Bridging the Gap: Can AI Learn Cultural Intuition?

Pharma brands are cautious, and often apprehensive, regarding the uptake of AI systems. Their concern lies less in AI’s potential value, and more in the possibility of biased or low-quality outcomes.

While AI-powered marketing drives 20–30% higher ROI, accuracy remains a sticking point. And in life sciences, where regulations are strict and mistakes are costly, that concern is amplified.

Increasingly, brands are turning to AI fine-tuning as the answer, shaping general-purpose models with cultural nuance, corporate tone of voice, and local regulations.

Human-AI Gap

AI-generated content can miss the mark, overlooking compliance, company values, and cultural nuances that seasoned teams handle with ease. AI missteps can erode customer trust, delay MLR reviews, and result in the lack of brand identity.

A phrase like ‘contains gelatin for faster absorption’ may be read as a benefit claim in Europe, yet in the Middle East it risks clashing with dietary laws. In another case, an AI system might address key opinion leaders as ‘influencers’, ignoring the professional etiquette and established classifications within life sciences. Or it could generate value statements such as ‘passion is our biggest priority, when in fact the organization’s culture is rooted in patient centricity.

Yet, marketing teams can no longer play it safe with AI-free manual workflows, as decreasing entry barriers and rising competition demand faster action. To stay ahead, brands must engage HCPs early in their journey with consistent, relevant messaging.

Fine-Tuning the Model: AI as a Cultural Ambassador

By custom-training AI, companies can capture the best of both human and machine worlds: cultural/regulatory awareness and speed of execution. Culturally trained AI embodies your values and tone of voice, enhancing customer trust and ensuring brand consistency.

Successful integration starts with defining use cases, establishing ethical guidelines, and aligning on what success looks like. Providing the model with high-quality content samples allows it to adopt your corporate values, tone of voice, and company-specific terminology.

The more examples you can provide, the more accurate and bias-resistant your model becomes. The gap between training with 15 documents versus 500 is substantial.

For pharma brands, a critical priority is guaranteeing that AI systems produce compliant content in local markets. The principle is the same: models need to be trained on curated, high-quality, error-free, regulatory and cultural data. When applied effectively, AI can generate life sciences content and flag potential issues pre-MLR, increasing first-pass approval rates.

When to Add a Human Touch

Some cultural nuances are so subtle that only human experts can catch them. That’s why human oversight is essential — not just for the final review stage, but throughout the entire content pipeline. Involving employees in designing prompt engineering frameworks helps shape the tone and format of outputs while reducing the risk of bias or hallucinations (AI content that is false but presented as true).

Focus on a pilot first to surface errors and cultural biases before expanding. During a pilot, set up stakeholder feedback loops to ensure that AI is working as intended. With each correction, the model improves, generating more accurate results over time.

Be sure to document these early wins to address skepticism and help teams overcome resistance to new workflows. When it comes to scale, invest in prompt engineering training and maintain a cycle of measurement and refinement.

Can AI truly grasp the cultural intuition that employees spend years building? With custom training, it can, but human oversight is still essential. This approach lets employees spend less time on production and review, while content reflects a stronger brand identity and builds confidence ahead of MLR.

Need a hand with AI fine-tuning and implementation? Get in touch to learn more.