Create personalized user journeys with Omnichannel Marketing as a Service.
Empower your marketing operations with AI and Data.
Adopt a future-proof holistic approach to content strategy.
Leverage a suite of professional services for pharma and life sciences.
Develop and enable adaptable pharma and life sciences marketing content.
Enable format-free marketing with content management platform.
End-to-end platform for content validation and sales force readiness.
AI-powered congress engagement coordinator.
Discover our story, values, and how we're making a positive impact
Explore the heart of our organization and meet the visionary minds
Join the Viseven team and grow your career with us
The Viseven team is about respect, diversity, comfortable working conditions, and equal opportunities for everyone. We explore, evolve, and adapt – together.
MLR teams play one of the most critical roles in pharma — yet their contribution rarely gets the recognition it deserves. More often, MLR is seen as the bottleneck: the stage where content slows down, gets stuck in endless revision loops, or dies altogether. Content writers and marketers regularly name it as their biggest frustration. However, done well, pharma approval workflows can be the foundation that makes content trustworthy enough to succeed. So how can MLR teams become genuine drivers of success rather than a checkpoint to survive? And what role is AI starting to play in getting them there? That’s what this article explores.
MLR stands for Medical, Legal, and Regulatory, and all of these are different functions, each responsible for reviewing content for a different reason:
Each discipline is protecting against a different kind of risk — clinical misstatement, legal liability, regulatory violation — and none of them can substitute for the others. MLR review is inherently slower than a single-reviewer sign-off, and this is not because of poor workflows or bureaucratic bloat.
MLR teams are the cross-functional review group responsible for approving all of the content produced by a company. Promotional materials, HCP-facing content, patient-facing content, digital/social assets, sales aids, congress materials, websites — basically anything a company puts into market that makes a claim about a product has to be first checked by MLR teams.
Content demands are accelerating faster than most MLR teams in pharma can adjust. Rising volume, omnichannel speed expectations, and AI-driven personalization are converging all at once — and the traditional review model wasn’t built to absorb any of them, let alone all three together.
Pharma content and marketing teams are constantly put under a lot of pressure to create more, get published on multiple platforms, keep up with modern trends, and do a roster of many other things. Data shows content production rose 7% globally and 29% in the U.S. in 2023 over the prior year, with no signs of slowing — though notably, field teams rarely or never use 77% of their field content, which signals that the workflows are simply not ready for the volume of content that’s expected from pharma companies.
HCPs and patients now expect pharma brands to engage them quickly and consistently across every channel at once — email, rep visits, web portals, social, in-app messages — rather than through slow, siloed, one-channel-at-a-time communication.
With 84% of physicians preferring to maintain or increase the share of virtual interactions with pharma companies, the pressure has moved from just about being present across more channels to also being fast and relevant within the ones HCPs are already choosing.
With content production scaling up by 10x or even 100x due to hyper-personalization, tools will be essential to streamline MLR review processes. However, giving AI tools to employees who aren’t prepared to use them can actually decrease performance rather than boost it — one of the biggest mistakes organizations make. AI-driven personalization doesn’t automatically reduce complexity — it can increase it if literacy and governance don’t scale alongside the tools.
See how eWizard turns pre-approved claims and modular content into faster, cleaner MLR review — without cutting corners on compliance.
The traditional model for the MLR review was built for a lower-volume, single-channel era — and it’s now colliding with the trends taking over the industry. Here are some of the biggest challenges that hamper progress and innovation within pharma:
Traditionally, content moves through Medical, then Legal, then Regulatory (or some fixed order) — one reviewer at a time, each waiting on the last. A single round of feedback can take weeks; multiple rounds of revisions compound that into months.
Reviewers manually scan for unapproved claims, missing references, off-label language — line by line, asset by asset — even when much of the content is nearly identical to something already approved. A tagline that’s been cleared a dozen times still gets flagged and re-checked from scratch, simply because no system remembers the prior approval.
Without centralized systems, tracking which claims/visuals/disclosures are actually approved becomes difficult, especially once content gets reused or repurposed across channels without a clear system of record.
Comments and revisions often bounce between marketing, medical, and legal in disconnected threads (email, PDFs, meetings) rather than a single coordinated workflow — creating duplicate reviews and miscommunication rather than one clean pass.
Knowing what’s broken is the easy part. The harder question is what to actually fix first — and here, the teams pulling ahead of the pack tend to agree on a short list of priorities.
MLR exists to protect patients, but overly cautious review can become a growth bottleneck. Still, trying to optimize MLR only for the sake of speed is likely to result in jeopardized safety of patients and HCPs. For many pharma MLR teams, the goal has become not less scrutiny, but smarter scrutiny.
As AI takes over more of the mechanical checking (references, claim-matching, missing citations), the differentiator between average and high-performing MLR teams stops being “did we catch the errors” — everyone with decent tooling can do that now. It becomes “did we get the context right,” because that’s the harder, judgment-dependent layer that AI can’t fully own.
Teams often resubmit nearly identical assets for full review, wasting reviewer time on things that were already approved elsewhere. Content modularization solves the problem by introducing a different mechanism: pre-approved modules (claims, visuals, disclaimers, references) get reviewed once, then reused across channels and campaigns — only new/changed pieces need re-review.
The biggest speed gains come from catching problems before content reaches MLR, not from speeding up the review meeting itself. This is “shifting left”: fix issues early, where they’re cheap to correct, instead of at the review stage.
The teams pulling ahead go a step further, toward “compliance by design”: content authored directly from structured, pre-approved claims and references, so there’s less to catch at all. That’s the real gap between a team using AI as a faster patch and one that’s redesigned the process around it.
Most teams treat MLR as a brake pedal on creative ideas. And when we happen to see a brand that moves very fast, we tend to think they are the ones with the loosest compliance. In reality, those brands are actually the ones who have managed to build compliance infrastructure based on both creativity and innovation.
Doing everything by the book can feel rigid and inhibiting. And it is true to some extent, but only when the teams behind compliance and content don’t work together to come up with a modern and flexible infrastructure, as it lets creative teams move faster and take more creative swings, because they’re not guessing at what will survive review.
MLR review isn’t just pattern-matching against a rulebook — it requires judgment calls that carry legal and regulatory accountability. Someone has to weigh ambiguous claims against evolving regulations, interpret how a message will land with a specific audience, and ultimately sign off knowing they’re liable if it’s wrong. Here is how AI is changing MLR teams and how they work:
As AI absorbs repetitive checking, MLR teams may shrink in headcount-per-asset terms, but the remaining roles demand more scientific and strategic judgment, not less.
The problems are clear. The fixes are not complicated — but they do require pharma teams to stop treating MLR as a bottleneck to survive and start treating it as a process to design. Here’s where to begin:
Before you purchase any tool or integrate any new systems, make sure you have a structured foundation first. AI-powered MLR checks and modular content will fail without a clean infrastructure. If the library’s a mess, any tool built on top of it inherits that mess. This is the natural “start here” advice.
Many MLR teams in pharma think that the biggest problem is the reviews taking too much time. However, if they were to take a look at the matter from a different angle, they would notice that most problems stem from content being recreated and resubmitted multiple times. If content is near-identical to something already approved, it’s best to repurpose the old piece of content instead of creating something very similar.
The phased rollout, despite taking more time, has been proven to be more effective than a launch of AI-powered tools across all areas right away. Start with one therapeutic area or market, run in parallel with the existing process for a few weeks, measure precision/recall honestly, then expand. Avoid the “AI demo excitement” trap of rolling out broadly before trusting the output.
Compliance-by-design should be a main approach for pharma teams. Compliance should shape workflows from the start. Involving MLR and legal early, both in content development and in curating the claims library, helps determine how AI-powered workflows can fit into compliance rather than work around it.
As AI takes the mechanical load off reviewers, teams should deliberately redirect that freed-up time toward context, nuance, and strategic judgment, rather than just using it to process more volume.
The pharma teams that win in 2026 won’t be the ones with the most content, or even the fastest reviews. Those who have rebuilt the foundation so speed and compliance stop being a trade-off will be able to withstand the challenges in the pharma industry and adopt AI in a way that advances innovation.
MLR was never the enemy of speed or creativity — the old process just wasn’t built for the volume, channels, and personalization pharma is dealing with in 2026. Get that foundation right, and the debate changes entirely. Compliance stops being a brake on content and becomes what lets teams move faster, personalize more, and take bigger creative swings — because they know exactly where the guardrails are before they start building.
If you are looking for a solution that helps companies build efficient MLR workflows that don’t hinder content creation and allow for freedom of creativity, contact our team. We will set up a free call where we will discuss all possible solutions that are suitable for you.
Talk to Viseven about piloting AI-powered review on one therapeutic area — no full-scale rebuild required to get started.
MLR stands for Medical, Legal, and Regulatory — the three review functions responsible for approving pharma content before it reaches the market. Medical checks clinical accuracy, Legal reviews IP and liability risk, and Regulatory confirms alignment with health authority rules.
MLR review is inherently multi-layered because each function is protecting against a different kind of risk, and none can substitute for the others. Delays usually come from linear, one-reviewer-at-a-time workflows, manual line-by-line checking, and content being resubmitted for review when it’s nearly identical to something already approved.
No. Regulators including the FDA, EMA, and ABPI Code require a human to be the final approver of regulated content. AI can speed up the mechanical parts of review — flagging unapproved claims, missing references, or off-label language — but clinical nuance, context, and final sign-off stay with human reviewers.
Modular content means pre-approving individual building blocks — claims, visuals, disclaimers, references — once, then reusing them across channels and campaigns. Only new or changed pieces need re-review, which cuts down the duplicate review cycles that slow most MLR teams down.
It’s the practice of building content directly from structured, pre-approved claims and references from the start, rather than creating first and checking for compliance afterward. It brings MLR teams upstream into claims-library curation and content architecture, rather than keeping them as a final gatekeeping step.
Not inherently. Creative work tends to fail late in review when constraints weren’t known upfront, not because reviewers reject ideas. When claims libraries and pre-approved modules make the guardrails clear from the start, creative teams can design within them rather than discovering the limits after the work is already built.
