Can AI Work in Pharma Route to Market? ft. Hemal Savla
Featuring: Hemal Savla, Chief Information Officer, Shalina Healthcare
Host: Nikhil Chaudhary, VP Marketing at BeatRoute
Pharma is one of the most regulated and sensitive industries out there. Compliance and accuracy always come first. So, when a pharma brand plans its AI roadmap, it has to be far more careful than an FMCG, building materials, or appliances company.
That is what makes AI in pharma such a difficult question.
In this episode of The BeatRoute Podcast, we sit down with Hemal Savla, CIO at Shalina Healthcare, one of the biggest pharma companies in Africa. Hemal is a legendary CIO who has driven transformation across consumer electronics, FMCG, medical devices, construction, and cables. His time at Shalina has coincided with the arrival of AI, and very few people can dissect this topic as deeply or as precisely as he can.
If you are in pharma and want to understand why and how to use AI in your route to market, there is no better person to listen to.
Doctor Engagement Starts With a Few Minutes
Doctors are central to pharma’s route to market because they influence which products patients are prescribed. Yet medical representatives often have only a few minutes to engage with them.
Field technology needs to help representatives prepare for that limited window. They need the right content, a clear visit plan, and tools that allow them to complete essential activities without taking time away from the conversation.
My tech stack is then designed in such a way that it helps them plan the activities, sequence it out in a way so that they’re able to do almost everything in the limited time they get.
For pharma brands, the challenge is not simply to increase the number of doctor visits. It is to help representatives make better use of the few minutes they have. These are also the challenges Hemal returns to when discussing AI: helping representatives prioritize visits and receive more relevant guidance in the field.
A Prescription Is Not Yet a Sale
Doctor engagement creates prescription demand, but pharmacy stock availability determines whether patients can purchase the prescribed product.
Pharmacists also influence the final purchase. If a product is unavailable or an alternative is recommended, the brand may lose a sale even after its representative has successfully engaged the doctor.
That is why pharmacy visits involve more than relationship-building. Representatives need to check stock, understand competing products and prices, and share this information with the teams responsible for distributor replenishment.
If I’m promoting a certain product with my doctors, I am also ensuring that the product is available in the pharmacies around that area. That’s how my actual lead gets converted into actual sales.
Connecting doctor promotion with nearby pharmacy stock gives brands visibility into where prescription demand may not be met and where replenishment is needed.
Hemal sees a specific role for AI-assisted coaching here. Based on information from previous stock-taking, AI could alert a representative to a potential stockout and help them prepare for a more relevant conversation with the pharmacist. It would support the representative’s next action, rather than replace the need to connect doctor engagement with pharmacy execution.
One Field Platform Must Work Across Markets
Pharma brands can standardize core field activities across African markets while adapting their technology to local languages, commercial models, and team structures.
Hemal explains that doctor visits, pharmacy visits, detailing, stock-taking, and order-taking follow similar underlying processes across the countries where his team operates.
If I’m looking at a platform or a solution for my field force, it cannot be radically different to different countries.
The challenge is getting commercial teams to adopt a common platform without overlooking their local needs.
Hemal addresses this by involving business leaders early, showing them what the existing platform supports, and identifying what needs to change for their market.
This gives each country a common starting point while allowing for differences that genuinely affect execution. As pharma brands explore AI, those differences still matter: a capability needs to fit the field processes and business requirements of the markets where it will be used.
Field Technology Must Survive Field Conditions
A field sales platform must work in the conditions where medical representatives actually operate. Across African markets, unreliable internet connectivity can make a system that works well at a central office difficult to use in the field.
Hemal’s team addresses this by testing solutions with representatives before a full rollout. These pilots help identify whether the platform fits local processes, works with the available infrastructure, and allows representatives to complete their activities without delays.
We do a pilot for a couple of weeks. We identify the challenges, address them, stabilize the platform, and then we do a full-scale rollout. And we follow this model in every country.
The goal is to identify and resolve these problems before deploying the platform across an entire market.
Hemal takes a similarly practical approach to AI evaluation. Before adopting a solution, he wants to test whether it works with his organization’s own data and systems.
What Should Pharma Leaders Look for in AI-Enabled Sales Technology?
A pharma field sales platform must meet the business’s current requirements while remaining flexible enough to support future changes.
For Hemal, functionality fit comes first. He then looks at how easily the solution can adapt to changing business needs, integrate with existing systems, and support consistent data across the organization.
The second most important thing is the integration with your existing systems. Now, integration has its own challenges because each system is different.
Integration also requires clear ownership of data. For example, if the ERP is the source of truth for product information, the field sales platform should not create a separate version of the same data.
Hemal also emphasizes testing AI capabilities with the organization’s own data and operating environment rather than relying solely on vendor demonstrations.
Doing a POC on my environment is very, very crucial for me to understand whether that solution is going to fit in my environment.
For pharma leaders, evaluating AI means testing whether it can deliver useful outcomes with their own data, systems, and business requirements. A successful demonstration elsewhere does not guarantee the same results in their organization.
How AI Can Improve Pharma Territory Planning
In African markets where reliable external customer data is limited, information collected through years of field operations becomes an important resource for territory planning.
Doctor and pharmacy interactions help brands understand customer potential, competing products, stock availability, and market activity. This information helps teams decide which customers to prioritize and how frequently representatives should visit them.
How do I optimize that route to be the most productive? How do I ensure that my most likely customers are being met most frequently?
Hemal identifies territory planning as an area where AI can make a significant impact on pharma sales execution.
AI can help medical representatives plan their visits around customer potential and productivity, ensuring that high-potential doctors and pharmacists receive appropriate attention.
He also sees AI helping representatives align their product promotion with the marketing team’s priorities. If a brand is focusing on a particular product during the month, the representative’s detailing activities should reflect that priority.
With limited working hours, representatives need to focus on the customers and opportunities that matter most. AI-assisted planning can help them use the available field data to make those decisions more effectively.
The Doctor Conversation Remains a Blind Spot
Field sales technology can help representatives plan visits, present content, and record activities. But Hemal identifies one important gap: the conversation that actually happens with the doctor.
Those few minutes may reveal questions, feedback, and insights that could help improve future engagement. Yet much of that information is not captured by existing field systems.
When the actual conversation with the doctor is happening, those four or five minutes, what is being discussed, what is the feedback we’re getting from doctors, that is not getting captured anywhere.
Capturing and analyzing these conversations with AI could help pharma teams better understand doctors’ needs and prepare representatives for more relevant follow-ups.
How AI Can Support More Relevant Digital Detailing
Moving from printed visual aids to tablets gives pharma brands more control over the information their representatives share with doctors.
Centrally managed content helps ensure representatives present the latest product information instead of relying on printed material that may be outdated. Videos and other visual formats can also make detailing more engaging during the limited time available.
We are actually using a lot of videos rather than using data sheets or presentations. That helps us get more time with doctors and better engagement with them.
Hemal also explains how AI is helping his team create content that better reflects African market contexts rather than relying entirely on material developed for other regions.
The value of digital detailing lies in helping representatives deliver information that is current, relevant, and easier for doctors to engage with. AI-assisted content creation offers another way to make that material more relevant to the markets where representatives work.
AI Can Coach Reps Between Manager Visits
AI can help pharma field teams in two ways: by guiding medical representatives toward high-potential doctors and pharmacies, and by providing contextual coaching during customer visits.
AI-assisted planning can help representatives prioritize their visits and align product promotion with the marketing team’s priorities.
During a visit, AI could also provide guidance based on previous interactions. For example, it might remind a representative about a doctor’s earlier feedback or flag a possible stockout at a pharmacy based on previous stock-taking.
This is particularly useful because field managers cannot always accompany representatives on their visits.
Because now they know they are not alone. There is somebody who is guiding them, coaching them along the way and helping them improve their conversations.
Instead of following the same checklist at every visit, representatives can receive guidance based on the customer’s history, current situation, and the brand’s priorities.
For Hemal, AI-assisted coaching can help representatives feel more confident, improve their customer conversations, and make their time in the field more productive.
AI Adoption Begins With the Field Team
Even a useful AI recommendation has little value if the representative does not trust it or act on it.
For Hemal, successful adoption starts with involving business teams in defining the problem AI is meant to solve. Representatives need to see how a new capability improves their daily work rather than having a solution imposed on them by the technology team.
Any AI use cases in the organization have to be led by the business or functions themselves. It cannot be a technology-led initiative at all.
Hemal also recognizes that employees may worry about AI replacing their jobs. Involving them from the beginning and showing how AI can support their work helps build confidence in the technology.
When representatives see useful results in their own territories, they become more willing to use AI and identify other areas where it could help.
For pharma brands, AI adoption is therefore not just about introducing new technology. It also depends on giving field teams a meaningful role in deciding how that technology will support their work.
AI Needs Reliable Data and Human Oversight
AI can help representatives make better decisions, but allowing it to act independently introduces a different level of risk.
Hemal distinguishes between assistive AI, which provides recommendations, and autonomous agents, which can make decisions and take action on behalf of users.
Before moving toward autonomy, organizations need reliable data and clear controls. An autonomous agent acts on the information available to it. If that information is incorrect, it may make incorrect decisions and take actions that are difficult to reverse.
You need to have a human in the loop always. You need to have that kill switch.
This is especially important in regulated industries such as pharma. Hemal emphasizes that autonomous decision-making must take place within a controlled environment, with appropriate safeguards and human oversight.
He also sees regulatory requirements as a way to make technology evaluation more focused. Organizations can define the controls a solution must meet before implementation rather than treating compliance as an afterthought.
For pharma brands, the question is not simply what AI can automate. It is whether the organization has the data, safeguards, and oversight needed before allowing AI to make decisions and take actions independently.
The Way Forward for AI in Pharma Sales Execution
Hemal’s experience points to practical opportunities for AI in pharma sales execution. Territory planning can help representatives focus on high-potential doctors and pharmacists. Contextual coaching can help them prepare for customer conversations, while AI-assisted content creation can make detailing material more relevant to local markets.
But identifying a useful application is only the beginning. Pharma brands need to test AI with their own data, involve the business teams who will use it, and establish appropriate safeguards before allowing AI to take actions independently.
The bottom line: Hemal sees opportunities for AI to improve field planning and coaching. As organizations consider giving AI greater autonomy, he emphasizes the need for reliable data, appropriate safeguards, and human oversight.
About BeatRoute
BeatRoute SFA-DMS platform helps pharma brands grow prescription volumes, keep products available at chemist counters, and increase sales in the OTC channel, all through one sales force automation platform. Its results are proven across 200+ enterprise brands, including pharma leaders like Shalina Healthcare, Unilab, DynaDrug, and more.