How to Virtually Launch a Biopharmaceutical During Covid-19
.avif)
Based on unmet need and inbound physician inquiries, your team has decided to launch a new biopharmaceutical right now - during a pandemic. With a different molecule or indication, you may have made a different decision. But given your current circumstances, you have decided to move forward with launch.
So, what’s next? How do you do it? What is different from a typical launch provided the current environment and projections over the next three to twelve months?
To be successful, you’ll need to create a flexible launch plan that is adaptable to changing market scenarios. As part of the plan, you’ll need to redefine your customer segmentation, which includes public health and economic factors; create new marketing resources that reflect an entirely virtual customer and patient journey; rapidly upskill your field team on virtual engagement skills; and run virtual launch meetings that effectively drive outcomes despite constraints.
A Launch Plan that Adjusts to Market and Public Health Triggers
While you’ve decided to move ahead with the launch, your execution plan should anticipate new developments in the healthcare ecosystem and remain agile to respond to those scenarios. First, you’ll need to identify these scenarios and the appropriate responses. To do this, war game various economic and public health conditions with your cross-functional team to determine how to move forward within different possible operating environments.
Data from past launches will likely hold little insight into your upcoming launch, and there will be a paucity of reliable data for some time. As a result, your war gaming models will likely need to rely on “what-if” analyses grounded in the shared expertise of your team. General economic forecasts and the epidemiological model of your choice can provide a firm starting place but should include outlier scenarios to provoke your launch team’s best thinking.
Based on this scenario planning, you can then identify alternative launch plans that can be implemented based on external triggers. This gives your team the flexibility to operate under ongoing uncertainty and alignment on what to do in each scenario.
Reprioritize Regions, Accounts and Customers Based on Economic and Public Health Factors
Given your launch plan, your sales team will need to re-segment their customer base to include factors beyond the appropriate patient population, coverage and prescriber affinity. New factors to include will be Covid-19 cases within a territory, cases within an account, the effect of the pandemic on physician load (depending on specialty they may be experiencing a surge or lack of patients), unemployment claims within territory (coverage changes will reduce new patient starts and adherence), and virtual access to prescribers.
This reprioritization could lead to a wholly different initial focus for your field organization, potentially away from academic institutions and toward community-based clinics. Physicians and patients alike may be quickly moving to lower-risk treatment locations.
Redesign Marketing for Entirely Virtual Customer and Patient Journeys
The status quo approach to designing physical core sales aids and leave behinds needs to be completely replaced by digital assets and messaging. The first step is rapidly translating existing assets to virtual and gaining approval for electronic dissemination. However, your marketers will also need to rethink their approach and create “digital first” assets to support your field team.
On the consumer and patient side, they will have limited access to their clinicians and thus will turn to online resources and education more than ever before. Your patient-facing teams will need to meet that upswell of demand with new and engaging resources that move them along their patient journey.
KOL and Speaker Bureau engagement will also need to become digital, providing opportunities to engage during live sessions, connect with peers, and share insights firsthand—potentially allowing for real world data capture.
Rapidly Build Your Field Team Members’ Virtual Engagement Capability
Field team members, from salespeople to account management, medical affairs, patient advocacy, and field reimbursement, are highly skilled in face to face interactions. Given the nature of their roles, their technical proficiency often lags behind that of home office employees. They will need to be rapidly upskilled on first the basics of using virtual communications platforms like Zoom, Veeva Engage, and Google Meet, as well as the key differences between physical and virtual interactions.
Next, they will need to begin building the capabilities to change provider behavior using communication platforms and the new interactive tools being created by marketing. Optimal use of these tools will allow for the capture of real time data, providing insights to help marketing be more agile.
This upskilling can happen mostly asynchronously, through peer collaboration and with coaching from your existing field training team. Successful field adoption will be a factor of execution tools that are easy to digest, readily on-demand, and emphasize practice and outcomes.
Virtual Launch Meetings that Drive Impact
The best face to face launch meetings convey best practices and ideas to the field team, allow for opportunities for deep practice, and create a sense of purpose and teamwork on behalf of patients. The best virtual launch meetings can do this too—but with new constraints and opportunities. With much of your field team responsible for caregiving demands at home, there is no need for launch meetings to be six hours a day for four days in a row. In fact, for many people, that is impossible while children are out of school.
With diminishing returns and screen fatigue, it’s important to experiment with the structure, duration, and modality of these virtual gatherings. Maintain enthusiasm throughout the meeting by keeping days shorter. Prioritize personal application time and small work groups.
Beyond the agenda, new virtual platforms allow for peer best practice sharing, foster interaction through polling and live Q&A, and replicate the practice sessions that keep the commercial team engaged and energized.
Create a wraparound experience by sending branded swag ahead of time and providing meal delivery during the event. Set aside time for carefully designed virtual networking to encourage new connections and organic relationship building. Done well, virtual launch meetings can educate, upskill and motivate your team in different but equally effective way as the face to face meetings of the past.
Launch and Learn Fast
Thousands of people’s efforts and expertise go into launching a pharmaceutical product, and it’s hard to get it right during the best of times. During the Covid-19 era, you’ll be prepared to succeed by creating a flexible launch plan that shifts based on market and public health conditions; reprioritizes regions, accounts and customers based on new economic and pandemic related factors; encourages your marketing team to innovate on their digital approach; gets the field ready to engage virtually; and drives results through a wholly new approach to virtual launch meetings.
These are some things you can plan and predict. Once you begin to engage customers, your initial meetings will be fact finding missions that should inform your approach as fast as possible. Expect to be wrong about the future fairly often. It’s always an honor to serve patients, and during this ongoing public health crisis, this is truer than ever, and the stakes are even higher. Stay humble, be prepared to be wrong, and get ready to learn fast.
Related content
Related content
Global spending on AI is forecast to reach $2.52 trillion by 2026, a 44% year-over-year increase, according to Gartner. At the same time, only about 10% of AI pilots scale beyond proof of concept.
What’s the disconnect?
Why aren’t most organizations seeing the ROI they hoped for, despite making such large investments?
It’s not because the technology isn’t ready. And it’s not because the use cases are unclear.
The disconnect exists because many organizations are investing in AI as a technology upgrade and expecting a business transformation in return.
The tools are advancing at breathtaking speed, and most organizations already have AI in motion. But the work itself often stays the same. AI gets layered onto existing tasks instead of being used to rethink workflows end to end. Adoption metrics go up, while decisions, operating models, and value creation remain largely untouched.
When teams first start using AI, they do what makes sense. They try to recreate today, just faster. Can it help me write this? Analyze that? Save a bit of time?
That’s a smart place to begin. But it’s not where ROI, or reinvention, actually shows up.
Getting over the hump
Real returns begin when teams experience what we often call “getting over the hump.”
This is the moment when two things click at once:
- AI can fundamentally change how work gets done.
- People don’t need deep technical expertise to make that change happen.
When teams see weeks of work compress into hours, or watch an end-to-end workflow suddenly run in a new way, something shifts. Confidence replaces hesitation. Curiosity replaces caution. The questions change, from “How do I use this tool?” to “What’s possible now?”
That shift matters, because ROI doesn’t come from using AI more often, it comes from using it to work differently.
Why ROI stalls as AI scales
As AI initiatives expand, many organizations discover that the limiting factor isn’t the technology itself. It’s the environment surrounding the work.
ROI shows up when teams are able to explore and redesign workflows, not just automate steps. That requires clarity on outcomes and guardrails, but also room to experiment, learn, and iterate. When AI is tightly controlled or narrowly deployed, pilots stay pilots. When people are trusted to rethink how work happens, value starts to compound.
Organizations that unlock ROI don’t chase perfect use cases upfront. They focus on learning faster and applying those insights where they matter most.
The early signal that ROI is coming
Long before AI shows up in financial results, there’s an earlier indicator that organizations are on the right path.
People are energized by the work.
You see it when teams start sharing experiments, when ideas move across functions, and when learning becomes visible rather than hidden. Progress feels owned, not imposed.
That energy isn’t accidental. It’s a signal that people feel trusted to rethink how work happens, and that trust is essential to turning investment into impact.
Reinvention happens closer to the work than most expect
AI reinvention rarely starts with a sweeping rollout or a multi-year roadmap. More often, it begins with one meaningful workflow, one team close to the work, and a willingness to ask a different question.
With the right support, that team gets over the hump. What they learn becomes reusable. Patterns emerge. Over time, those insights connect, creating enterprise-wide impact and sustained ROI.
That’s how organizations move from isolated pilots to real returns.
What this means for AI investment
No organization feels fully “caught up” with AI, and that’s true across industries.
The organizations that will realize ROI aren’t waiting for certainty or the next breakthrough tool. They’re reinvesting their AI spend into new ways of working that scale human potential alongside technology.
Handled thoughtfully, AI doesn’t distance people from the work. It brings them closer - to better decisions, stronger collaboration, and better outcomes.
For many organizations, that’s where the real return begins.
Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.
After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.
What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.
For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.
AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.
When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.
- What specific market problem does this solve, and what is it worth?
- Is this capability genuinely unique, or is it rapidly becoming commoditized?
- What is the true total cost - not just to build, but to run and evolve over time?
- What is the current pace of innovation for this niche?
For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.
Why build versus buy is the wrong starting point
One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.
A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.
In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.
The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.
The issue is rarely technical. It is temporal.
Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.
Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.
Licensing as a way to buy time and insight
In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.
The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.
Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.
AI is neither a silver bullet nor a feature
Coaching is a useful microcosm of the broader AI debate.
Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.
Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.
At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.
That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.
The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.
A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.
What this means for leaders
Across all of these examples, the same pattern shows up.
The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.
Building from scratch only makes sense when you can clearly articulate:
- What you believe that the market does not
- Why that belief creates defensible value
- Why you’re willing to concentrate risk behind it
Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.
Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.
In these environments, strategic clarity, not headcount or spend, creates advantage.
This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.
This article was originally publish on Rotman Management
IN OUR CONSULTING WORK with teams at all levels—especially senior leadership—my colleagues and I have noticed teams grappling with an insidious challenge: a lack of effective prioritization. When everything is labeled a priority, nothing truly is. Employees feel crushed under the weight of competing demands and the relentless urgency to deliver on multiple fronts. Requests for prioritization stem from both a lack of focused direction and the challenge of efficiently fulfilling an overwhelming volume of work. Over time, this creates a toxic cycle of burnout, inefficiency and dissatisfaction.
The instinctive response to this issue is to streamline, reduce the number of initiatives, and focus. While this is a step in the right direction, it doesn’t fully address the problem. Prioritization isn’t just about whittling down a to-do list or ranking activities by importance and urgency on an Eisenhower Decision Matrix; it also requires reshaping how we approach work more productively.
In our work, we have found that three critical factors lie at the heart of solving prioritization challenges: tasks, tracking and trust. Addressing these dimensions holistically can start to address the root causes of feeling overwhelmed and lay the foundation for sustainable productivity. Let’s take a closer look at each.