Authored by Ben Rigby, Associate Vice President and Louis Martin, Associate Director at Prescient.
From Strong Portfolios to Uneven Commercial Outcomes
You are six months from launch. The strategy deck is polished, the positioning is clear, and leadership is aligned. And yet, somewhere in the room, there is an unspoken question: are we actually confident in this – or just comfortable with it?
It is a question worth sitting with. Many pharmaceutical portfolios look compelling on the eve of launch – assets with meaningful efficacy, an acceptable safety profile, and a navigable regulatory pathway. On paper, the fundamentals are sound. Yet commercial performance frequently tells a different story. Uptake stalls. Positioning fails to resonate. Assets that appeared primed for success struggle to realise their anticipated contribution to portfolio value. In fact, it’s estimated that one-third of clinically differentiated launches fail to meet expectations three years after launch.
The science is rarely the problem. More commonly, the strategy is built around a version of decision-making that hardly ever exists in practice – one where stakeholders carefully weigh evidence, respond to differentiation, and change behaviour when presented with a compelling case. In practice, decisions work differently. Technically credible products routinely underperform not because they lack merit, but because merit alone is rarely enough to move behaviour at scale.
Commercial Decision-Making on Autopilot
One of the most underappreciated challenges in commercialisation is that many real-world decisions are made with remarkably little conscious deliberation. Familiarity, precedent, and perceived safety tend to dominate – not because decision-makers are irrational, but because efficiency demands it. In practice, many decisions default to intuitive, heuristic-driven processes rather than deliberate evaluation, consistent with System 1 Thinking as described by Daniel Kahneman.
This creates two critical implications for commercial strategy.
First, strategies built on the assumption of fully rational, deliberate decision-making consistently overestimate their own impact. Even robust, well-communicated evidence can struggle to shift behaviour when it conflicts with instinctive or habitual responses.
Second, strategy teams themselves are not immune to the same cognitive dynamics. Strategic choices are frequently shaped by instinct, precedent, and internal narratives as much as by rigorous analysis. Certain positions feel intuitively “safe.” Certain assumptions go unchallenged. Certain options are quietly ruled out before they are ever formally considered.
When commercial strategies are developed on autopilot, they risk mirroring the very behavioural patterns they are designed to overcome – and the blind spots go unnoticed until they surface as underperformance.
Elevating Strategic Thinking Through AI‑Augmented Exploration
This is precisely where AI-augmented strategy development can deliver meaningful value.
AI creates space for more rigorous strategic conversations by reducing the time spent assembling information and increasing the time spent interpreting it. As highlighted in a piece authored by Stephanie Hall and Suzie Denton at Uptake (part of Prescient), the advantage comes from combining AI-driven insight generation with human-led strategic judgement, rather than replacing it.
The risk is not that teams lack insight – it is that they converge too quickly on a single, internally coherent view of the world. AI addresses this by simulating commercial scenarios and revealing how different strategic choices play out under real-world conditions. Instead of locking into a single narrative, teams can test multiple credible paths, challenge their assumptions, and confront trade-offs before committing.
Used in this way, AI elevates strategic thinking across three dimensions:
- Making assumptions explicit – surfacing where confidence is rooted in instinct rather than evidence, and prompting teams to examine what they are taking for granted.
- Exploring genuine strategic alternatives – enabling teams to test materially different approaches and land on what they won’t do (often as important as deciding what they will do).
- Stress-testing against real-world constraints – examining how strategies hold up when access barriers, competitive responses, and behavioural inertia are factored in, not treated as footnotes.
The goal is not to replace human judgment. It is to enable more deliberate, transparent, and defensible strategic choices.
Five Principles for Stronger Commercialization Strategy
For organisations seeking to close the gap between portfolio potential and commercial performance, five principles consistently differentiate high-performing launches:
- Anchor strategy in behaviour change: Define success in terms of what needs to shift in real-world decision-making – not simply how the product compares on paper.
- Focus early on what it takes to change decisions: Concentrate effort on the specific points where evidence or differentiation shifts stakeholder behaviours.
- Challenge default thinking: Make explicit where internal narratives, precedent, and familiarity are shaping strategy – often unconsciously – and test whether they hold under scrutiny.
- Use AI to generate (not just validate) strategic options: Use AI to explore materially different commercial paths and expose what the dominant narrative may be missing.
- Make portfolio trade-offs explicit: Define where to invest, where to accept similarity, and where to take risks.
By forcing greater clarity on choices and reducing reliance on implicit, “autopilot” assumptions, AI-augmented strategy development brings trade-offs into the open. The goal is not a single “correct” answer, but better, earlier decisions that determine whether portfolio potential is realised or lost.
Reference:
https://www.zs.com/insights/build-a-pharmaceutical-launch-strategy-and-operating-system
The Uptake Launch Excellence Report 2026
