Show Notes:

Julie Noonan shares a case study on using AI while working with a top 15 global pharma company to get the most insight from the data and reduce time to market or time to development of their particular molecules and drugs. In early 2022, the pharma company was using artificial intelligence and machine learning to analyze clinical and research data. The organization Julie worked with was a digital and data concentration alongside data scientists and computer scientists. Julie shares where this organization placed focus and what their goal was with regards to using AI and machine learning(ML), and the role she played in developing this center of excellence.

Company Use Cases of AI and ML

Most of the early use cases involved clinical data and research data. Clinical groups were conducting the first clinical trials with animal populations, and recording their data in various tools. They were studying a specific model molecule to understand its implications across projects. For example, they were studying a molecule for one disease indication and wanted to predict its relevance for another project that another team was working on. AI and machine learning prompts were used against the data, allowing them to organize and prompt data to return potential other indications that could be tested with the collected data.

Julie talks about how companies are grappling with the rapidly evolving AI technologies, and a center of excellence can be a solution. However, concerns may arise about adding bureaucracy and slowing down innovation. She explains how she helped her client deal with these concerns. The company culture of this global organization highly values entrepreneurialism, and allows data ownership within its group, allowing for experimentation unless it directly impacts patients.

She mentions that they were able to educate interested groups about the importance of patient safety and ethics. The organization rewards innovation by publicly recognizing those who come forward with project ideas. Even if the project is not great or a failure, it is a lesson learned. The company's top priority is the patient, and they reward those who come forward with ideas without imposing penalties or shutting down projects. The organization also stresses the need to comply with correct procedures to avoid ethics violations.

Inspiring a Company Culture of AI and ML Innovation

Julie talks about how her role in change management helped inspire innovation within the company. They used polls to encourage innovation and encourage change. They run exciting advertising, competitions, and partnerships with universities, allowing for the introduction and excitement of new AI technologies. This approach helps companies navigate the challenges of AI adoption and ensures that their innovation is not stifled by bureaucracy. Julie explains that for change to be successful, leader support plays a key role. The center of excellence (COA) is a key change management initiative within an organization. It involves making people aware of AI and machine learning, which can be achieved through various marketing strategies. The organization chose a name that aligns with its culture and annual message from the CEO, highlighting the future and benefits of AI and machine learning in drug delivery.

The COA also held pop-up events where individuals could access learning materials, certifications, and practice using fake data. Office hours were provided for those who had no idea about IT architecture or how the organization operated. Newsletter articles, posted posts, and video monitors were used to promote the COA's existence. A community of practice was formed, which met monthly for educational sessions and discussions on AI usage. Julie also explains how they monitored ethics and DEI to represent the target patient population.

Measuring the Efficacy of the COA

Measuring the effectiveness of the COA is challenging due to the lack of metrics. Julie talks about measuring awareness, and how the organization has grown from six members to a global community of over 1500 people. She also mentions accessing use of the learning, accessing use of the sandbox, and the number of projects brought into be evaluated, focusing on their metrics. For example, in the first year, 10 projects were part of a competition with a local university, where teams of university and company employees worked together to implement AI/ML elements in their projects. The project metrics included surprises, opportunities, and lessons learned. This success was significant in the pharmaceutical industry, as more drugs and experiments fail than succeed.

Over the last two years, the number of data scientists has grown dramatically, and the COA has become a vital tool for the organization's digital transformation efforts.

Timestamps:

AI use cases in pharma company

06:33 Balancing innovation and governance in a large organization

11:29 Marketing a new AI center of excellence internally

15:47 AI and ML center's effectiveness measured through awareness, access, and project metrics.

Links:

Website: www.jnoonanconsulting.com

LinkedIn: https://www.linkedin.com/in/jnoonanconsulting/

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557. Julie Noonan: AI Project Case Study

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