Can AI Really Help? A Realistic Look at Pharma Strategy Today
Discover the real potential and limits of AI in pharma. Explore how AI is transforming real-world evidence, diagnostics, and patient engagement, and learn the critical steps for building a trustworthy, equitable AI strategy.
For anyone working in Pharma right now, one question seems to be on everyone’s mind: “What should we actually be doing about AI?”
The hype is impossible to ignore. But beyond the buzzwords, what is the real potential of artificial intelligence? And just as importantly, what are its limits? Having spent years navigating traditional product launches before diving deep into AI-enabled strategy (including recent training at Harvard), I’ve seen both sides.
The short answer is: yes, AI can absolutely help. But it's not magic. It's a tool and like any tool, its value depends on how we use it.
Where AI is Already Making a Real-World Impact
When you hear "AI in pharma," do you picture futuristic labs? The reality is often more practical and is already delivering value today.
🔍 Finally Making Sense of Real-World Evidence (RWE): We’ve talked about RWE for years, but AI is the engine that truly makes it run. AI algorithms can analyze mountains of real-world data (health records, insurance claims) faster than any human team. This means we can quickly understand how our treatments perform in the real world, providing powerful evidence for doctors and payers.
🧠 Powering Smarter Diagnostics and Precision Medicine: This is where AI’s pattern-recognition skills really shine. From spotting subtle signs of disease in medical images to identifying biomarkers, AI is helping clinicians make earlier and more accurate diagnoses. This is the foundation of precision medicine—getting the right treatment to the right patient, faster.
💬 Engaging Patients in a Meaningful Way: How do we offer support to patients 24/7? AI is helping us do just that. Smart chatbots and virtual assistants can answer common questions, provide medication reminders and offer empathetic support, making patients feel heard and empowered in their own care journey.
The Elephant in the Room: Bias, Equity and Trust
This is the part of the conversation we can't afford to skip. For all its power, AI is a mirror that reflects the data it's trained on. If that data contains historical biases, AI will not only learn them—it will amplify them.
My training at Harvard hammered this home:
Ethical implementation is non-negotiable.
Confronting Bias: An algorithm is only as unbiased as its data. We must actively ensure our data represents diverse populations. Otherwise, we risk building tools that work for some patients but fail for others, widening the very health equity gaps we aim to close.
Building for Equity: The goal isn’t just an AI model that works "on average." The goal is an AI model that works for everyone. This requires intentional design and continuous testing across all demographic groups.
Earning Trust: Trust is pharma’s most valuable asset. To maintain it, we need to be transparent about where we're using AI and how it works. Black-box solutions won't cut it. Patients, doctors and regulators need to have confidence in the technology we deploy.
Breaking Down the Silos: How Marketers and Data Scientists Can Actually Work Together
The biggest barrier to AI success often isn't the technology—it's the people. The traditional wall between commercial teams and data scientists has to come down.
So how do we do it?
Start with the Problem, Not the Tech: Don't ask, "How can we use AI?" Ask, "What's our biggest strategic challenge and could AI help us solve it?" Whether it's finding the right patients for a trial or personalizing doctor engagement, lead with the business need.
Learn to Speak the Same Language: Marketers don't need to become data scientists, but they do need to understand the basics. And data scientists need to understand the market realities. Create space for cross-functional teams to learn from each other.
Iterate, Get Feedback and Refine: The best AI solutions are built in a loop. The data team builds a model, the commercial team provides real-world feedback and the model gets refined. It’s a partnership, not a hand-off.
From Then to Now: A Personal Reflection
I remember the days of building launch strategies based on months-old market research and painstakingly manual data analysis. We did great work, but it was slow and limited by our own bandwidth.
Today, my work is about augmenting that human intuition with the power of AI. It’s about using predictive analytics to see around the corner, not just look in the rearview mirror.
The "Age of Intelligence" isn't coming; it's here. But it’s not about choosing between human insight and artificial intelligence. It’s about creating a powerful partnership between the two. And for pharma, that partnership is the key to accelerating innovation and building a more equitable future for healthcare.
Author: Agata Kinga Kaczmarek - The Health Tech Advocate
For anyone working in Pharma right now, one question seems to be on everyone’s mind: “What should we actually be doing about AI?”
The hype is impossible to ignore. But beyond the buzzwords, what is the real potential of artificial intelligence? And just as importantly, what are its limits? Having spent years navigating traditional product launches before diving deep into AI-enabled strategy (including recent training at Harvard), I’ve seen both sides.
The short answer is: yes, AI can absolutely help. But it's not magic. It's a tool and like any tool, its value depends on how we use it.
Where AI is Already Making a Real-World Impact
When you hear "AI in pharma," do you picture futuristic labs? The reality is often more practical and is already delivering value today.
🔍 Finally Making Sense of Real-World Evidence (RWE): We’ve talked about RWE for years, but AI is the engine that truly makes it run. AI algorithms can analyze mountains of real-world data (health records, insurance claims) faster than any human team. This means we can quickly understand how our treatments perform in the real world, providing powerful evidence for doctors and payers.
🧠 Powering Smarter Diagnostics and Precision Medicine: This is where AI’s pattern-recognition skills really shine. From spotting subtle signs of disease in medical images to identifying biomarkers, AI is helping clinicians make earlier and more accurate diagnoses. This is the foundation of precision medicine—getting the right treatment to the right patient, faster.
💬 Engaging Patients in a Meaningful Way: How do we offer support to patients 24/7? AI is helping us do just that. Smart chatbots and virtual assistants can answer common questions, provide medication reminders and offer empathetic support, making patients feel heard and empowered in their own care journey.
The Elephant in the Room: Bias, Equity and Trust
This is the part of the conversation we can't afford to skip. For all its power, AI is a mirror that reflects the data it's trained on. If that data contains historical biases, AI will not only learn them—it will amplify them.
My training at Harvard hammered this home:
Ethical implementation is non-negotiable.
Confronting Bias: An algorithm is only as unbiased as its data. We must actively ensure our data represents diverse populations. Otherwise, we risk building tools that work for some patients but fail for others, widening the very health equity gaps we aim to close.
Building for Equity: The goal isn’t just an AI model that works "on average." The goal is an AI model that works for everyone. This requires intentional design and continuous testing across all demographic groups.
Earning Trust: Trust is pharma’s most valuable asset. To maintain it, we need to be transparent about where we're using AI and how it works. Black-box solutions won't cut it. Patients, doctors and regulators need to have confidence in the technology we deploy.
Breaking Down the Silos: How Marketers and Data Scientists Can Actually Work Together
The biggest barrier to AI success often isn't the technology—it's the people. The traditional wall between commercial teams and data scientists has to come down.
So how do we do it?
Start with the Problem, Not the Tech: Don't ask, "How can we use AI?" Ask, "What's our biggest strategic challenge and could AI help us solve it?" Whether it's finding the right patients for a trial or personalizing doctor engagement, lead with the business need.
Learn to Speak the Same Language: Marketers don't need to become data scientists, but they do need to understand the basics. And data scientists need to understand the market realities. Create space for cross-functional teams to learn from each other.
Iterate, Get Feedback and Refine: The best AI solutions are built in a loop. The data team builds a model, the commercial team provides real-world feedback and the model gets refined. It’s a partnership, not a hand-off.
From Then to Now: A Personal Reflection
I remember the days of building launch strategies based on months-old market research and painstakingly manual data analysis. We did great work, but it was slow and limited by our own bandwidth.
Today, my work is about augmenting that human intuition with the power of AI. It’s about using predictive analytics to see around the corner, not just look in the rearview mirror.
The "Age of Intelligence" isn't coming; it's here. But it’s not about choosing between human insight and artificial intelligence. It’s about creating a powerful partnership between the two. And for pharma, that partnership is the key to accelerating innovation and building a more equitable future for healthcare.
Author: Agata Kinga Kaczmarek - The Health Tech Advocate
For anyone working in Pharma right now, one question seems to be on everyone’s mind: “What should we actually be doing about AI?”
The hype is impossible to ignore. But beyond the buzzwords, what is the real potential of artificial intelligence? And just as importantly, what are its limits? Having spent years navigating traditional product launches before diving deep into AI-enabled strategy (including recent training at Harvard), I’ve seen both sides.
The short answer is: yes, AI can absolutely help. But it's not magic. It's a tool and like any tool, its value depends on how we use it.
Where AI is Already Making a Real-World Impact
When you hear "AI in pharma," do you picture futuristic labs? The reality is often more practical and is already delivering value today.
🔍 Finally Making Sense of Real-World Evidence (RWE): We’ve talked about RWE for years, but AI is the engine that truly makes it run. AI algorithms can analyze mountains of real-world data (health records, insurance claims) faster than any human team. This means we can quickly understand how our treatments perform in the real world, providing powerful evidence for doctors and payers.
🧠 Powering Smarter Diagnostics and Precision Medicine: This is where AI’s pattern-recognition skills really shine. From spotting subtle signs of disease in medical images to identifying biomarkers, AI is helping clinicians make earlier and more accurate diagnoses. This is the foundation of precision medicine—getting the right treatment to the right patient, faster.
💬 Engaging Patients in a Meaningful Way: How do we offer support to patients 24/7? AI is helping us do just that. Smart chatbots and virtual assistants can answer common questions, provide medication reminders and offer empathetic support, making patients feel heard and empowered in their own care journey.
The Elephant in the Room: Bias, Equity and Trust
This is the part of the conversation we can't afford to skip. For all its power, AI is a mirror that reflects the data it's trained on. If that data contains historical biases, AI will not only learn them—it will amplify them.
My training at Harvard hammered this home:
Ethical implementation is non-negotiable.
Confronting Bias: An algorithm is only as unbiased as its data. We must actively ensure our data represents diverse populations. Otherwise, we risk building tools that work for some patients but fail for others, widening the very health equity gaps we aim to close.
Building for Equity: The goal isn’t just an AI model that works "on average." The goal is an AI model that works for everyone. This requires intentional design and continuous testing across all demographic groups.
Earning Trust: Trust is pharma’s most valuable asset. To maintain it, we need to be transparent about where we're using AI and how it works. Black-box solutions won't cut it. Patients, doctors and regulators need to have confidence in the technology we deploy.
Breaking Down the Silos: How Marketers and Data Scientists Can Actually Work Together
The biggest barrier to AI success often isn't the technology—it's the people. The traditional wall between commercial teams and data scientists has to come down.
So how do we do it?
Start with the Problem, Not the Tech: Don't ask, "How can we use AI?" Ask, "What's our biggest strategic challenge and could AI help us solve it?" Whether it's finding the right patients for a trial or personalizing doctor engagement, lead with the business need.
Learn to Speak the Same Language: Marketers don't need to become data scientists, but they do need to understand the basics. And data scientists need to understand the market realities. Create space for cross-functional teams to learn from each other.
Iterate, Get Feedback and Refine: The best AI solutions are built in a loop. The data team builds a model, the commercial team provides real-world feedback and the model gets refined. It’s a partnership, not a hand-off.
From Then to Now: A Personal Reflection
I remember the days of building launch strategies based on months-old market research and painstakingly manual data analysis. We did great work, but it was slow and limited by our own bandwidth.
Today, my work is about augmenting that human intuition with the power of AI. It’s about using predictive analytics to see around the corner, not just look in the rearview mirror.
The "Age of Intelligence" isn't coming; it's here. But it’s not about choosing between human insight and artificial intelligence. It’s about creating a powerful partnership between the two. And for pharma, that partnership is the key to accelerating innovation and building a more equitable future for healthcare.
Author: Agata Kinga Kaczmarek - The Health Tech Advocate
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No spam, unsubscribe anytime.