AI in pharma: The right approach for value creation and efficiency gains

The industry has long been a front-runner in applying the latest algorithms and statistical methods, particularly in research and development (R&D). However, AI solutions can also significantly improve efficiency, achieve time savings, and reduce costs in more administrative and daily operational areas. By embracing AI across all aspects of their business, pharmaceutical companies can enhance their pioneering R&D efforts while also streamlining routine operations, positioning themselves for even greater success. 

AI-suitable areas in the pharmaceutical industry include:

• R&D and early discovery: AI can be used for in silico compound screening, faster identification of patients for trials or scientific knowledge extraction from various sources. 

• Clinical development: A regulatory intelligence engine or a submission document writer can be implemented in this area. Compliance tracking, virtual patient models and medical and legal review assistance also have great potential for process automation using AI. 

• Operations and manufacturing: Quality control automation, augmented sourcing in manufacturing and predictive maintenance are examples of suitable AI applications in this field. 

• Commercial and go-to-market: AI can provide strategic decision support and market demand forecasting. Furthermore, content production can be automated, and AI can help with sentiment analysis and customer insights. 

• Medical affairs: AI can effectively handle medical inquiry responses and assist with drug information dissemination, patient intake, and patient adherence programs. 

• Company-internal support activities: As in many other industries, AI is well on the way to becoming a permanent fixture in billing, contract management, training and software development. HR processes and other administrative tasks can also be very well supported by AI. 

AI use-case approach as a key success factor 

Whatever area you are planning to use artificial intelligence in: The approach to the topic is crucial for success, so that AI really has the desired effect. The first step should always be self-reflection, whereby the following questions should be asked:

• Business strategy: How can AI support our business strategy and objectives? Do we have the necessary infrastructure? 

• Value levers: What value do we expect primarily? Cost reduction, efficiency improvement or new product/service development? 

• Investment and ROI: What is our budget for AI initiatives? What kind of return do we expect in what timeframe? 

• Risk and compliance: What are the potential risks regarding compliance, privacy, or ethics? 

AI opportunities should then be identified more precisely:

• Task identification: What tasks are inefficient, repetitive, and low involvement? 

• Process improvement: Which processes could be improved? 

• Data-driven decisions: Which decisions do we currently take under high uncertainty and could be improved by the use of data? 

• Customer experience: Can we serve our customers faster, better or more personalized? 

• Innovation: How could AI help in the creation of new products or services? 

From a use case longlist to AI value creation 

Once these questions have been answered, the next step is to create specific use cases for AI. To do this, it is recommended to first create an AI use-case longlist, based on the company value chain and the key business processes. In the next step, these use cases should be evaluated and prioritized based on feasibility, impact and availability of required data. Then, depending on the requirements for the prioritized use cases, an integrated data strategy and infrastructure plan should be developed to ensure robust data management, scalability, and seamless implementation. Finally, when it comes to implementation, it is important to create value with AI in a timely manner. The aim should be to implement use cases that have an immediate or at least medium-term impact on value creation. Once these use cases have proven themselves, the iteration and improvement of existing use cases is on the agenda, as is the prioritization of cases at the next maturity level. 

Examples of AI use cases

The challenge: streamlining the medical-legal review process 

AI can successfully support in navigating one of pharma's biggest challenges: medical-legal reviews (MLR) towards drug authorization. Generative-AI-based MLR solutions can condense the process to trim costs and facilitate quicker launches. As pharma companies need to strictly adhere to regulatory authorities such as the European Medicines Authority (EMA), and European legislation requires that companies bear most of the costs of the development and approval process, there is a great need for cost-efficient solutions. This is all the more true as drug authorization involves many feedback and communication loops with health authorities and is therefore very time-consuming. 
 

The solution: AI support for the creation of documents 

AI can help here in several ways, for example through semi-automated document creation: ready-made drafts of certain documents can be created by compiling data from different sources automatically into the required format. AI can also ensure that most of the advertising and promotional materials are accurate and already comply with regulatory requirements within the first submission by learning from old successful proposals and pre-checks. This means a well-thought-out AI solution reduces the cost of drug approval, shortens the time to approval and needs fewer communication loops.
 

The challenge: inefficient sales process 

AI can revolutionize the way pharmaceutical sales are conducted by making every interaction more personalized, compliant, and efficient, ultimately driving higher sales performance and better customer satisfaction. So far, data overload from multiple sources makes it difficult for sales agents to extract actionable insights – and this is true not only for the pharma industry. Communication often is inefficient as sales agents struggle with crafting timely and contextually appropriate responses due to increasing volume and customer needs. Furthermore, training and onboarding take significant time and resources.
 

The solution: AI assistant for sales in pharma

This is where AI can ensure context-aware communication. Specifically, a sentiment analysis can be performed to gauge the tone and emotion of communications and to give suggestions on how to adjust the tone in e-mail responses. Conversation can be tailored to each client based on previous interactions and profile-specific information. This enables a higher level of outreach without sacrificing the quality of customer relationship. Even more: AI-supported communication can lead to increased conversion rates.

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