The AI Implementations That Produce Real Business Value

Over recent years, the pharmaceutical industry has generally embraced AI. Due the nature of the technology, various companies has implemented it across multiple sectors. Now, the question that everyone is asking is: where is AI having an actual impact?

In March of this year, ACTO co-founder and CEO Parth Khanna wrote a piece for Pharmaceutical Executive discussing an area where he sees AI making a major impact. According to him, digital health solutions allow companies to both gather larger amounts of data and then derive actionable insights from that data.

Khanna recently spoke with Pharmaceutical Executive, where he elaborated on this topic, while also discussing how things have evolved in the months since he initially wrote his original piece.

Pharmaceutical Executive: What AI implementations produce real business value?
Parth Khanna: The most important factor in any AI rollout is the trust the program can generate. When you study where that trust comes from — how a human being comes to trust artificial intelligence — two big components emerge consistently, both through our work with customers and through a series of executive roundtables we've had the privilege of conducting. It almost comes down to an equation: trust equals compliant plus capable.

Think about it in everyday terms. If you have an AI tool that is capable but not compliant — take ChatGPT in its bare form, where it sometimes goes off in unintended directions — you're probably not going to trust it fully. Conversely, if you have something that is highly compliant but not capable — think of the AI assistant that pops up on a website and responds to every question with "Sorry, I can't answer that" — you'll eventually conclude it's a waste of time. Trust disappears in both cases. Being compliant and being capable are both essential.

What our research and experience have led us to is this: the more context an AI system has about the role and responsibilities of the human it's designed to support, the more capable and compliant it can be simultaneously. Take a medical science liaison as an example. An AI agent that understands what an MSL does, what a scientific exchange looks like, what the parameters of that role are, and the specific ways it should and shouldn't support that person — that system will be both more capable and more compliant than one built without that context. The role knowledge is what makes the difference.