This article is part of our collaboration with some of the best-in-class marketers who are shaping the future of pharmaceutical communications. Together, we explore the upcoming trends that will redefine how brands connect with healthcare professionals and patients. From AI-driven compliance to more human, patient-centric messaging, these perspectives highlight what’s next in effective, trusted communication.
Why I use AI (and why you probably should too)
I don’t treat AI like sci-fi or sorcery. I treat it like a screwdriver that saves my wrists. In AI without fear (Sztuczna inteligencja bez strachu) I make exactly that point: AI already lives in our day (from face unlock to text prediction), so the job is to demystify it and use it calmly, ethically, and in plain language. When teams stop fearing the acronym and see a useful workflow, adoption accelerates experiment a little, ask questions and learn by doing.
In pharma, that “no-fear” mindset only works if it’s welded to regulation and empathy. In Pharma Marketing DNA I argue for a patient-centric operating system, clear, balanced information, real support programs, and continuous feedback loops. It’s not just good marketing; it’s good medicine.
My non-negotiable: regulation by design
AI helps me move faster within the rules, never around them. We hard-code compliance into prompts and templates: risk-benefit balance, indication scoping, adverse-event triggers, and regional guardrails. (Think FDA fair balance in the U.S., EMA requirements in the EU, EFPIA transparency, and PMDA accuracy in Japan.)
What makes a real difference is that AI doesn’t just produce drafts, it produces drafts already aligned with the rules. When I brief the system correctly, it automatically integrates the right disclaimers, highlights mandatory safety information, and reminds me of jurisdiction-specific limitations (for example, no direct-to-consumer promotion of prescription drugs in Europe). It’s like having a junior colleague who has memorized every SOP, every clause in the EFPIA Code, and every footnote in our training decks.
Internally, I insist on SOPs, review logs, and recurring training, culture and process matter as much as copy. And here AI is again an ally: it helps me structure submissions to MLR (Medical, Legal & Regulatory) in a way that fits the template, attach the right references, and pre-check whether a claim is balanced before a human reviewer even sees it. In practice, that means fewer “back-and-forth” loops, shorter approval cycles, and less frustration for everyone involved.
We audit proactively, collaborate with regulators when needed, and use technology to document interactions and approvals. AI is part of that workflow: automatically tagging versions, logging sources, and keeping a traceable chain from reference to claim. It makes compliance more transparent and less bureaucratic.
The paradox is that while regulation may look like a brake, building AI on top of procedures and regulations actually makes me faster. It’s safer and yes, genuinely quicker over time.
In every pharmaceutical company, MLR is often seen as the “bottleneck” of marketing. And not without reason: this is where science, law, and regulation meet - three domains that do not tolerate shortcuts or speed at the expense of accuracy. From my experience, well-implemented AI can be more than support here; it can actually become a catalyst for quality.
How AI supports me in MLR
Pre-check of materials
Before any draft reaches the MLR table, AI reviews it for:
- whether every claim has a referenced source,
- whether mandatory risk information and contraindications are included,
- whether the language avoids off-label suggestions.
This pre-screening eliminates many errors in advance and shortens the review cycle.
Automating compliance with procedures
Language models can “read” our SOPs and regulatory checklists, and then apply them directly to draft materials. In practice, this means AI automatically inserts disclaimers, safety statements, and standard wording, formatted exactly as expected by the committee.
Language models can “read” our SOPs and regulatory checklists, and then apply them directly to draft materials. In practice, this means AI automatically inserts disclaimers, safety statements, and standard wording, formatted exactly as expected by the committee.
Documentation and audit trail
Every version, edit, and source can be automatically logged. The result is a transparent audit trail that can be shown to regulators if needed. AI not only writes but also organizes and archives.
Every version, edit, and source can be automatically logged. The result is a transparent audit trail that can be shown to regulators if needed. AI not only writes but also organizes and archives.
Training and onboarding
For new team members, AI serves as a mentor: it explains why a change is required and points to the exact procedure. This accelerates onboarding and builds understanding, rather than a “checkbox” mentality.
For new team members, AI serves as a mentor: it explains why a change is required and points to the exact procedure. This accelerates onboarding and builds understanding, rather than a “checkbox” mentality.
The practical effect
AI does not replace the MLR committee. Human accountability and judgment remain central. But thanks to AI pre-checks and structured documentation fewer drafts bounce back for corrections, review cycles are shorter, teams feel less frustration and the final material is more consistent, more robust, and easier to defend in front of regulators.
Why this matters
When AI works “hand in hand” with procedures and regulations, MLR stops being a brake. Instead of a “bad cop,” it becomes a process that enhances safety, transparency, and efficiency. For me as a leader, it proves that technology can become a partner, not a risk.
What AI actually does for my team
1) Evidence-grade content at speed, with a human finish.
We draft literature-backed summaries, balanced risk statements, FAQs and claims modules as reusable building blocks, then humans add nuance and clinical judgment. Readability is a feature, not an afterthought.
We draft literature-backed summaries, balanced risk statements, FAQs and claims modules as reusable building blocks, then humans add nuance and clinical judgment. Readability is a feature, not an afterthought.
2) Omnichannel that feels personal, not pushy.
Models help us choose the next best content for HCPs, patients, caregivers, and payers, less noise, more help. Personalization is grounded in real needs, not vanity segmentation.
Models help us choose the next best content for HCPs, patients, caregivers, and payers, less noise, more help. Personalization is grounded in real needs, not vanity segmentation.
3) Field-force signal briefs.
Each morning, reps and managers get concise territory insights, trend deltas, guideline updates, and approved assets, so the time goes into meaningful conversations, not spreadsheets. That’s the everyday version of “AI as partner” I describe for leaders.
Each morning, reps and managers get concise territory insights, trend deltas, guideline updates, and approved assets, so the time goes into meaningful conversations, not spreadsheets. That’s the everyday version of “AI as partner” I describe for leaders.
4) Reputation-safe SEO and patient education.
We answer the actual questions people search for plain language, visible risks, and clear hand-offs to nurse lines or official leaflets. The aim is understanding and adherence, not clicks.
We answer the actual questions people search for plain language, visible risks, and clear hand-offs to nurse lines or official leaflets. The aim is understanding and adherence, not clicks.
5) Pre-MLR QA, not MLR bypass.
Before a reviewer sees anything, AI flags unbalanced claims, missing qualifiers, and off-label drift. Review cycles shrink; quality rises.
Before a reviewer sees anything, AI flags unbalanced claims, missing qualifiers, and off-label drift. Review cycles shrink; quality rises.
Keeping the work human (my LIDER compass)
Tools don’t transform organizations, people do. In Modern Leader 4.0 (Nowoczesny Lider 4.0) I share a simple compass I actually use:
- L - Ludzie (People): I lead people, not projects. Psychological safety first; it’s how truth and learning show up.
- I - Innowacyjność (Innovation): I reward curiosity and small, fast pilots; stagnation is the real risk.
- D - Dane (Data): Intuition without data is guessing; data without reflection is blindness. I want both the numbers and the humans behind them.
- E - Empatia (Empathy): Trust is built in conversations, not dashboards. Radical candor, with care.
- R - Rozwój (Growth): Continuous learning, micro-habits, prompt libraries, and coaching. Skills compound.
And yes, AI really can be a partner to leaders: daily signal triage, lighter logistics, and clearer insight, while I keep the human judgment.
Five use-cases I recommend (and repeat)
- Patient Education Hub: top questions answered in readable, balanced pages, with clear support paths. It boosts confidence and reduces confusion.
- Modular HCP Email Factory: indication-scoped, reference-linked blocks sequenced via CRM; unsubscribes fall as utility rises.
- Field-Force Briefs: territory anomalies + next actions → better calls, better follow-ups, fewer admin hours.
- Reputation-safe SEO: search-informed content that puts risks front-and-center and speaks human.
- Pre-MLR QA Bot: catches balance gaps and off-label drift; makes compliance a speed enabler.
What I measure (so I can sleep at night)
- Outcome-proximate KPIs: comprehension and adherence proxies, not just CTR.
- Quality of engagement: HCP utility and alignment to guidelines; patient readability and confidence.
- Compliance efficiency: MLR cycle times, reuse rate of approved modules, false-negative rate of AI flags.
How I roll out AI in 30 days
Week 1 - Show, don’t preach.
Live demos inside tools people already use; set up redaction and source whitelists; pick three use-cases.
Live demos inside tools people already use; set up redaction and source whitelists; pick three use-cases.
Week 2 - Guardrails & gold standards.
Write prompts with acceptance criteria; build reference-locked templates for claims and risk; define what we never generate.
Write prompts with acceptance criteria; build reference-locked templates for claims and risk; define what we never generate.
Week 3 - Pilot & learn.
Shadow reviews; measure cycle time, error types, readability; hold a “mistake review” and fix the prompts.
Shadow reviews; measure cycle time, error types, readability; hold a “mistake review” and fix the prompts.
Week 4 - Ship & scale.
Publish the prompt/block library; turn on pre-MLR QA; schedule office hours so learning compounds.
Publish the prompt/block library; turn on pre-MLR QA; schedule office hours so learning compounds.
A short checklist I actually use
- Do our prompts/templates embed regional rules and PV hooks?
- Are the top patient questions answered in plain language and tested for readability?
- Do managers/reps start with a one-page signal brief and approved assets?
- Does pre-MLR QA auto-flag balance gaps and off-label drift?
- Are LIDER habits visible in how we work (safety, curiosity, data, candor, growth)?
Final word
If AI feels like one more block in the Tetris of your week, borrow my mantra from my book: don’t be afraid, experiment, ask, learn. Then run the Pharma Marketing playbook, patient-centric, compliant, measurable and steer with modern leaders, so the culture keeps pace with the code. That’s how I use AI to be more human at scale. The rest is just tooling.
Sebastian Cudny
Sebastian Cudny – a long-standing leader on the Polish market, a management practitioner with extensive experience in leading teams and driving change initiatives. Author of business and artificial intelligence books: “Artificial Intelligence Without Fear”, “Leader in the Age of AI”, and the forthcoming “The Modern Leader 4.0”. Creator of the proprietary LIDER model (People, Innovation, Data, Empathy, Development), which combines empathetic leadership with data-driven decision-making and practical applications of AI in a manager’s daily work. He has experience as a trainer and speaker, delivering webinars and workshops for companies and L&D teams. He regularly publishes on leadership, communication, and AI. His audience includes mid-level managers, HRBPs, and team leaders in both SMEs and large corporations. He emphasizes a practical approach: checklists, conversation scenarios, and tools that can be put to use as of tomorrow.
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