How Artificial Intelligence (AI)-powered Platforms can drive higher returns in Pharma Competitive Intelligence (CI)

Pharma companies have traditionally leveraged artificial intelligence (AI) and machine learning for drug manufacturing, R&D processes in drug discovery and clinical trials. According to the Mckinsey Global Institute, AI, and machine learning in the MedTech and biopharma domain could generate nearly $100 billion revenue annually in the US healthcare system alone. But the rewards of implementing AI-powered platforms in pharma are not just limited to that. AI systems can oversee multiple data sets and derive insight on competitor analysis, drug marketing, drug availability and disease prediction. AI also helps drug companies track and predict epidemic outbreaks using latest information ranging from satellite pictures and social information on digital media.

AI-powered systems can handle massive amounts of drug marketing and competitor data and glean predictive insight for business. These can help devise and implement marketing processes effectively to get new drugs and products into the market. Drug companies can use important analysis methods such as customer affinity prediction and customer journey design to boost pharmaceutical sales. Other similar marketing methods powered by AI are patient switch propensity, KOL mapping and influence networks. Data-science algorithms use multivariate data analytics that is supported by past experiential evidence to sift through vast amounts of drug and clinical data. These algorithms help combine population-based treatment outcomes, individual patient clinical data, and medical history to create new drug combinations and alternative treatments. The returns on drug effectiveness and quality are very high when AI-powered data management systems are used.

Perhaps the most developed use of AI in CI research is in algorithms designed to read, combine, and interpret huge volumes of textual data. Research teams can save time and efficiently examine the enormous amounts of data from a large volume of research publications to validate and discard several hypotheses. AI powered systems can interpret handwritten notes, chemical combinations, test results and imaging scans to cross-reference and analyze important medical information. These AI systems can also extract intelligence after converting data in usable formats for analysis.

AI-powered predictive analytics can help CI analysts go beyond traditional search options and keyword monitoring for devising industry-leading strategies. For CI teams, advancements in technology and digital information mean mining and filtering data that can be noise and not quality information. AI-powered data mining systems provide analysts with tools to produce actionable contextual analysis. It gives them the weapon to discern important information from non-important ones especially when the data is gathered via social forums. By leveraging advanced AI data management tools, CI teams can save time and streamline workflows, connect data sources, and use information services to extract better information. Some of these platforms also provide a customized insights hub that enable all users in the organization to have access to dashboards, newsletters and reports, battle cards to take informed decisions for business.

Ai powered platforms can also help CI teams carry out information tagging and understand patterns in that to categorize myriad commercial and drug information. This content categorization can help drug companies save massive time and money in the long run. AI summarization technology can also summarize long pieces of content into easily digestible formats for business exigencies. Marketing teams and drug companies can gather important market data, cut considerable costs, and decrease discovery and marketing timelines of drugs with predictive CI platforms in AI.

Another high return driven by AI-powered platforms is via mining regulatory enforcement data in pharmaceuticals. AI tools offer an integrated set of data enforcement software that can mine a deluge of records such as drug observations and warning letter citations, regulatory body guidelines and market laws. Drug manufacturers can gain competitor insight via these AI tools to read and analyze regulatory environments when marketing and introducing drugs into the supply chain.

Competitive intelligence teams also can gain enough insight about supply chain fluctuations in the global market by utilizing unified and harmonized AI systems and processes. These tools enable them to support predictive decision-making and mitigate longstanding bottlenecks in the marketing and supply of drugs.

Ai-powered platforms can also drive high returns in strategy teams by streamlining data collection and referencing. These unify the collecting of data and sharing of that intelligence with other departments and drug companies to eliminate complex process challenges in drug data management. With common data models, codes, architecture and tooling, AI platforms unify the entire fragmented pharma business processes across departments and units to offer greater process intelligence all from different systems. Data application becomes easy and efficient, and insights better utilized in the long run.

Ai tools combine a multi-disciplinary approach to data integration across structured and unstructured competitor and market data. Knowledge collation via structured and unstructured public and commercial data sources is streamlined. CI teams can feed R&D insights needed on competition and local regulations, release of new drugs into the supply chain and market conditions in a particular regulatory regime. These market insights can help them understand strategic moves in a very competitive pharma landscape. They enable CI teams to maintain an economical advantage in a market that is flooded with varied products with a plethora of guidelines.

Another significant move that enables CI teams to stay ahead in the competition is to use AI software to get alerts on trends and developments in the drug market.

Despite the overwhelming advantage of AI and ML technologies in CI research and insight generation in pharma, there are obvious challenges that prevent CI teams from implementing a digitization drive. For example, the current patent laws prevent AI from being acknowledged as an inventor technology in pharma in many markets. AI inventions often are not protected as well as traditional inventions in CI. The data stores, including the stores of genetic code are also becoming larger and more complex. As a result, security risks are amplified necessitating governments to initiate new regulations in the drug market.

Another limit that AI faces are processing speeds that require pharma companies to keep their CI at pace with product research, development, and launch. Competitor analysis often cannot keep pace with these short timelines and scale up in a rapidly changing environment. The pace of the drug market globally demands machine learning intelligence for real-time consumer recommendations and data classification. But scaling up to demand is not possible always.

AI systems also need to be integrated into other core systems (e.g.: cloud) with personalized content available through multiple devices. As AI increases pipeline pace, such collaborative tools are becoming vital in companies. But are companies transforming fast enough? Innovative technologies feed off each other and growth of AI in CI research is interdependent with digitization of other facets of pharma operations. Success of AI tools in CI therefore is dependent on digitization of other processes too.

Nevertheless, innovation is critical in times of need and even if risks and uncertainties continue to mount in the post-pandemic world, the benefits of AI are undeniable. Though the technology is still emerging and application times for companies can be longer, when implemented correctly, AI will transform CI research and manage risks, exploit new opportunities, and deliver efficient results for all stakeholders globally.