Faisal Zain is a seasoned visionary in the medical technology and pharmaceutical sectors, recognized for his ability to dismantle the traditional boundaries between manufacturing, diagnostics, and commercial strategy. With 2026 ushering in a new era of agentic AI, he has become a leading voice in urging life sciences to move beyond simple automation toward a total reimagining of the commercial function. His work focuses on integrating the lean, outcome-driven agility of digital health into the complex machinery of global pharmaceutical organizations, ensuring that innovation results in meaningful patient outcomes rather than just faster spreadsheets.
In this discussion, we explore the structural evolution of commercial teams, the transition from speed-based AI to transformative AI, and the necessity of adopting a “digital-first” operating rhythm. We also delve into the future of healthcare provider engagement and the strategic importance of a unified data layer to replace the fragmented systems that currently define the industry.
Commercial departments often operate in silos like field, marketing, and market access, creating a fragmented approach to the market. How does this “complex ball of yarn” structure hinder innovation, and what can be learned from the leaner models found in digital health?
The current state of pharmaceutical commercial teams is often a sprawling web of separate threads where the marketing team pores over agency data on tactic performance while sales teams are out in the trenches, battling daily for a mere two to five minutes of a healthcare provider’s attention. Even in 2026, we see “SWAT teams” that swoop in for a launch and then dissipate, leaving behind a trail of disconnected digital tools and unwieldy data that the field force struggles to use. This siloed approach creates a massive amount of friction; for instance, a rep might be handed a new AI-driven “next best action” tool, but because it is bolted onto the same static quarterly system that has existed for years, it fails to address any fundamental underlying model improvements. Digital health companies, by contrast, have grown up leaner because they have to, building consolidated functions that focus on specific interventions rather than spreading themselves thin across entire populations. By adopting this consolidated mindset, pharma can move away from dozen sub-functions each optimizing their own small area and instead align everyone toward a handful of clear, high-impact patient outcomes.
You have often compared pharma’s current use of AI to the early days of the internet when newspapers simply digitized their physical copies. In what ways are we focusing too much on speed and not enough on redefining the realm of commercial possibility?
There is a distinct difference between doing things “quicker” and doing things “differently,” and right now, most of the industry is stuck in the former. While R&D teams are using AI to discover what is possible in drug development, commercial teams are primarily talking about faster call planning, faster reporting, and faster content approval. We are essentially re-creating existing ways of working—taking the “physical copy” of our 2010s-era commercial model and just making it digital. This agentic AI we are seeing now shouldn’t just be about efficiency; it should be a forcing function that makes us ask if the commercial function would even look like this if we designed it from scratch today. We need to stop focusing on the speed and efficiency coin and start looking at how AI can change the actual operating rhythm of the entire portfolio, allowing us to think about commercial possibility in a way that prioritizes the patient journey over the internal approval chain.
Digital health teams often “ship” new features or concepts in a matter of days or weeks based on usage patterns, while pharma can take an entire quarter just to gather requirements. What shifts in operating rhythm are necessary to move from a static quarterly cycle to a real-time production model?
The discrepancy in speed is staggering when you consider that a digital health team can identify a usage pattern and ship a feature to production in weeks, while a pharma commercial team is often bogged down by organizational fatigue and sequential approval processes. To close this gap, pharma must borrow the “radical focus” of digital health and pair it with its own inherent strength in regulatory and compliance expertise. This means moving away from the sprawl of sub-divisional dashboards and instead adopting a model where we ship concepts to production in days rather than months or years. It requires a fundamental shift in how we view our work—not as a series of rigid, quarterly milestones, but as a continuous, living cycle of engagement and improvement. When you reduce the time it takes to go from insight to action, you create the opportunity to expedite patient outcomes in a way we have never seen before, turning the “juggernaut” of big pharma into a much more responsive and agile entity.
The traditional sales model relies on securing very short windows with busy healthcare providers on a static cycle. How could a “cognitive command center” and platforms like OpenEvidence replace this outdated cycle with more meaningful engagement?
The traditional model of dispersing reps to chase five-minute windows is increasingly out of step with how healthcare providers actually want to interact with industry information. Instead of this static cycle, we should be investing in a “cognitive command center” powered by AI that operates across functions rather than within them. Imagine a system that flags a stalled account to market access, content, and the field team simultaneously, allowing for a coordinated response instead of routing a request through three separate, sequential approval chains. Furthermore, we should be shifting our investment toward fewer but much richer touchpoints, such as deep-dive partnerships and twice-yearly high-impact events. We need to meet healthcare providers where they already are, utilizing AI-native platforms like OpenEvidence that clinicians are choosing to use on their own terms. This shift isn’t about spending more; it’s about spending the existing commercial budget on a model that prioritizes deeper, more meaningful partnerships over repetitive, low-value interactions.
Many pharma companies prioritize building internal solutions before looking at external partnerships, often leading to wasted time. Why is the “partner-first” mentality so critical for the future, and how does a single shared data layer support this?
Digital health companies build with partners from day one because they lack the massive internal resources of a global pharma giant, and this necessity has become their greatest strength. Pharma often plans in-house first and only looks for a partner once internal options are exhausted or the “build” is already nearly complete, which is a recipe for obsolescence in a fast-moving market. A more effective approach would be to replace the current sprawl of separate systems with one shared data layer—a single source of truth that tracks HCP engagement, patient journeys, and pipeline data with layered access rights. This would allow a company to plug in best-in-class external partners seamlessly into their ecosystem from the start. By moving to a single data layer, you eliminate the need for entirely separate systems per function, allowing the entire organization to see the same picture of the market and react in unison rather than in isolation.
What is your forecast for the evolution of the pharmaceutical commercial model through 2028?
Between 2026 and 2028, we will see a Great Decoupling in the industry: the companies that treat AI as a tool to redesign their entire commercial model will pull significantly ahead of those who only use it to streamline their current, siloed functions. I expect the “field force” as we know it to transform into a high-level strategic partnership unit supported by a cognitive command center that handles the bulk of routine engagement and data routing. We will see the end of the “digitized paper copy” era, as firms finally abandon 2010s-style job descriptions in favor of cross-functional roles that prioritize agility and real-time data integration. Ultimately, the winners will be the organizations that stop trying to make the old machine run faster and instead build a new, leaner engine that is built for the complexity and speed of the modern healthcare landscape.
