AI in Pharma: Marketers Need to Relearn How to Supply Content

Pharma marketing is a battlefield. Brands versus brands. Evidence versus medical myths. Marketers must be nimble enough to deliver content fast, because HCPs have limited time and often collaborate only with three or fewer companies. They also need to be persuasive enough to ensure that emotionally charged misconceptions spreading online don’t bury their facts and evidence.

Content also must stay relevant. Brands need to understand what HCPs and patients care to hear, how they should be delivered, and in what format and tone.

To be all that, brands need to invest in artificial intelligence (AI) solutions and a sound strategy to guide their implementation. In this long-read, we’re going to answer these questions:

• How do you get started?
• Can AI power the entire content supply chain?
• What are three key prerequisites for good content performance?
• When does relying on AI no longer make sense?

Good Data in, Good Data Out

For AI to deliver high-impact content, your data has to be complete, accurate, and clean. Here’s what you need to do.

Involve medical teams early on

For years, clinical trials and medical data lived in a secure bubble, cut off from commercial teams. Medical experts were only involved at the tail end of campaigns, leaving patients’ questions largely unanswered. 

Nataliya Andreychuk, CEO of Viseven, shared on Biotech Bytes how, as an oncology patient, she struggled to find practical guidance on how to manage side effects. Yet, the brand she chose for treatment provided only general information about the drug mechanism of action. 

These information gaps happen because medical teams weren’t fully integrated into commercialization. Viseven now closes that gap by giving commercial teams access to the data both patients and HCPs need.

We orchestrate teams and create a structured content library of clinical evidence, ready for AI to work on. This way, pharmaceutical companies stop talking to customers about their products only and start creating content that speaks to their pains, hopes, and uncertainties. 

Modularize your content

Do AI technologies make modular content obsolete? Prasant Vijayakumar, Chief Strategy Officer at Viseven, is optimistic about the future of modular:

We haven’t even started using modular in the right way to claim it’s no longer needed.

Modular content is the structured data AI needs to generate consistent, compliant, and accurate outputs. By tagging each key message with metadata, like audience type, country, or channel, you build a library of approved, discoverable content blocks. So, when a marketer gives an AI agent, like our eVa, a prompt, the AI pulls from this library and its metadata to create fresh, compliant content on the fly.

Prasant believes that the next step is video. As content becomes more modular, eVa could eventually assemble compliant videos. The pharmaceutical industry already appears to be moving in that direction.

Once you see these benefits of modular content, it’s tempting to break down every piece of campaign data. But focus on the messages you’ll actually reuse and repurpose in future projects. Otherwise, you could end up with so many modules that AI pulls less relevant pieces, or your team spends extra time crafting precise prompts to get the right output, slowing the overall team’s efficiency.

AI Across Your Content Supply Chain

AI is a powerful tool in life sciences, but its impact depends on understanding the right use cases and recognizing when it’s better to avoid it.

Briefing

Briefs are a simple example of where AI can speed up teams. As Prasant points out, managers are often short on time, and tech can produce drafts in seconds. Their role is then simply to review and confirm accuracy before sending briefs to writers. 

When briefs are created manually, it’s unlikely that different team members will produce them in the same way. Aligning them across the team takes time, but AI can handle this heavy lifting quickly.

Tagging

The next step is deciding whether to create something new or repurpose existing assets stored in a digital asset management (DAM) system. If you don’t have to be extremely creative and you have relevant modules, you can speed up delivery by reusing content. 

Yet, efficient reuse depends on proper tagging. Since manual tagging is slow and error-prone, we integrated an AI feature, auto-tagging, into our content authoring platform, eWizard. The system assigns tags and ensures metadata remains consistent across clients’ libraries.

Building assets

Many view AI as an unreliable content creation tool that will hallucinate the moment it doesn’t know how to fill the information gap. In life sciences, outputs that merely seem plausible can create delays and drive up costs, as medical, legal, and regulatory (MLR) teams request multiple revisions.

But instead of shying away from AI and creating content like it’s 2016, imagine connecting a large language model (LLM) directly to your content library and knowledge base. Our AI agent, eVa, is trained to understand your DAM. It can be fine-tuned to get to “know” your company’s terminology, brand guidelines, and local regulations. That means the content it produces is both compliant and fully tailored to your brand.

Make sure the agent you choose is as transparent as possible, so you can see how each output is created and where every piece of content comes from. If, for example, no relevant materials are available in your DAM, our eVa can suggest alternatives based on publicly available web content but will clearly label them as unapproved.

Lastly, AI agents reduce the number of tools you need for content production. Research shows that context-switching fatigue from numerous different tools can erode a team’s efficiency by up to 40%. From what we see in practice, when eVa handles everything from text and images to layouts, it saves teams up to 5+ hours per asset.

Seeking resonance

“What content do we want to share with our customers?” is one of the most common wrong questions brands ask. A better place to start is: “What do our customers want us to share, highlight, or address?”

One way to answer this question is to use modular. Analyzing which parts of your content grab the most attention will give clues on what future content can be worth delivering.

Yet, there’s nuance to it. You still can be ignoring important conversations happening among HCPs and patients, since you came up with the story in the first place. In order to understand your gaps, you need to look outside.

Disclaimer: Simply looking at competitors and copying their hooks or messages doesn’t work. An effective strategy starts with thorough market analysis. Pay attention to what your customers are saying online about your product and brand, identify what competitors are doing that you’re not, and, most importantly, track the conversations happening across platforms.

Notice what Reddit users complain about, the new terms and concepts doctors discuss on LinkedIn, or the topics trending on YouTube podcasts. These insights help you understand the current state of the market and anticipate trends before your competitors do.

Using social listening tools is a powerful move. They help you not only understand your audience better but also gauge how they feel about your brand. AI-powered solutions can reveal overall sentiment at scale, showing whether conversations are positive, negative, or neutral. Yet, be careful, since AI can sometimes miss nuances like sarcasm or irony. So, it’s important to have a human eye review the insights.

Making digital interactions feel personal

“Remember that a person’s name is, to that person, the sweetest and most important sound in any language,” as Dale Carnegie famously said. It feels good when someone remembers your name. It implicitly signals that they pay attention and that you matter.

Surely, it’s not only about names. Knowing your customers’ likes and dislikes allows you to create products and experiences that they find hard to resist. AI can help create audience segments, so you can target your audience with dead-on accuracy.

Quick tip: when using AI for segmentation, make sure it doesn’t come up with more than 6 personas. Having more than that makes it difficult to act on and to consistently generate tailored and compliant content for each segment.

Making face-to-face feel personal too

When an HCP makes time for a visit, they expect a MedRep to deliver value. If that value isn’t clear in the first few minutes of the conversation, you’ve lost them for good. They’ll listen, stay silent, ask no questions, and will be “busy” the next time you try to schedule a meeting.

When MedReps come unprepared, they often resort to reading from their iPads. And, in our experience, that’s exactly when the opportunity to build a relationship disappears.

Recently, we launched an AI mentor, Kheiron, which lets reps train before a visit and become confident in real-world conversations.

Using HCP personas, Kheiron simulates interaction, acting as if it’s the provider the rep is about to meet.

This kind of simulation helps the field force get HCPs talking. And when HCPs voice their fears, doubts, or concerns, it’s actually a good sign. They’re starting to engage with your pitch.

Kheiron generates questions tailored to a specific HCP. Once a rep answers, it grades their response, and after the session, all results are sent to the marketing team. This allows them to refine content and guide the field force if needed.

Accounting for timing and channels

When it comes to personalization, marketers often focus on content and overlook timing and channels. Even the most relevant and engaging content won’t reach your audience if it’s delivered when a provider is busy with patients. And even if you get the timing right, choosing a channel where the provider is inactive or unresponsive means your efforts can still fall flat.

Here’s an example of how we approach this kind of personalization. For email marketing, we use Salesforce Einstein Send Time Optimization to analyze HCP engagement over a 90-day period. The data shows the optimal day of the week and time of day when each audience is most likely to open and respond. 

With AI, you can identify where engagement with your desired audience is already happening and focus your efforts on the channels that consistently drive results. Yet, it doesn’t mean you need to spray and pray, adding more and more channels.

Focus on a few, experiment, and act on your data. The most important thing in omnichannel personalization is making sure you collect data about HCPs, not channels. That’s why it’s vital to connect your CRM with your marketing automation. This is what lets you build smooth customer journeys across touchpoints.

Ensuring first-time approvals

In the past, teams would double-check materials until they felt confident to pass them to MLR review. But that confidence wasn’t based on data. It came from industry experience and intuition. 

AI tools like eWizard replace that gut feeling with data-driven confidence. Instead of relying on assumptions, teams can see a score that estimates how likely their content is to get approved.

The platform highlights exactly what needs to be improved and suggests specific fixes, whether it’s adding a missing reference or replacing an inappropriate image. This makes the revision process faster and far more predictable.

What makes eWizard a “purple cow” in a market full of brown ones is its ability to be fine-tuned. Imagine you need to check whether your content complies with local regulations in a specific market. You can feed eWizard with regulatory data, and it learns and retains it.

Then, when you upload your content, it evaluates it in the context of those local requirements. This helps your teams not only be globally compliant but locally accurate as well.

The eWizard’s pre-approval engine ensures a 99% first-time MLR pass, dramatically speeding up content delivery. In a world where misinformation spreads quickly, this speed helps brands amplify their voices and avoid getting buried under a flood of myths.

Localizing content

Does AI translation always live up to your expectations? If the answer is no, one likely reason is that you’re using generic tools. Switching to pharma-focused solutions can make a difference for your team.

With generic AI, you often end up editing heavily after translation. eWizard is different. It’s trained on pharma-specific knowledge, which makes its outputs far more accurate. It also goes beyond translation and supports localization when fine-tuned.

For example, you can train eWizard on your brand guidelines, local market linguistic nuances, and cultural peculiarities. This way, local markets get localized assets in seconds, with minimal manual editing required.

Even when AI does most of the work, it still needs a (human) hand to ensure the content resonates and is ethically sensitive to local audiences. For example, in some markets, people who have had cancer may not want to be referred to as “survivors” or “warriors.”

These are nuances AI can easily miss. That’s why human review remains vital, even when you’re using pharma-focused, fine-tuned platforms.

Three Elephants in AI-Powered Content Production

Recent analysis shows that the phrase “AI slop” was mentioned more than 475,000 times in just one month across four platforms. AI content can quickly turn into slop, generating zero ROI unless it stands on three strong elephants: depth, diversity, and velocity.

Depth

Your hypotheses should be grounded in data and backed by clear reasoning. You need a deep understanding of your customers: their behaviors, pain points, frustrations, triggers, motivations, doubts, and concerns.

Without that, it’s easy to default to whatever’s trending, copy it blindly, and throw marketing spaghetti at the wall to see what sticks. That approach drains your team’s bandwidth without delivering the results you’re aiming for.

Use AI social listening tools, data analytics, or even LLMs to research your audience. Here’s a quick tip: turn on the deep research feature of your AI assistant to see how it perceives your brand. This insight can help you fine-tune your generative engine optimization (GEO) efforts. 

Diversity

Diversity matters more than you think. If your content looks and feels the same every time, HCPs might not respond.

Some days, you reuse what you already have. Other days, you create something new with AI agents. The trick is to keep everything consistent while experimenting with formats and channels. AI can help you do that by creating layouts you can reuse and store in your content library, so everything still feels like your brand.

Velocity

What is velocity in physics? It describes how quickly an object changes its position over time, including the direction of that motion.

In AI-powered content production, velocity is how quickly you create assets, test them, and make changes when needed. In other words, it’s not about moving fast, but about moving in the right direction.

You can increase velocity in different ways: by adopting a modular content approach, speeding up MLR with pre-approval checks, or using pharma-focused AI agents to generate content.

Equally important is to reduce dependencies wherever possible. For example, building ready-to-use templates or choosing a no-code platform allows marketers to move faster without waiting for IT deployment.

Kind reminder: All these three pillars need to work together for your AI content supply chain to run like a well-oiled machine.

If you have only velocity and depth, you quickly deliver content that addresses customers’ needs. But without actively exploring new audiences, angles, or channels, you risk stagnation. What works today may stop working tomorrow, and you’ll have nothing ready to replace it.

When you have depth and diversity, you’re likely to struggle with scale. You may not keep up with evolving audience needs, miss engagement opportunities, and gradually lose some HCP segments. 

If you have velocity and diversity without depth, your content may fall flat. It fails to resonate, yet costs keep rising without returns.

When AI in Pharma Drags You Down

Don’t let AI run the show in every part of your content supply chain, especially where nuance and expertise matter. For example, AI might not spot the claim sensitive to a patient with a life-threatening disease or generate a CTA that doesn’t really encourage this specific HCP to act.

AI isn’t meeting with doctors, building relationships, or thinking about patients’ needs. Human judgment and expertise are what keep content accurate, relevant, and safe.

Also remember that people are not creatures of logic, but creatures of emotion. AI can write the most logical case for why your drug is better than the alternative. But the alternative can highlight that it doesn’t have a side effect that affected an HCP’s niece. Suddenly, the decision isn’t about logic anymore.

Finally, don’t let AI take over patient stories. Imagine reading a story about a woman fighting breast cancer that’s entirely AI-generated. How would it make you feel? Lack of respect? Annoyance? Like you’re being marketed to? When people sense a story isn’t authentic, all the trust a pharma brand has spent years building can start to crumble.

Optimize Your Content Production with Viseven

There’s no one-size-fits-all digital transformation playbook for how life sciences brands should change their processes to make AI drive ROI. From our experience, the first step is understanding your underlying business problems and then cherry-picking the tools that align with them.

Do your local markets start from scratch every time? Do you get frequent revision requests from MLR? Are medical reps frustrated by unresponsive doctors? Answering questions like these will show you which AI solutions deserve your focus.

Finding the right tool is, however, an easy part. You need proper vision and operational capabilities to make AI a part of the system.

That’s why Viseven isn’t just a tech provider. Even though we have our own AI pharma marketing solutions, we also help brands come up with a solid AI strategy, manage and deliver content operations, and orchestrate everything so that it delivers measurable results. By aligning teams and technologies, we help top pharma companies run faster without inflating their costs.