-world impact: Ensuring that AI initiatives have a tangible business impact is a critical benefit of having an AI blueprint. Pharmaceutical companies invest significant resources into developing and implementing AI initiatives, and it is essential to ensure that these initiatives have a real-world impact on the business.
One way to ensure that AI initiatives have a tangible business impact is to identify where the corporate and business unit goals can leverage AI effectively to achieve them, as well as define clear objectives for each initiative, how to achieve those objectives leveraging AI as an enabler and to measure the outcomes against these objectives.
For example, a pharmaceutical company may implement an AI initiative to streamline the drug development process. The objective of the initiative may be to reduce the time it takes to bring a new drug to market by 30%. By defining clear objectives and measuring outcomes against these objectives, the company can ensure that the AI initiative has a tangible impact on the business.
There are several examples of pharmaceutical companies that have successfully implemented AI initiatives that have had a tangible business impact.
For instance, Pfizer implemented an AI system utilizing machine learning algorithms to analyze clinical trial data. This system enabled Pfizer to identify biomarkers that predict patient response to specific drugs, leading to more targeted therapies and cost and time savings in clinical trials.
Likewise, Novartis implemented an AI system powered by natural language processing algorithms to analyze patient data and identify adverse drug events. This proactive approach enabled Novartis to detect potential safety issues earlier in the drug development process, reducing the risk of trial failures and associated costs.
By ensuring that AI initiatives have a tangible business impact, pharmaceutical companies can realize significant benefits. A recent study by Accenture found that companies that invest in AI and other digital technologies could increase their revenue by up to 38% and their profitability by up to 45%. However, to unlock these benefits, it is essential to align AI initiatives with business goals, measure outcomes, and continually refine strategies based on data-driven insights.
3. Lower failure rate
Reducing the rate of AI projects failing to achieve their objectives is another critical benefit of having an AI strategic blueprint. Pharmaceutical companies invest a significant amount of time, money, and resources in AI projects. However, despite the potential benefits of AI, many projects fail to deliver the expected results. According to a survey by Gartner, up to 85% of AI projects fail to deliver their intended business benefits and only 15% of AI projects could be defined as a success.
An AI strategic blueprint ensures that AI projects are aligned with the overarching business goals of the pharmaceutical company. By clearly defining the objectives for each AI project, companies can establish a focused direction and avoid embarking on projects that lack strategic relevance. This alignment ensures that AI initiatives are purposeful, measurable, and contribute to the desired business outcomes.
Pharmaceutical companies that have embraced an AI strategic blueprint have experienced remarkable success. Merck, for instance, implemented an AI project utilizing machine learning algorithms to analyze chemical structures and predict viable drug candidates for clinical trials. This project successfully reduced the time and cost of drug discovery, resulting in the identification of several promising compounds.
Similarly, Sanofi implemented an AI project leveraging machine learning algorithms to analyze patient data and identify potential new drug targets. The project accelerated drug discovery efforts and yielded several successful drug candidates. These examples demonstrate how an AI strategic blueprint can lead to tangible success in pharmaceutical research and development.
By reducing the failure rate of AI projects, pharmaceutical companies can save significant resources and minimize the risk of costly setbacks. According to Deloitte, failed AI projects can result in substantial financial losses ranging from tens of thousands to millions of dollars. By leveraging an AI strategic blueprint, companies can minimize the risk of project failures and allocate resources more efficiently, ensuring optimal utilization of time, money, and talent.
4. Effective scalability
Ensuring that AI pilot projects are able to scale effectively is another important benefit of having an AI blueprint. Many pharmaceutical companies start with pilot projects to test the viability of AI solutions before implementing them across the organization. However, the challenge lies in scaling these pilot projects effectively to realize the full benefits of AI.
Having an AI strategic blueprint serves as a roadmap for the development and implementation of AI pilot projects. It outlines clear guidelines for testing, evaluation, and identification of successful projects that have the potential for effective scalability. This strategic approach enables pharmaceutical companies to leverage valuable insights gained from pilot projects and seamlessly expand AI initiatives throughout the organization.
There are several examples of pharmaceutical companies that have successfully scaled their AI pilot projects by implementing an AI blueprint. For example, Pfizer implemented an AI blueprint that included a roadmap for the development and scaling of AI initiatives. The blueprint helped the company to identify successful pilot projects and develop strategies for scaling them effectively.
Similarly, Sanofi implemented an AI blueprint that included comprehensive guidelines for testing and evaluation of pilot projects. Through the blueprint’s strategic approach, Sanofi successfully identified promising pilot projects and efficiently scaled them across the organization. This scalable implementation resulted in enhanced productivity, streamlined processes, and optimized business outcomes.
5. Proactive Approach
Traditionally, pharmaceutical companies have relied on reactive approaches to address business challenges, waiting until a problem arises before taking action. However, in today’s rapidly changing business environment, this approach is no longer sufficient.
An AI blueprint can help pharmaceutical companies to take a more proactive approach to emerging business challenges and opportunities. By providing a roadmap for the development and implementation of AI initiatives, the blueprint enables companies to identify potential challenges and opportunities and develop strategies to address them proactively.
One exemplary pharmaceutical company that has embraced this proactive approach is GlaxoSmithKline (GSK). GSK has integrated an AI-based system into its operations, which accurately predicts drug compound toxicity. This implementation has not only saved millions of dollars in development costs but has also reduced the risk of drug failures. By staying ahead of the curve in drug development, GSK has fortified its position as a leading pharmaceutical company.
Novartis presents another compelling example of the benefits of a proactive approach. The company has harnessed the power of an AI-powered drug discovery platform, enabling faster and more efficient identification of new drug candidates. By proactively embracing this technology, Novartis has maintained its leadership in the pharmaceutical industry and outpaced its competitors in drug discovery endeavors.
By adopting a proactive approach and integrating AI solutions, pharmaceutical companies can unlock significant benefits. They can uncover untapped opportunities, streamline processes, reduce costs, and enhance overall business performance. Furthermore, by leveraging AI to predict and identify potential issues, they can preemptively mitigate risks before they escalate into significant challenges.
6. Operational Efficiency
Optimizing business processes and operations through AI-driven automation and decision-making is another key benefit of having an AI blueprint in place for pharmaceutical companies. By harnessing the power of AI to automate tasks and enhance decision-making, companies can streamline their processes, lower costs, and elevate overall business performance.
Pfizer serves as a prime example of a pharmaceutical company that has effectively utilized AI to optimize its operations. By implementing an AI-driven digital assistant, Pfizer has automated mundane tasks such as data entry and document processing. This strategic adoption of AI has streamlined operations, reduced costs, and significantly enhanced the accuracy and efficiency of critical business processes.
In addition to automating routine tasks, AI can also be used to optimize decision-making processes. For example, AI algorithms can analyze data from clinical trials and other sources to identify patterns and insights that may not be apparent to human analysts. This can help pharmaceutical companies to make more informed decisions about drug development, clinical trials, and other business operations.
Another area where AI can be used to optimize business processes and operations is supply chain management. By using AI algorithms to analyze data on demand and supply patterns, pharmaceutical companies can optimize inventory management and reduce waste. This proactive approach yields significant cost savings and bolsters operational efficiency across the supply chain.
7. Enhanced Customer Experience
Delivering exceptional customer experiences and tailored services are paramount for pharmaceutical companies, and an AI strategic blueprint can be instrumental in achieving this goal. By harnessing the power of AI to analyze customer data, companies can gain valuable insights into customer preferences, needs, and behaviors. This enables them to provide personalized and relevant products and services, elevating the overall customer experience.
One example of a pharmaceutical company that has successfully leveraged AI to improve customer experience is Novartis. The company has implemented an AI-powered chatbot called “Ella” that provides personalized support and information to patients with multiple sclerosis. The chatbot uses natural language processing and machine learning algorithms to understand patients’ questions and provide relevant information and support. This has helped Novartis to improve the quality and consistency of customer interactions and services and enhance the overall patient experience.
Merck presents another compelling case by implementing a virtual assistant known as MIA (Merck Intelligent Assistant). MIA caters to the specific needs and preferences of healthcare professionals, providing personalized support and guidance. Through natural language processing and machine learning, MIA offers tailored recommendations and insights based on individual analysis. This personalized approach empowers healthcare professionals and contributes to a heightened customer experience.
In addition to chatbots, AI can also be used to personalize marketing and sales communications. For example, AI algorithms can analyze customer data to identify patterns and insights that can inform more effective marketing and sales strategies. This can lead to more personalized and relevant communications with customers, and ultimately, improve the overall customer experience.
AI also plays a pivotal role in expediting and improving diagnosis and treatment processes. By scrutinizing vast amounts of patient data, AI algorithms can identify patterns and insights that may that may not be apparent to human doctors. This capability leads to swifter and more accurate diagnoses, ultimately resulting in improved patient outcomes.
8. Cost Savings
By leveraging AI technology, companies can identify cost-saving opportunities across different operations, including supply chain, manufacturing, and clinical trials. AI algorithms can analyze data from various sources to identify inefficiencies and areas of improvement, enabling pharmaceutical companies to streamline their processes and minimize waste.
For example, a comprehensive study conducted by McKinsey & Company revealed that AI-powered predictive maintenance techniques can reduce maintenance costs by up to 25% while increasing machine uptime by as much as 20%. Additionally, AI-enabled inventory management systems can help companies avoid the pitfalls of overstocking or understocking, resulting in significant cost savings.
Moreover, AI technology can help companies optimize their resource management by predicting demand, identifying areas of over or under-staffing, and improving resource allocation.
According to a report by Accenture, by 2035, AI could potentially save the pharmaceutical industry up to $100 billion annually in the areas of R&D, manufacturing, and supply chain management.
9. Revenue Increases
In the fast-paced pharmaceutical industry, an AI strategic blueprint can be a game-changer, driving revenue growth and uncovering new business opportunities. By harnessing the power of AI-driven data analysis and customer insights, pharmaceutical companies can identify untapped revenue streams and capitalize on emerging trends.
AI technology plays a vital role in identifying market gaps and areas for drug development. Through sophisticated analysis of patient needs, healthcare trends, and other critical factors, AI can pinpoint opportunities for developing new drugs or repurposing existing ones for novel applications. This strategic approach enables companies to stay ahead of the curve and seize lucrative market opportunities.
Marketing and sales strategies can also be greatly enhanced through AI. By leveraging AI to analyze customer data and behavior, pharmaceutical companies can personalize their marketing messages and target their efforts with precision. This tailored approach resonates with customers, leading to increased sales and revenue growth.
Furthermore, AI empowers companies to optimize pricing strategies and revenue management. By analyzing market data and demand trends, AI algorithms provide valuable insights that enable companies to set competitive prices aligned with market demand. This data-driven approach ensures maximum revenue generation and a stronger market position.
10. Staff Satisfaction and Retention
By empowering employees with AI-driven tools and insights, they are able to perform their job roles more effectively and efficiently. This can lead to increased job satisfaction and motivation, as well as a sense of ownership and investment in the company’s success.
AI-enabled tools play a pivotal role in automating routine tasks and delivering real-time insights. By automating repetitive and mundane activities, employees are freed up to focus on more complex and intellectually stimulating work. This not only enhances their job satisfaction but also enables them to contribute more meaningfully to the company’s objectives. With access to real-time insights, employees can make informed decisions and take proactive measures, resulting in improved overall performance.
Moreover, AI technology supports the identification of areas for employee performance enhancement. By leveraging AI algorithms, companies can analyze individual performance data and provide personalized training and development opportunities tailored to each employee’s needs. This customized approach fosters continuous growth, enabling employees to upskill and excel in their roles.
Research has consistently shown that increased job satisfaction and engagement have a direct impact on reducing attrition rates. By investing in the satisfaction and development of their workforce, companies can mitigate the costly process of recruiting and training new employees. In fact, a study conducted by Gallup revealed that companies with highly engaged employees experienced a remarkably 21% higher profitability compared to those with low engagement levels.
11. Patient Outcomes
AI-powered healthcare solutions have revolutionized the way healthcare professionals analyze vast amounts of data, enabling them to make accurate diagnoses and informed treatment decisions. By leveraging AI technology, healthcare providers can unlock invaluable insights from patient records, medical histories, and clinical trials.
For instance, IBM Watson for Oncology uses AI to analyze a patient’s medical history and genetic data to provide oncologists with personalized treatment recommendations. A study conducted by Memorial Sloan Kettering Cancer Center found that Watson for Oncology’s recommendations aligned with those of oncologists in 96% of breast cancer cases.
Moreover, AI can aid in early detection and prevention of diseases. For example, deep learning algorithms can analyze medical images, such as X-rays and MRI scans, to detect abnormalities that might be missed by human radiologists. The use of AI in detecting diabetic retinopathy has shown promising results, with Google’s AI-based screening tool achieving a 94% accuracy rate in detecting the disease.
By improving patient outcomes, AI-driven healthcare can lead to reduced hospital readmissions, decreased healthcare costs, and improved patient satisfaction.
12. Competitive Advantage
By using AI to automate and optimize various processes, companies can differentiate themselves from competitors and become more innovative and efficient in their approach to drug development, manufacturing, and distribution. For example, AI-powered drug discovery platforms can help identify new and more effective treatments, giving companies an edge in the market.
AI can also help companies improve their supply chain management, reducing costs and increasing efficiency. This can lead to better delivery times, faster product development, and ultimately, greater customer satisfaction. Additionally, AI can enable pharmaceutical companies to better understand their customers’ needs and preferences, allowing them to tailor their products and services to meet those needs.
One example of a company that has successfully used AI to gain a competitive advantage is Novartis. The company has developed an AI-based platform called the Novartis AI Innovation Lab, which aims to accelerate drug discovery and development by using AI and machine learning algorithms to analyze large amounts of data. This has allowed Novartis to identify new drug targets and develop more effective treatments for various diseases.
Conclusion
In part 2 (click here to read now) we will continue this topic and delve into
The Risks of Implementing AI Solutions Without a Strategic Blueprint
Key Elements of a Strategic AI Blueprint
How Pharma Can Ensure AI is Employed Strategically
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At Eularis, we are here to ensure that AI and FutureTech underpins your pharma success in the way you anticipate it can, helping you achieve AI and FutureTech maturation and embedding it within your organisational DNA.
We are also the leaders in creating future-proof strategic AI blueprints for pharma and can guide you on your journey to creating real impact and success with AI and FutureTech in your discovery, R&D and throughout the biopharma value chain and help identify the optimal strategic approach that moves the needle. Our process ensures that you avoid bias as much as possible, and get through all the IT security, and legal and regulatory hurdles for implementing strategic AI in pharma that creates organizational impact. We also identify optimal vendors and are vendor-agnostic and platform-agnostic with a focus on ensuring you get the best solution to solve your specific strategic challenges. If you have a challenge and you believe there may be a way to solve it with AI but are not sure how, contact us for a strategic assessment.
See more about what we do in this area here.
For more information, contact Dr Andree Bates abates@eularis.com.
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