Artificial Intelligence is reshaping how life sciences organizations gather insights, understand stakeholders, and make decisions. But with new opportunities come essential questions: When should we lean into AI-driven approaches, and when should we stick with traditional methods?
In healthcare, where patient safety, regulatory expectations, and evidence quality matter deeply, the answer isn’t always straightforward.
Where AI Brings Meaningful Value
AI can enhance healthcare research when used to support, not replace, human interpretation. When applied responsibly, AI-supported approaches can help:
- Accelerate synthesis of large qualitative datasets
- Surface early themes, signals, or emerging concerns
- Expand access when populations are small or hard to reach
- Provide rapid directional insight when timelines are tight
- Identify sentiments or emotional patterns that may shape decison-making
In these situations, AI allows teams to explore questions that would otherwise require significantly more time or manual effort, without compromising human oversight.
When Traditional Approaches Still Lead
There are also moments when AI should not be the first choice. Traditional qualitative or mixed-method approaches are still essential in situations where:
- Nuance is highly sensitive.
- Clinical or therapeutic complexity demands expert moderation.
- Regulatory implications require full traceability.
- Insights could affect labeling, safety, or claims.
- The topic involves emotionally charged decisions or vulnerable populations.
Live conversations provide context, depth, and empathy that automated pattern recognition cannot replace. In these areas, AI may still play a supportive role, such as summarizing transcripts, identifying patterns, or organizing data, but should be used independently.
Balancing Innovation with Responsibility
The real opportunity for healthcare organizations lies not in ‘choosing AI’ or ‘avoiding AI’, but in knowing when each approach makes sense.
Effective, responsible decision-making is characterized by a balance between crucial, sometimes competing, factors:
- Speed with accuracy
- Innovation with credibility
- Efficiency with empathy
- New methods with clinical rigor
At KJT, AI-supported methods are always paired with human validation, therapeutic expertise, and careful oversight. This ensures that insights remain relevant, credible, and grounded in real patient and provide experiences.
When to Hold Steady, and Why That Matters
In specific scenarios, maintaining the status quo with traditional research is the most responsible choice. This includes:
- Formative work informing strategy or claims
- Emotionally sensitive patient research
- Areas where bias must be tightly controlled
- Situations where granular clinical understanding is critical
- Early-phase concept work requiring co-creation or active moderation
Choosing not to use AI is not a lack of innovation, it is a strategic decision in service of quality.
A Framework for Informed Choice
Organizations often benefit from asking:
- What decision will this insight inform?
- What level of nuance is required?
- Is AI-supported analysis helpful, or could it risk oversimplification?
- Does the therapeutic context require more careful handling?
- Is speed the priority, or is depth more important right now?
- Will stakeholders expect high traceability and human interpretation?
Thoughtful answers to these questions help determine whether an AI-supported, traditional approach, or a blended model is most appropriate.
Conclusion
AI offers healthcare organizations powerful new tools, but it is not a universal answer. The most effective teams are those that innovate with intention – embracing responsible AI where it genuinely strengthens insight, while maintaining traditional approaches where human nuance, empathy, and clinical understanding is essential.
At KJT, we believe the future of insight is not AI versus human expertise – it is AI with human expertise. The choice lies in knowing when to use which.
Author
Dan Wasserman
Chief Operating Officer and Head of AI Solutions
Dan Wasserman is KJT’s COO, leading the integration of generative AI into client solutions to drive innovation as part of Sparq Intelligence. With expertise in compliance, operations, and analytics, he transforms complex data into actionable insights. Dan values collaboration with KJT’s teams to help clients design better products and services.
