It is easy to forget that Artificial Intelligence, in the way it is understood and deployed today, has had a relatively short history in business. Early efforts in the late 1980s occurred as a few large government, academic and research led organizations began to develop and deploy expert systems. At this stage the main goal was to incorporate simple software driven algorithms and routines into repetitive processes (such as large and detailed or complex calculations) with the aim of saving time, money and improving quality of outcomes.
This foundational work was followed in the 1990’s and early 2000’s by AI being quickly adopted in many commercial industries such as manufacturing, retailing, transportation, finance, law, advertising, insurance, entertainment, education and even healthcare generally, and today is in almost every sector of the economy (often going unnoticed by many consumers).
As AI models have become more effective, and generative AI in particular has shown in very recent times, the effects of Artificial Intelligence will be massively magnified in the coming few years, as virtually every industry and sector transforms their core processes and business models to take advantage of machine learning on the path to what is Artificial Generalized Intelligence or AGI within the next 15-25 years (where human cognitive abilities can be built into in software so that, faced with an unfamiliar task, the AGI system could find a solution.
Applications to Biopharma
We already have much existing evidence that current iterations of AI have the potential to revolutionize the biopharma industry specifically by offering improved efficiency, accuracy, and speed across many processes, especially where many of these are either slow, expensive and labor-intensive. This means that all biopharma companies should be looking to both recruit and train internal talent that can spend more time and attention on the potential for AI and in establishing new partnership with AI knowledgeable and experienced AI companies.
Successful biopharma partnerships in the AI space are likely to have many early benefits of introducing AI more widely (or for the first time) – a few of which are as follows:
- The next three to five years are likely to prove AI’s value in the transformation of biopharma research and development (R&D), especially in drug discovery, again driving down costs but also revealing new revenue side opportunities.
- AI can help optimize clinical trial design and recruitment, which can reduce the time and cost required to bring a drug to market.
- Biopharma companies can use the AI-first approach in many small-molecule drugs in discovery and clinical trials. This can be done by providing access to new biology and improved or novel chemistry, and by helping to achieve better success rates with quicker and cheaper discovery processes.
- AI in supply chain and manufacturing can help to reshape and make both more efficient and effective the pharma value chain and bring life-saving therapies to market faster
- AI can be used to enhance biopharma promotional strategies, improve patient support, and optimize omnichannel marketing.
- Generative AI can help identify new uses for existing drugs, which can reduce the time and cost required to bring a drug to market.
In summary, AI can help biopharma companies enhance productivity, provide probability-of-success gains, reshape the pharma value chain, and bring life-saving therapies to market faster. AI is therefore a realm to embrace fully, so as to leverage the many opportunities it will create.
