Pharma’s Digital Shift: AI, Data & Human Insight

Welcome to another episode of Pharma Talks – the space where we explore the ideas, shifts, and innovations reshaping AI-driven pharma marketing and digital engagement.  

Today’s guest is someone respected for his holistic view of pharma, his data mindset, and his ability to tie predictive analytics back to commercial value. 

Jaramillo isn’t just a data scientist — he’s a seasoned expert spanning data analytics, commercial strategy, and medical fields, with experience at major companies such as Novartis, Johnson & Johnson, and Ipsen. This episode focuses on what it truly means to be a data-driven pharma organization in the age of AI, and how the industry can evolve and survive amid this technological transformation. 

How does pharma survive in the age of AI? Let’s discuss these topics today with Alejandro. 

From Dashboards to Decisions 

Nataliya opened the discussion by asking how the role of commercial analytics had evolved — from simple dashboards and reporting to actually shaping business AI-driven pharma decisions. “I always say that data used to describe the past — now it shapes the future,” she remarked, emphasizing that we need to view analytics in life sciences marketing as a decision-making engine rather than a mere reporting function. 

Alejandro agreed, recalling that when he first started in the field, most of the work involved looking backward — understanding what had happened and building relationships around that knowledge.

Today, given the technology, it’s more about analytics driving business strategy. The shift is from analyzing the past to predicting the future, enabling discussions that uncover both opportunities and challenges. 

He likened analytics to “a GPS for the business — helping on the journey of discovery.” While many people think of AI and machine learning as something entirely new, he clarified that much of what’s being used now builds on earlier methods. What’s changed is the scale and speed.

Nataliya agreed with the metaphor, adding that everyone today needs to understand the basics of data and learn to be “skilled users of the results.” Smiling, she added, “We are all GPS users in the end.” Then she turned the conversation toward the next topic: “If we shift our angle a bit — what’s hype, and what’s real value?” 

AI in Pharma: Value vs. Hype 

When Nataliya brought up the growing buzz around AI pharmaceutical marketing, she asked Alejandro to separate the real, tangible use cases from the hype, and to share where companies should be careful not to overpromise. She noted: “We work a lot with modular content and omnichannel journeys, and what I see is that AI in life sciences can bring huge value, but only when the business question is clear. It’s not magic.”

Alejandro agreed that AI has become the topic of the moment, and he returned to his earlier GPS analogy to illustrate his point. “With a GPS, we can go anywhere, find any location — it’s amazing,” he said. “But it’s both a journey and a destination.” Similarly, he explained, AI offers enormous potential, but it’s essential to understand what it actually is — a collection of systems that help businesses process information, reason, and make decisions at scale. 

What matters is defining what we want to accomplish with it. AI isn’t a genie in a bottle that will solve every challenge. The focus should be on identifying which components of AI can make business processes more efficient and predictive — while remaining realistic about what it can and cannot do. AI won’t replace thinking, but it will change the way we think.

Alejandro pointed to real-world examples from biopharma, where AI has helped improve sales performance, prediction accuracy, and patient adherence. Moreover, AI contributes to pharmaceutical R&D innovation, healthcare digitalization, drug development automation, and clinical trials optimization. But he also cautioned against overreliance. “We sometimes expect perfect predictions, but we must remember the importance of compliance and data ethics, especially in pharma.” Ensuring patient privacy and adhering to existing regulations are non-negotiable when building AI tools and platforms. 

Alejandro also warned about the risk of bias, particularly in areas like health equity. “If we’re not careful,” he explained, “AI can reinforce existing inequalities.” He offered an example: imagine a product performing well among patients with commercial insurance. Suppose AI identifies that group as the most profitable and directs more next-gen pharma marketing resources there. In that case, it may unintentionally ignore other populations — such as those with less access or lower adherence — further deepening disparities. 

“That’s why we need human judgment,” he emphasized. “AI can help us find opportunities that were hard to see before, but we must stay focused on delivering value across the entire patient population.” 

Personalization at Scale 

When the conversation turned to personalization at scale, Nataliya noted that many in the AI-driven pharmaceutical industry today are talking about the “human orchestration of an AI instrument.” She emphasized the importance of maintaining strong guardrails — from HIPAA compliance to responsible governance — even as companies embrace automation. Citing recent data, she mentioned that more than 44% of pharma companies are ready to invest in AI, and roughly 80% of IT budgets already go toward data integration. “So,” she asked, “how can data and AI support true personalization in pharma without overwhelming field teams or crossing regulatory compliance and AI boundaries?” 

Alejandro agreed that this was one of the most important, and complex, questions in the industry today.

It’s hard to have a single recipe, but you’re touching on something essential. Too often when people talk about AI, they immediately think of software, data, and infrastructure — and forget about the human element. 

“That human piece is actually where personalization begins.” AI, in Alejandro’s view, should be designed around the people it’s meant to serve, whether patients, customers, or healthcare providers. He gave the example of sales representatives who have only a few minutes to speak with a physician or clinic staff. If AI can help personalize the information a rep brings into that meeting — making it more meaningful and relevant — it empowers them to have a better, more valuable conversation. In this way, AI enables tailored engagement that saves time while improving the quality of interactions. 

Alejandro described personalization as not only a strategic advantage but also a moral imperative: “It’s part of our moral clarity, and AI helps us simplify these decisions.” He added that everyone — whether a field force member or a doctor — has a unique way of processing information. AI can help simulate behaviors, train field teams, and prepare them to make the most of those brief, high-impact moments with healthcare professionals. 

Nataliya concluded that this is exactly where modular content meets data. “Personalization doesn’t mean creating a hundred versions of the same message,” she said. “It means being smart about when and what we deliver to healthcare professionals.”

The Next Generation of Pharma Leadership 

Nataliya opened the next question by reflecting on how quickly the industry and its talent are evolving. “The next generation is growing and changing. What skills or mindsets do you think will define the next generation of AI pharma marketing and strategy leaders?” She added her own view that the future leaders will combine empathy with analytics. Emotional intelligence and data fluency must go hand in hand — it’s the only way to connect meaningfully with both HCPs and patients. 

Alejandro agreed, saying that future leaders will need to be comfortable with analytics, tools, and cross-functional teamwork. “They’ll have to align around common goals and strategies, and practice centered, values-based leadership.” While data literacy will be critical, he clarified that it doesn’t mean being an expert in everything. Instead, leaders should understand what AI and advanced analytics can do — and where their limitations lie — so they can ask the right questions and turn insights into meaningful action. 

He emphasized the importance of breaking down silos and listening attentively.

Sometimes leaders are so focused on implementing their own strategy that they stop listening. Even if we disagree, it’s important to understand where others are coming from — and to have the courage to say, ‘You’re right.’ Curiosity will be another defining trait. We may think AI has all the answers, but it still requires good judgment, understanding, and communication to drive a successful strategy.

In his view, tomorrow’s leaders won’t just be storytellers with data — they’ll be bridge builders, turning complexity into clarity and strategy into patient impact. Nataliya agreed, saying that connecting people is one of the most powerful aspects of modern leadership.

The new leadership role is transformational. It’s like being a translator. The biggest blocker today is language — marketing talks about brand, analytics talks about models, and sales talks about stories. We need translators, people who can connect the dots across silos.

Collaboration Across Functions 

When asked how analytics, marketing, sales, and market access can truly work together in a data-first model, Alejandro said that alignment across these functions is essential, but it takes time and trust. Too often, each team operates within its own priorities and data sets. “The real power comes from connecting all those pieces into one story.” 

He emphasized the need for open dialogue and shared priorities: marketing teams should move beyond simply measuring campaigns; sales should look past short-term metrics to leverage analytics; and analytics teams themselves should act as translators, not just data producers. “Analytics should be the glue that connects these functions,” he said, “and that glue is trust.” Teams need to know that analytics brings real value — grounded in solid methodology and good judgment — not just numbers. 

For him, success starts with understanding the business context.

It’s not enough to launch a campaign or build a model. You need to know the goals, the objectives, and how results — whether through traditional analytics or AI — will actually impact the business. Sometimes, it means challenging business partners and asking tougher questions about what data is meant to answer. 

Nataliya agreed, noting that becoming a truly data-driven company is more about culture than technology. “You can hire the smartest data scientists and buy the best platforms,” she said, “but if teams don’t understand the technology or its impact, the value will be limited.” The goal is to empower people across the organization to use insights confidently. Real transformation happens when data stops being exclusive and becomes a shared asset. And it goes well beyond AI pharma marketing.

To Sum It Up 

Alejandro built on that point, saying that leaders should focus on learning the business, not just the technology adoption in pharma. “It’s easy to get caught up in the hype of AI tools, but the real value lies in connecting technology to business problems and empowering organizations.” Collaboration is key: “You can’t do it alone. You need to work with people who may not be familiar with the tech.” 

He encouraged teams to stay curious and be team players. “Don’t be the person who only cares about numbers. Be the one who helps others get the most out of the technology.” As AI becomes easier to use — from generative tools to AI agents and chatbots — the real differentiator will be people who can bridge the gap between data and daily work. 

Nataliya wrapped up by thanking Alejandro for the insightful discussion. She invited listeners to continue the conversation with him on LinkedIn, adding:

Don’t hesitate to reach out to us — there’s so much more to explore in this space. 

Watch the full episodes of the Pharma Talks podcast on our YouTube channel. And stay tuned for more, featuring pharma and life sciences leaders from all over the world.

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