What is the state of AI in Pharma commercial operations? What is the biggest hurdle to getting meaningful impact and value from AI in Pharma today?

Listen in to Aktana CEO David Erhlich share his industry knowledge on those questions in the “AI in Business Podcast” presented by Emerj Founder Daniel Faggella, whose top-rated podcast weekly focuses on interviews with AI and machine learning executives, investors, and researchers.

Listen to the AI in Business Podcast Here

Some highlights from David’s remarks

The pharmaceutical industry in general is at the very beginning of this journey towards what’s often framed as omnichannel. Meaning that rather than digital and marketing teams operating on one side and the field operating on another side, with them coming together once or twice a year, they can now use data science to influence the collaboration between those two sides on a real time basis.

However, one of the learnings that our customers are going through with AI, is that the models and the data are only a piece of the equation. 

Everybody focuses on: How do we get the data? What models do we need? How do we build that intelligence? Which can lead to lots of models, lots of rules and a lot of data. And that can generate possibly 55 things for a sales rep to do in the next hour. But that sales rep can’t do 55 things in the next hour – not important things. Maybe they can do one, and maybe that one will take two hours. 

So there needs to be something that rationalizes, synthesizes and optimizes all of the different AI to say, here is the optimal next action amongst all the signals that have come in. And here’s how to orchestrate that across all of these different channels. 

Leveling up commercial operations requires an infrastructure piece that is beyond models and data. It is the infrastructure to connect the output of those models through the data to actual actions, and then learn from those actions and take better actions next time. 

That infrastructure is really difficult and complicated, and many companies don’t even think about that until they’ve already got 200 data scientists on board building models. They’re beginning to recognize that they need to manage all that output.

Our solution is a platform that allows customers to plug models in to create journeys and identify where ROI should be optimized. Then our engine goes through and looks at every permutation of action that the models produce; then we look at the actions that journeys or micro journeys suggest. We will score every possible combination of actions through all channels to all segments by ROI. And then we will orchestrate the first of those. And of course, after you’ve orchestrated the first action, the whole set of conditions changes, and you have to re-optimize to get to the second action, and so on continuously.

Interested in learning more about how to create, manage and optimize AI-driven engagement strategies in pharma? Talk to us.