This blog post is the fourth in a series on the impact that AI will have on different business aspects of pharma.
The latest wave of AI continues to break across many industries, including retail, self-driving cars, agriculture and manufacturing. While tales of driverless cars are likely to catch your eye, there’s a less newsworthy, but no less important demand for leveraging AI behind the scenes in the workplace. How can non-commercial, enterprise functions improve efficiency and enable better decision-making?
To learn more about how pharma’s enterprise functions are adapting to this trend, I spoke with my colleague, Shankar Viswanathan, who leads ZS’s advanced data science team in India and has been named one of the top 10 data scientists there.
Q: We often hear how AI can be applied in commercial, clinical or R&D settings across pharma. What applications do you see in other enterprise functions?
A: There’s interest and use across manufacturing, finance, HR and even functions like regulatory and compliance. Centralized groups like enterprise IT are looking to build cross-enterprise AI capabilities that can be leveraged by multiple groups within an organization. Some are looking at leveraging natural language processing and text-finding capabilities that can be applied to different types of unstructured data, like operator notes in manufacturing and digital profiles of job candidates for HR. Also, you can use AI to analyze quarterly funding reports from finance or scan for adverse event signals for regulatory and compliance purposes.
Q: Is the aim of these AI programs different from what you see in commercial programs?
A: Well, there's a stronger bottom-line focus in the key aims of AI in these situations. For example, optimizing operations, especially at scale, is important. In manufacturing, there's a focus on using AI and AI-based systems to predict and mitigate lower-yield batches. Analyzing large volumes of data and sensor data and combining it with unstructured notes from operators is a priority in manufacturing.
Another priority for AI in these functions is driving agility, again at scale. Finance teams across industries are looking to forecast short-term demand for stock. They're often dealing with thousands of SKUs across many geographies, countries and regions in the face of both internal and external business changes. This, in turn, feeds downstream decisions such as inventory optimization and site-level manufacturing planning. This is why agility is key.
Another key aim is to derive insights efficiently to enable better decision-making, again at scale. In HR, AI-based systems pre-screen candidate profiles to predict their likelihood of success at an organization. Performance review systems are being augmented to mine feedback notes for talent across the organization and synthesize personalized insights for coaches to leverage with their direct reports. Intelligent automation systems are scouring through reports and prioritizing signals for human review.
In these enterprise functions, there is, therefore, a stronger emphasis on optimization, automation, efficiency and agility, all with a more bottom-line focus. That’s why enterprise IT groups are actively exploring AI algorithm-as-a-service options that can be configured for different contexts across the enterprise.
Q: Are there any key considerations for organizations as they build these types of AI capabilities across the enterprise?
A: Yes. Keep the focus on the human in the loop—the financial analysts, the HR recruiters and the manufacturing operators. Make the predictions and outputs of the AI systems clear and transparent to the humans involved. That’s critical for adoption and impact. People have a hard time trusting recommendations from AI without a clear explanation behind each recommendation. That’s why "explainable AI," or XAI, is a hot area for research with significant focus and funding from organizations like DARPA. The future of human-AI collaboration requires transparent and clear explanations in order to engender trust.
Thank you for your insights, Shankar.
In my next post, I’ll interview another pharma expert who will explain how AI is impacting his or her area in meaningful ways. Among the many topics that I’m currently exploring are user experience, customer targeting and patient insights. Until next time!