How AI Is Changing the Way We Market in Healthcare

AI is changing nearly every part of the healthcare ecosystem, including how we think about and execute marketing. From shaping early strategy to helping create content faster, AI-powered tools offer new ways to speed up timelines, respond more flexibly, and make smarter decisions based on real-time data.

In this piece, we’re taking a closer look at where AI is already showing up in healthcare marketing, where we think it’s going next, and how we’re starting to apply it to support drug launches and commercialization efforts at TJP.

AI’s Growing Role Across Healthcare

AI is being implemented in everything from diagnostics and care delivery to operations and administrative workflows. A recent Deloitte report on generative AI in healthcare points to some of the pressures the industry is facing and highlights the potential of AI to ease some of that strain.

“The health care ecosystem is grappling with interlocking crises, from labor shortages and clinician burnout to declining profitability and worsening health outcomes, particularly in underserved communities…Generative AI technology has the potential to address these existential crises, among enterprise and direct-to-consumer applications alike.”

At the same time, research from McKinsey shows that 85% of healthcare leaders say their organizations are already exploring or implementing AI initiatives. Many are doing so through partnerships, seeking to build new capabilities that can help them work smarter and respond faster.

Why the AI Trend Matters for Pharmaceutical Marketing

For marketers, this shift is more than a tech trend. It reflects a broader change in how healthcare organizations think, plan, and communicate. At TJP, we’re starting to incorporate AI into large-scale pharmaceutical initiatives in ways that help improve speed, clarity, and adaptability.

That includes using AI tools to simplify repetitive content tasks, support modular messaging, and help brand teams move more quickly through the development process, while still staying grounded in regulatory expectations. In fact, we’ve already seen cases where this approach cuts production time by over 30% compared to traditional workflows.

But we also know that speed isn’t everything. One of the real challenges ahead will be striking the right balance: leveraging AI to increase efficiency while still maintaining the quality and compliance that pharmaceutical marketing demands. That balance will take thoughtfulness, rigor, and clear internal standards.

AI Tool Applications Impacting Pharmaceutical Marketing

The examples below reflect areas where marketers are already using these tools to solve familiar problems: market segmentation, audience targeting, customer outreach, and rapid yet adaptable campaign execution.

Audience Segmentation and Targeting

Effective pharmaceutical marketing depends on understanding the distinct needs of each stakeholder group. AI tools can help us better understand and target key audiences (patients, providers, or payers) based on real-world behaviors, preferences, or clinical profiles. That means more tailored messaging and campaigns that resonate on a deeper level.

Predictive Campaign Planning

From identifying the right moments to engage, to optimizing budget spend and creative assets, AI can help forecast which outreach strategies will have the most impact at specific stages in customer journeys (whether patient, payer, or HCP). Even modest applications of these tools can increase efficiency and unlock meaningful gains in reach, click-through rates, and conversions.

AI Chatbots

While still evolving, AI chatbots are already proving helpful in patient and HCP support, answering common questions, easing administrative burden, and providing always-on access to basic information. In the process, they may even be leveraged to capture valuable data that can inform future marketing strategies.

Content Automation and Personalization

From drafting headlines to adapting messages by segment or channel, AI can take a lot of the heavy lifting out of content production. More fundamentally, AI also enables personalization. Content can readily be adjusted to match the needs and preferences of different users, whether that's providers researching treatment options or patients seeking support resources.

Compliance Support

AI is starting to play a role in regulatory review, too, flagging language risks or off-label claims before assets hit MLR, which helps teams avoid slowdowns and stay consistent.

American Pharmaceutical Review takes a deeper look at compliance applications for AI in the pharmaceutical industry here.

How We’re Putting AI to Work at TJP

At TJP, we’re taking a practical, test-and-learn approach to integrating AI into our work. Some of the ways we’re currently applying it include:

• Message testing: Using generative AI to explore message variations across payer and HCP segments.
• Segmentation: Applying natural language processing and real-world data tools to define and prioritize key audience clusters.
• Field readiness: Simulating rep conversations with AI-powered training tools.
• Competitive monitoring: Using AI to track trial data and competitor activity, then distill that into actionable insights.

We see these tools not as a wholesale replacement of what we do, but as accelerators and ways to get to stronger outcomes, faster. These applications reflect how we are weaving AI into the fabric of pharmaceutical marketing: not as a standalone solution, but as a toolkit of accelerators that can be leveraged at clients’ discretion to amplify precision, adaptability, and impact across the commercialization lifecycle.

Let’s talk about how we can apply this thinking to your next launch.