Building for Real Impact: The Responsibility of AI in Pharma Analytics

Dr. Rajashri Mokashi
Sep 1
6 min read

The pharmaceutical industry stands at a pivotal moment. With AI spending projected to reach $16.49 billion by 2034, we're witnessing unprecedented investment in artificial intelligence, healthcare analytics, advanced healthcare analytics, healthcare data analytics, and big data in the healthcare industry across drug development, commercialization, and patient care. Yet as we celebrate these technological advances, a critical question emerges: Are we building AI systems that truly serve patients, or are we automating our way into new forms of risk?

Recent regulatory developments signal that this question is no longer academic. The FDA's January 2025 draft guidance on AI in drug and biological product development marks a watershed moment, establishing the first formal framework for how artificial intelligence, predictive analytics in healthcare, predictive healthcare, and healthcare predictive analytics should support regulatory decision-making in our industry.

But regulation, while necessary, addresses only part of our responsibility. As builders and users of AI systems in pharma analytics, pharma sales analytics, secondary sales tracking software, data analytics in the healthcare industry, and predictive analytics and healthcare, we must grapple with deeper questions about the role of technology in a space where every decision ultimately impacts human health.

The Compelling Promise of Complete Automation

The appeal of AI in pharma analytics is undeniable. Machine learning, predictive analytics, and embedded analytics SaaS models can process vast datasets in minutes, identify patterns invisible to human analysis, and generate insights that would take teams weeks to uncover manually. In a resource-constrained industry facing mounting pressure to accelerate time-to-market while reducing costs, AI promises efficiency at scale.

Yet this promise carries inherent risks, particularly when we mistake correlation for causation or efficiency for wisdom.

Consider territory planning – an area where AI excels at processing rep activity data, market potential metrics, and historical performance indicators. An AI system might recommend reallocating resources from Territory A to Territory B based purely on algorithmic optimization. But what the algorithm cannot capture is that Territory A serves a predominantly elderly population with complex comorbidities who require longer consultation times, or that the local healthcare infrastructure creates unique access challenges that historical data doesn't reflect.

The "black box" problem inherent in many AI models compounds this challenge. When pharmaceutical teams cannot understand why an AI system made a particular recommendation, they lose the ability to apply contextual judgment – the very human insight that separates good analytics from dangerous oversimplification.

The Traceability Imperative: Beyond Algorithmic Accountability

The AI Act's emphasis on transparency duties reflects a growing recognition that AI systems in healthcare and top healthcare data analytics companies must be fundamentally different from those in other industries. When an e-commerce algorithm makes a poor product recommendation, a customer might receive an unwanted package. When a pharma analytics algorithm makes a flawed recommendation, patients might lose access to life-saving medications.

This reality demands what we call "traceability by design" – building AI systems where every decision can be traced back through its logical chain to human oversight and domain expertise. True traceability goes beyond technical logging; it requires creating audit trails that connect algorithmic outputs to the business context that should inform their application.

In our work with pharmaceutical organizations, we've learned that effective traceability requires three foundational elements:

Contextual Documentation: Every AI recommendation must be accompanied by the specific assumptions, data limitations, and boundary conditions that influenced the model's output. This isn't just about model explainability – it's about creating a bridge between technical capabilities and business reality.
Human Interpretation Layers: AI systems should augment, not replace, human expertise. The most successful implementations we've seen include mandatory review processes where domain experts evaluate AI recommendations against factors the algorithm cannot assess.
Decision Genealogy: Organizations need the ability to trace any business decision influenced by AI back through its full decision tree, including both algorithmic and human inputs. When regulatory questions arise or unexpected outcomes occur, this genealogy becomes crucial for both compliance and continuous improvement.

The Collaboration Imperative: Breaking Down Silos

Recent consensus recommendations emphasize the need for transparency regarding dataset limitations and proactive evaluation across population groups. This highlights a fundamental truth: building responsible AI in pharma cannot be a purely technical exercise. It requires unprecedented collaboration between technologists, healthcare professionals, regulatory experts, and the communities our industry serves.

The traditional model – where data scientists build models in isolation, then hand them off to business users – is fundamentally inadequate for pharmaceutical applications. Instead, we need embedded collaboration where healthcare expertise informs every stage of AI development, from data collection through deployment and ongoing monitoring.

This means data scientists spending time in field offices to understand the real-world constraints sales representatives face. It means commercial leaders participating in model validation sessions to ensure algorithms account for market complexities that pure data analysis might miss. It means regulatory affairs teams engaging early in the development process to embed compliance considerations into system architecture rather than retrofitting them later.

Most importantly, it means acknowledging that the most sophisticated AI system is only as good as the human judgment that guides its application.

The Bias Challenge: When Algorithms Perpetuate Inequity

Ensuring high data quality and representativeness to mitigate algorithmic bias has emerged as one of the most critical challenges in pharmaceutical AI. Historical healthcare data often reflects systemic inequities in access to care, clinical trial participation, and treatment outcomes. When AI systems learn from this data without careful curation, they risk perpetuating these inequities at scale.

In commercial analytics, this might manifest as AI systems that consistently underestimate market potential in underserved communities or recommend resource allocation patterns that reinforce existing access barriers. The algorithmic efficiency appears mathematically sound, but the real-world impact undermines the fundamental goal of improving patient outcomes.

Addressing this requires more than technical fixes. It demands intentional efforts to identify and correct for historical biases, to ensure diverse representation in the datasets that train our models, and to continuously monitor AI outputs for discriminatory patterns that might emerge over time.

Building Responsible AI: A Framework for Action

Based on our experience supporting healthcare organizations in their AI journey, we propose a framework for building AI systems that prioritize patient impact over technological sophistication:

Start with Purpose, Not Possibility: Every AI initiative should begin with a clear articulation of the patient or healthcare outcome it aims to improve. Technical capabilities should serve this purpose, not drive it.
Embed Domain Expertise from Day One: Include healthcare professionals, regulatory experts, and patient advocates in every stage of AI development. Their expertise should shape not just how models are applied, but how they are built.
Design for Transparency: Build systems that can explain their reasoning in terms that domain experts can evaluate and stakeholders can understand. If you cannot explain why the AI made a particular recommendation, you cannot be confident it made the right one.
Implement Continuous Human Oversight: Create systematic processes for human review of AI recommendations, especially in high-stakes decisions affecting patient access or safety.
Monitor for Unintended Consequences: Establish ongoing monitoring systems that track not just technical performance metrics, but real-world outcomes including potential bias or equity impacts.
Plan for Failure: Build systems that fail safely, with clear escalation paths when AI recommendations fall outside expected parameters or when human judgment overrides algorithmic suggestions.

The Path Forward: Technology in Service of Humanity

The pharmaceutical industry's AI transformation is not a question of if, but how. With exponential growth in AI adoption since 2016 and spending expected to hit $3 billion by 2025, we are already deep into this transformation.

The question we must answer is whether we will build AI systems that amplify human wisdom or replace it. Will we create technologies that improve access to life-saving treatments, or systems that optimize for metrics that may not align with patient benefit?

At Gregor Analytics, we believe the answer lies in refusing to accept this as an either-or proposition. We can build AI systems that are both sophisticated and transparent, both efficient and equitable, both powerful and responsible. But this requires intentional choices at every stage of development and deployment.

The stakes are too high for anything less. Behind every data point is a patient, behind every optimization is a potential treatment outcome, behind every algorithmic decision is a human impact. Our responsibility is to ensure that as we scale the power of artificial intelligence, healthcare analytics, embedded analytics SaaS, white label analytics SaaS, and predictive healthcare, we never lose sight of the human intelligence that must guide its application.

The future of AI in pharmaceutical analytics will not be determined by the sophistication of our algorithms, but by the wisdom with which we deploy them. That wisdom must be earned through collaboration, tempered by humility, and measured not by technical metrics alone, but by the real-world impact on the patients our industry exists to serve.

The question is not whether AI will transform pharma analytics – it already has. The question is whether we will transform how we think about our responsibility to use it wisely.