Is Your Best Business Decision Hiding in Your Data? Brand Management 156
Lead Author – Shailaja Dwivedi Pathak, with Vivek Hattangadi
This is a very powerful topic. It moves the discussion from technology to leadership.
A pharmaceutical company does not suffer from a shortage of decisions.
It suffers from the quality and speed of some of those decisions.
Every day, pharma leaders make many decisions.
• Should we increase the field force?
• Which doctors need more attention?
• Which brand deserves more promotional support?
• Which territory needs extra resources?
• Should we launch?
• Should we reposition?
• Should we change the communication?
• Which SKU should we stock?
• Where might we face a stock out?
• Which campaign is working?
• Which representative needs coaching?
Traditionally, these decisions come from experience, intuition, past data, and meetings. AI adds one more dimension: prediction.
• The progression is simple.
• Yesterday: What happened?
• Today: Why did it happen?
• Tomorrow: What is likely to happen?
• The next step: What should we do about it?
This last question is where AI becomes truly useful. It marks the shift from descriptive to diagnostic to predictive to prescriptive thinking.
Many Indian pharma leaders are already moving in this direction. Decision cycles are becoming shorter, shifting from quarterly to monthly and even weekly rhythms.
The “Imagine this” revolution
One of the strongest benefits AI can give a manager is the ability to ask Imagine this.
• Imagine this. If we increase the field force in Ahmedabad?
• Imagine this. We reduce frequency in Mumbai?
• Imagine this. We move twenty percent of our promotional budget from Canoglip to Canotide?
• Imagine this. If a competitor launches at a lower price?
• Imagine this. Stock availability drops by ten percent?
• Imagine this. We focus our next three interactions on a specific doctor segment?
AI can model possible outcomes. The manager still makes the decision.
AI improves the quality of the discussion before the decision.
This distinction is important.
Indian example: Sun Pharma (From a public domain)
A strong Indian example is Sun Pharma’s work with an AI sales analytics assistant. The system helps sales and business teams get insights from sales, inventory, and field force data without waiting for manual database queries or technical expertise.
The goal is simple: make access to sales intelligence faster. This shows a larger principle. AI should bring intelligence closer to the person who must decide.
A sales manager should not wait three days for an analyst to prepare a report before asking a business question.
One important caution
AI should be the copilot, not the captain.
This is especially true in pharma.
– AI can analyse.
– AI can find patterns.
– AI can predict.
– AI can recommend.
But human judgement, scientific responsibility, ethics, and accountability cannot be handed over.
This principle is also stressed in India’s healthcare AI discussions. AI should support human judgement, not replace it.
Your concluding statement
The competitive advantage will not belong to the company with the most data. It will belong to the company that converts data into better decisions, faster decisions, and more responsible decisions.
If you want, I can now shape this into a slide ready version or a crisp two minute speaking script.