Is AI the missing link in fixing the broken MLR process?

The major pharma company was pressing the board for answers. Medical representatives began reporting fewer opportunities to meet with HCPs, suspecting that competitors were collecting consent faster. At the same time, marketers pointed to competitors’ stronger content activity and greater presence.

Some C-suite members argued the threat was temporary, while others warned of the rise of AI and its ability to speed up content delivery.

This story resonates with many pharma stakeholders who wonder why so many conversations with HCPs never happen despite all marketing efforts. With the help of AI, their key competitors engage providers earlier in the decision-making journey and build trusted relationships that are nearly impossible to catch up to.

The root problem with MLR

Marketing teams often wait up to two months for medical legal regulatory (MLR) feedback. If the feedback is not positive, teams need more time for revisions, stretching content time-to-market even further.

Meanwhile, competitors who secure approval on the first try gain a clear advantage: they can engage HCPs sooner and more often, which puts them a step ahead in personalisation. After all, the faster you engage, the more tailored the experience becomes. And because HCPs make decisions based on both product value and the quality of engagement, the chances of being their first choice can diminish greatly.

Beyond lost engagement opportunities, there are also added costs. Teams spend time revising content, going back and forth with creative agencies and MLR teams. And even once they revise everything, there’s still uncertainty, as teams can’t be sure they can move on without facing an extra round of changes after another MLR feedback.

AI gives confidence during MLR

Just a few years ago, marketers could only dream of knowing their content would pass the MLR on the first try. Today, AI makes that 99% certainty possible.

AI algorithms can analyse content for compliance and predict approval likelihood. You can set a confidence threshold (for example, 96%) and, if a piece of content score is 95% and below, your team is automatically alerted to review and revise before submission.

For instance, solutions like eWizard flag errors, from missing references to inappropriate images or typos. And when campaigns are built from modular content, the AI pre-approval engine reveals which modules are unapproved or approved globally and locally, helping teams run with their content ideas much faster.

The workflow also becomes easier for the MLR team. The final PDF includes AI-generated labels that flag which modules are already approved and which need closer review. This way, AI accelerates not just pre-approval checks, but the approval process itself.

AI agent: Changing compliance rules

While most life sciences brands experiment with AI, few manage to scale it beyond the pilot stage. A recent McKinsey report shows that, while 8 out of 10 companies integrate technology into their workflows, 80% see little to no tangible benefit. The paradox is that role-specific GenAI has the greatest potential, yet, it’s the least adopted.

AI agents are starting to flip that script. Instead of functioning as one isolated tool, they act more like co-workers. Agentic AI is designed to handle complex tasks, interact with other tools, and continuously learn and adapt. Teams can collaborate with the agents and delegate repetitive, data-heavy tasks, like compliant content generation, that would otherwise eat up teams’ time and energy.

Interestingly, the same report found that teams tend to be more patient with AI agents than with traditional AI tools. As they see the model’s output improves with their ongoing feedback, they start to actively look for more ways to “agentify” their workflows.

Because the MLR process is standardised, agentic AI becomes a natural fit for pharma teams. Over the next five years, AI agents in compliance management could mean 4–8% more revenue and 5–9% lower costs.

Compliance through modular? Agents make it possible

Originally, the idea of the modular content approach was to cut down on unnecessary MLR rounds by reusing the existing high-performing assets. But in practice, modules often piled up in DAM systems. Teams couldn’t easily find what they needed and ended up building assets from scratch, especially at the local level.

Even with AI supporting data organisation, people sometimes forgot the perfect module already existed and went back to square one, building a new asset or a new module. Ironically, the attempt to avoid extra MLR cycles often led to duplicate efforts that required almost as many resources as the traditional review-and-revise process.

Now, life sciences brands that combine modular content with agentic AI can generate compliant content at scale. For example, with eVa, our AI agent, content creators can quickly generate new content using approved, high-performing modules from the DAM. No searching for the right block or manually composing assets, eVa can create the entire compliant and on-brand asset, including layouts, images, and copy.

Wondering what happens if there is no module to use? The agent can generate fresh content from web data, automatically flagging the unapproved parts. Teams can then verify those assets with the MLR pre-approval engine.

Finally, life sciences marketers can have open-ended conversations with agents on any topic. If AI is trained on pharma language and integrated with a company’s knowledge base, they get extremely accurate, contextual answers, helping to further refine their content.

Can we trust the quality of AI outputs?

There’s plenty of debate today about whether AI results can be trusted. And as a CTO at a company building AI solutions, I totally understand the concern. Life sciences is one of the most highly regulated industries and, here, even the smallest inaccuracy is unacceptable.

So, what happens if AI makes mistakes related to your company, product, local market regulations, or cultural nuances? Simple: you teach it.

Fine-tuning ensures the model retains the knowledge you feed it. That way, it won’t misspell your product name, slip into the wrong tone of voice, or overlook restrictions like direct-to-consumer advertising laws. So, your team moves faster and engages HCPs with compliant content in a timely way, when they are still open for communication.

You don’t need to worry about data cross-sharing either. Reliable providers allocate a dedicated memory server for each client, ensuring your information is never shared with other clients or AI vendors.

From MLR bottleneck to competitive edge

Life sciences companies shouldn’t fear AI. They should fear being unprepared to work with it. The longer they postpone adoption in critical processes like MLR, the harder it will be to catch up once competitors start using it to deepen engagement with HCPs.

The good news is that by speeding up the pre-approval process and automating compliant content creation, you are not only reaching HCPs faster, but also freeing your team to focus on strategy instead of checking local regulations or double-verifying references. This means higher-quality, more personalised, and more creative marketing campaigns.

About Viseven

Viseven is a global MarTech company specializing in digital content solutions for the Life Sciences and Pharma industries. With over 15 years of expertise, Viseven empowers pharmaceutical companies and their production agencies with AI-driven content management and automation solutions.

Our flagship eWizard Platform streamlines content planning, creation, distribution, and management—enhancing efficiency, reducing operational costs, and accelerating brand time-to-market. Designed for omnichannel and multichannel engagement, eWizard optimizes campaign management, data collection, and performance analysis, ensuring continuous message improvement for Brand Managers and Content Operations teams.

Visit us at viseven.com or follow us on social media: LinkedIn