Commercialising AI: Are We There Yet?
The business case for wholesale adoption of AI doesn’t need to be made. But how far along is pharma?
Advanced analytics and machine learning are the words on everyone’s lips in pharma right now. Companies big and small are clamoring to gain a commercial advantage in this space. Yet the industry seems long on enthusiasm but short on results.
To get an honest assessment of the industry’s current AI capabilities and share some practical advice, we spoke to Doron Aspitz, Founder and CEO of Verix, a business intelligence software company that delivers AI-driven analytic solutions for Sales and Marketing teams in the pharma industry.
From working closely with pharma companies, what would you say the overall maturity is?
Most BI/Analytic solutions in the pharma industry today focus on reporting of commercial results. We’ve seen advancement in the way things are presented – pretty graphs and charts, interactive dashboards that let analysts slice and dice the data, drag and drop capabilities to create different graphs, and more. In terms of look and feel there has been significant improvement. However, content wise, analytic solutions are not very mature. They don’t provide advanced analytics that amalgamate business logic, they don’t “understand” business processes and don’t analyze business drivers, market and brand dynamics. As a result, the actual analysis is still all done manually.
In 2018 we’ve seen AI solutions that offered NBA (Next Best Action) recommendations to field personnel, receive a warm welcome in the industry. It seems that these are rather immature as well, offering very limited insight to an experienced sales rep who doesn’t really know what to do with “Call on Dr. John Smith today” without any background on why, what specifically to talk about, etc.
Sales reps of today are very experienced and educated. They know their business and know their territory. Valuable insights would be helpful only if they offer a complete story – what drives this new opportunity, what messaging would work best here, why is it a hot opportunity?
What practical advice could you offer to commercial teams looking to become more AI-literate?
Practical advice – Don’t look at AI or ML as some panacea that will solve all your problems. It’s a tool. A strong and valuable tool but needs to be utilized as part of a well thought of solution. We have been using AI methods for many years to provide deep analytics and relevant, meaningful insights. It’s not a magic wand.
Next generation solutions will utilize AI to automate and boost business processes. Our latest AI based product Tovana, provides managers with advanced tools to continuously revisit strategy and align tactics for most optimal bottom line results. Tovana closes the gap between your Home Office strategy to tactics deployed in the field by keeping them always aligned.
AI needs to “understand” what it is analyzing, so now even more than before, vertical solutions that focus on a specific market will bring about more relevant and useful insights.
Tovana, Verix’s new highly dynamic packaged solution to uncover new and unexpected business opportunities for Pharma sales, is taking into account the specific characteristics of pharma sales with all its complexity. Surfacing deep, precisely focused insightsthat pinpoint relevant, practical information, tailored for the specific needs of each and every rep in the field. Tovana allows you to best plan and prepare your calls for the most effective results. Arrive with clear-cut messaging and ready answers to expected inquiries.
The idea behind Tovana is calling attention to opportunities and providing sales reps in the field with all the data they need to leverage upon those opportunities, easily accessed with a click of a button, without overwhelming them with too much irrelevant, confusing data in charts, tables, and graphs.
Tovana integrates a diverse of data sources, including new data sources such as Lab data, near-real time claims; Specialty pharmacy; patient services hub data, as well as continuous feedback from the field, and analyses with a combination of business rules and machine learning to predict patient opportunities and dynamically create the most promising target list.
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