Life Science companies have demonstrated a strong appetite for artificial intelligence solutions to support both the drug discovery and drug development processes. The percentage of those using AI soared from 44 percent in 2017 to 70 percent in 2019. Overall biopharma R&D budgets have remained strong in that time, which has funded increased investment in AI. Biopharma companies are feeding their growing demand for AI by hiring specialists and by contracting with outside firms to gain the skills and solutions they need. Both approaches present challenges. Hiring requires competing for candidates during an acute global talent shortage. Contracting requires shopping in a market where the “products” change daily as startups appear, others are acquired and subsumed by more established firms, and still other competitors emerge from adjacent industries. Whether an organization chooses to build or buy, both approaches take time, so efficiencies gained need to be timely and reliable if they are going to offset investment and improve the years-long effort to bring new drugs to market.
The good news is that applying AI can be successful and worth the effort. In many cases, suitable AI solutions and partners already exist for biopharma companies seeking to support drug discovery and development. More than 125 startups related to AI have been identified for drug discovery alone. These companies raised $1.8 Billion in funding in the last decade, including at least $100 Million every year between 2015 – 20192. Meanwhile, IT and business process service providers have achieved some impressive success stories in helping Life Science companies with AI initiatives. The challenges are how to find the right solution or provider, and then how to rapidly help your organization make the most of that investment in an emerging field.
More good news: The process of identifying, vetting, contracting with and managing specialized AI partners is nowhere near as arduous as the drug discovery and development processes themselves. ISG has identified processes and principles for effectively working with service providers, and we have helped Life Science companies apply them successfully in AI-driven engagements. Successes across the compound lifecycle include: identifying candidate molecules; safety-case processing and improving patient experience during trials. Other use cases include combining AI with robotic process automation (RPA) to form intelligent automation solutions to manage trial data and streamline reporting; and scaling AI and RPA efforts to non-scientific business processes throughout the organization.
Read this ISG paper Artificial Intelligence in Drug Discovery and Development: Improve Your “Hit Rate” for Successful Projects to learn more about drug discovery and development AI use cases and their value, and to learn how Life Science companies can evaluate potential partners and structure working relationships.
Download the paper.