Belcorp reimagines R&D with AI

Hurdles to success

As CIO, Gopalan says his biggest obstacles were the extensive and unstructured character of most of the data from R&D processes and external databases, the specific talent required for the project (including bio scientists, bio informatics professionals, technologists, and data scientists), and the cultural shift required to ensure the new platform’s acceptance.

To tackle the first challenge, Gopalan says the team concentrated its efforts on automating and cleaning the diverse data sources and formats to attain enough high-quality data to support robust analytics. They utilized data mining technologies to scrape and compile data for models from 23 international public benchmark databases, and compared that with data generated internally since 2016.

To address the second challenge, Belcorp hired new talent to bridge the knowledge gap among different teams and established a technology hub to recruit first-rate data scientists and data engineers to aid with the project’s design and implementation. Gopalan notes the data and technology team needed expertise and practical knowledge in a combination of areas, including:

• laboratory processes to comprehend the data, biological processes, and business objectives of each use case
• data architecture for efficient orchestration and connection of data and various platforms used in the end-to-end process
• advanced analytics and AI to develop predictive solutions
• software development to create customized plugins and Web apps to provide a visual interface for R&D analysts
• talent training on data capabilities to ensure the end user could fully utilize the platform.

The last obstacle involved addressing the cultural change resulting from eliminating many of the laboratories’ manual processes.

“To overcome this, we trained the laboratory analysts on how to use the platform and piloted the initial use case to gather feedback,” Gopalan says. “Based on this, we made iterative changes to fine-tune the platform and its user experience. Furthermore, we succinctly conveyed the platform’s value and benefits to the end-users through a series of workshops and demos, thus ensuring the platform’s adoption.”

Now fully deployed, the AI Innovation Labs Platform has delivered 12 use cases to date that Gopalan says have yielded significant results. He points to cost savings from the reduction in laboratory tests,  formulations, external software licenses, and the optimization of activities.

“The return on investment for the project stands at an exceptional 432%,” he adds.

Not only has the project delivered on expected results, Gopalan says it has also led to the digital transformation of R&D.

“Through the project’s implementation and exploration of data-driven insights, we have gained deeper insights into our product development process and customer needs,” he says. “This has opened doors to discovering new avenues for innovation and business growth, enabling us to identify and pursue additional opportunities that were previously untapped.”

Insights gleaned

Gopalan says developing the AI Innovation Labs Platform has given him five key insights into successful digital transformation involving AI and analytics:

1. Embrace the complexity of digital transformations. These transitions are intricate processes and mistakes are inevitable. “Rather than being deterred by these, take them as opportunities to learn and persist in your digital journey,” he says.
2. Follow a value-focused strategy. Focus your energy and resources on areas that have the potential to yield significant value: rapidly scale high-priority use cases, discontinue unsuccessful experiments, and use quarterly milestones for regular assessment.
3. Reimagine business processes. Only by reimagining and reinventing existing business processes can you truly tap the benefits of digital transformation.
4. Initiate an early impact narrative. A compelling success story, backed by endorsement from the executive team and prompted by a leading use case, is crucial to gain enthusiasm through the organization and among end users.
5. Recognize the importance of talent. Pinpointing the necessary skills and competencies, and aligning the right people in the right roles at the right time, is crucial to achieving success.