1. Drug development – R&D
The development of a single drug costs an average of $ 2.6 billion and up to 14 years, according to estimates by Tufts University and the US FDA. At the same time, not all drugs successfully enter the market.
The complexity of R&D is that researchers have to study tens of thousands of molecules in order to find “candidates” for inclusion in a drug by trial and error, then thoroughly test them and find the ratio of components that is effective in treating a specific disease. Analyzing all chemical compositions is a complex and costly process, even for large companies.
To optimize the search for the ideal drug formula, pharmaceutical companies are implementing text and natural language processing mechanisms based on machine learning for analyzing biomedical data arrays. The AI system in the laboratory conducts a deep semantic analysis of information from specialized databases and open sources on the Internet, and, based on the processed data, evaluates the potential effectiveness of each candidate for drugs (for example, an innovative molecule). Moreover, for AI tools it does not matter whether it is genomic data, speeches and research of analysts, or scientific articles – any type of unstructured data is processed automatically, without human intervention.
For example, the pharmaceutical manufacturer AstraZeneca signed a cooperation agreement with Berg. Berg’s technologies will help pharmaceutical companies identify “drug candidates”: the laboratory will analyze all fragments of compounds discovered by researchers and select from them potentially effective ones.
AI is also useful in the later stages of drug development: for example, after the approval of the laboratory, the drug will have to go through several more phases of clinical trials. In-depth analysis of patient data helps to select the most suitable people for research – for this, huge amounts of data about potential candidates and their state of health are analyzed. Biotech scientists together with Artificial intelligence experts can do this quickly and, as a result, compile a patient profile for whom the use of the new drug will be optimal.
2. Relationships with partners and customers
The work of medical representatives and KAMs of pharmaceutical companies, which directly interact with partners and clients (doctors, hospitals, pharmacy chains) in the regions, is one of the largest expenditure items for manufacturers. At the same time, the effectiveness of each such “live” meeting is far from obvious: a representative usually has only 3-5 minutes to demonstrate one product – this is not enough to promote the drug. Virtual AI and machine learning certification in Manila assistants based on “smart” algorithms come to the rescue.
AI algorithms can process massive training data about each client, provide a medical representative with the necessary marketing materials based on analysis, and even predict the outcome of an appointment. Thus, artificial intelligence tools are able to tell the medical representative what next step in relation to the client will be optimal for increasing sales in the future, and to build the optimal meeting agenda in advance. Moreover, today there are systems that analyze the results of previous meetings and themselves prompt the medical representative with whom and when to meet.
Most often, such AI assistants are embedded in CRM systems – specialized IT solutions for managing the work of a company’s medical representatives, as well as for coordinating marketing and sales.
3. Sales analytics and marketing
Perhaps the most interesting area for the application of AI in the pharmaceutical industry, which is actively developing, including in our company, is marketing management and related sales analysis. Unlike retail and retail trade, where voice and other virtual assistants are in demand, first of all, for direct communication with customers, there is a noticeable demand for data mining technologies in the pharmaceutical industry.
Forecasting customer demand. Forecasting customer demand allows you to form realistic plans and sales strategies, and based on the information received, build optimal plans for the production of products. To make an accurate forecast, the system analyzes huge amounts of information about sales areas, economic and demographic situation,s and other indicators. As a result, the efficiency of planning the product range of outlets is significantly increased, as well as over-stock and out-of-stock situations are minimized.
Marketing performance management. Data mining helps to determine the effectiveness of marketing channels, assess their contribution to sales growth, and based on this data reallocate marketing budgets. Moreover, the cost of contact is constantly decreasing, since the system is constantly learning and with each use, it more accurately prompts through which channels and which messages to convey to each specific partner or client.
Preventing customer churn. Smart analytics allows you to predict the volume of customer churn based on the analysis of the entire array of customer data. AI helps to assess the feasibility of retaining or losing each individual client and to target work with potentially useful clients for the company.
Such solutions help to build the most optimal marketing strategy and instantly adjust it with the introduction of new data into the system.
4. Manufacturing control
The main tasks that artificial intelligence solves for production departments are related to the optimization of the production process. For example, AI-based systems can predict the duration of the production cycle of a particular product and, depending on this forecast, build an optimal plan for loading production lines.
Other AI tasks in manufacturing relate to identifying critical production areas, finding defective goods, predicting downtime, equipment breakdowns, and planning repairs and maintenance of the fleet. For example, the AI solution. Equipment Fault Forecast is able, due to accurate planning of maintenance and repair (maintenance and repairs), to reduce the cost of purchasing spare parts and materials for PPR – 4%, for their storage – up to 12%.
Artificial Intelligence helps to optimize warehouse activities to avoid overstocking, overproduction or shortages of goods, as well as find better design solutions and choose more “selling” packaging design. Bank of America Merrill Lynch estimates that 45% of production tasks by 2025 will go to robots and AI.
