This blog post is the fifth in a series on the impact that AI will have on different business aspects of pharma.
The past few years have seen an explosion of data, and with it, the need to make use of it. Gathering and stitching together internal and external data sources presents one set of challenges, but what about the analysts who struggle to leverage all of this data? How can they keep up with increasing demands for data-driven insights from the entire organization? How can we empower the whole organization to have access to these critical insights? Here’s a hint: AI is part of the solution.
To delve into the role that AI is playing in analytics consumption, I spoke with my colleague, Mahmood Majeed, leader of ZS’s business technology practice, who has extensive experience exploring innovative ways that AI can augment analytics consumption.
Q: As organizations become more data-driven, what’s the response you’re seeing to the increased demand for insights?
A: The increased demand for insights means we’re seeing more analysis shared, and in more forms than ever before. But with so much information coming our way, it’s hard to discern what’s useful and leverage it to drive decision-making. Searching through every data point and report that’s available wastes time and effort. The challenge that organizations face is to deliver the most pertinent insights in a timely manner and in the most suitable format with an amazing user experience.
Can one converse with their device, asking it questions about performance to unearth the insights most relevant to them? Can they subscribe to insights in the form of a news feed the way we subscribe to information in our social channels? I see organizations leveraging new and exciting ways to do that with conversational analytics, AI and natural language processing (NLP). These technologies allow us to scan through data and extract insights through natural conversation and text and offer specific and personalized suggestions. It's getting more traction now because of disruptive NLP Technologies like Amazon’s Alexa, Google Assistant and Apple’s Siri as well as AI to decipher personalized and relevant insights from large volumes of data. If I'm able to integrate conversational tech into my personal life, why can’t I bring that to my work life?
Q: Where should companies be focusing their investments in the next few years?
A: Analysts are saying that in 2020, 50% of analytics queries will be generated via search, natural language or voice queries. This speaks to the user’s need for more consumable analytics. To meet that need, companies should be shifting now toward generating suggestions and insights rather than sending data and reports, but they need to go further and completely disrupt dashboards and reports as they will need to be augmented through contextual insights that are personalized to the user and the role.
Take salespeople, for example. The concepts of call planning, targeting, execution, performance monitoring, sales reporting and other forms of customer insights will all be combined into what I call contextual insights. They’re actionable, near real time are embedded in the context of sales processes and fueled by relevant data from across the entire sales enablement process. It's relevant and contextual to what the sales person is doing at the time. It simplifies the complexity that exists today between 25 to 30 different systems and turns all that data into powerful, easy-to-digest, useful insights.
If routinely asked questions will be answered more effectively by leveraging machines, this will free up analyst capacity, which can be directed towards solving more challenging business questions. We believe that within 18 to 24 months, we’ll see a significant shift in the way information is consumed and decisions are made in commercial pharma. AI will work side by side with humans to bring this vision to light.
Q: If that’s the trend, how should pharma companies adapt?
A: Start small, and do simple experiments, proofs of concept or pilots at first. This is disrupting the way we work and so significant change management is required. The algorithms also take time to learn and to get better, and so patience with such initiatives is a must. AI is less about moonshots and more about a collection of ideas that create impact.
Thank you for your insights, Mahmood.
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!
BLOG POST: AI & Pharma: Start Small But Think Big