Artificial intelligence (AI) is gradually entering the landscape of life science companies. More and more Pharma companies are including this topic in their strategy and as a result more and more initiatives are focused on implementing this new technology. Nevertheless, as with every new wave of technology, early adopters are encountering numerous challenges. The data quality and completeness are just one concern. The lack of management buy-in due to very complex and expensive business cases is another.
From BASE observations, there is no lack of potential use cases for using AI. However, the challenge is that is takes +6 months to verify if use cases should be transferred to AI solutions. Too much time is spent on data-cleaning and problem-solving without a proven concept. This leaves management and the data analysts’ team in a very frustrating situation with difficulties justifying the investment while knowing that the technology has a potential.
The situation forces companies to find a manner to test use cases much earlier in the process. It’s important to identify the cases that would bring business value and the one that won’t, before massive investments are done. Essentially, there is a need to find a way to prove the concepts, not in months but in weeks.
For this purpose, hackathons can be a source of inspiration. In fact, they are trying to break the traditional software development paradigm, so why not use the same approach for AI use cases? In this context, of course, the evaluation cannot solely be made on the technology, as it requires the inclusion of business expertise as well as data literacy in the domain, but the idea remains the same. Performing an AI hackathon makes it possible to select the right projects in less than four weeks through a very simple sequence of steps as illustrated below.
The fundamental idea is to identify use cases, build a temporary cloud platform allowing for building prototypes — and prove or reject the use cases effectively. This enables the business to build solid business cases without investing millions.
1. It all starts with the definition of the vision and to ensure the focus of the business on the right area, the area of needs.
2. The next step is to identify a laundry list of use cases within the domain chosen. This should go along with identifying data relevant to the use case. That’s where SME expertise is crucial. The team needs to know very early what type of data is required as well as what the source should be. From this wish list, data engineers can start building a cloud platform with relevant data
3. Once the test system is up and running, it becomes possible to run the hackathon. Bringing together data scientists, data engineers and business SME for one or two high energy days yield extremely insightful results. The outcome of the prototyping sessions is of course not a production-ready environment, but it allows all the participants to materialize the outcome of all use cases tested, which is a great starting point to invest in an AI solution.
We recently used a hackathon in a Regulatory Affairs department who had been struggling with transforming regulatory documents into structured content for easier updates and submission to authorities. Instead of setting up a large-scale project to analyze and potentially solve this, we set up a data platform and spent a focused day with a team of subject matter experts from the company and data engineers and scientists from BASE. The result was a proven concept for the future solution to be implemented. In addition to this, local management was involved to ensure early buy-in to the future solution.
In all, we think hackathons are a very effective way to start an AI journey, as it can speed up the decision and the identification of the valuable use cases while engaging business stakeholders very deeply. In our experience, such methodology ensures management buy-in while at the same time facilitating the outcome assessment
About BASE life science
BASE life science is a fast growing, fast paced consultancy focused on the life science industry. Established in 2007 and based in Copenhagen, Denmark, BASE targets a local as well as a global customer base.
Since inception, BASE life science focuses on helping Life Science companies create real business value from digital platforms and data within its area of expertise; Commercial Excellence, Clinical, Regulatory Affairs and Quality & Compliance. Since 2007, the company has been active globally from Denmark and Switzerland with more than 60 employees.
About the author — Thomas Røhme, Partner, Head of Analytics
Partner and Management consultant with extensive experience from driving large complex projects within R&D, Production and Quality in Pharma. Expert in facilitating Management decision regarding adopting new technologies such as RPA, AI and ML.