The life sciences forecasting function stands at a critical juncture. While 100% of organizations we surveyed plan to integrate AI, ML, or GenAI into their forecasting workflows soon, current adoption remains surprisingly minimal. This striking paradox reveals an industry that’s poised for transformation. One that’s not held back by resistance, but by practical barriers that can be overcome.
The forces driving this inflection point are unmistakable. In rare diseases, there’s a shift toward stratified patient populations which demands unprecedented precision. Compressed launch timelines require faster, more agile forecasting cycles. And GenAI has lowered the barrier to entry, making sophisticated analytical capabilities accessible to teams of all sizes.
Forecasting teams continue to deliver remarkable results despite manual, Excel-based processes. They respond to executive questions quickly, make rapid strategic pivots, and preserve the transparency that billion-dollar decisions demand. So the issue now becomes: how to maintain these strengths while unlocking AI's potential.
This raises crucial questions:
- What's preventing widespread AI adoption?
- Where should organizations focus their transformation efforts?
- What practical steps can teams take now to begin capturing value?
Throughout 2025, we surveyed 28 forecasting practitioners across 19 pharmaceutical and biotechnology companies, including both Big Pharma (71%) and emerging biotech (29%). These practitioners span organizational levels from managers to VPs, covering global, US, and international teams with forecasting scopes ranging from BD assessment through inline products.
Unlike theoretical analysis, this report captures the real challenges, priorities, and practices of forecasting teams navigating this transformation. The insights are grounded in practitioner experience and immediately actionable.
This report will show you:
- The 100% paradox, and what it means: Discover why universal interest in AI hasn't translated to adoption, and how this gap reveals readiness constrained by practical barriers rather than philosophical resistance.
- Current state capabilities and challenges: Understand how teams achieve remarkable responsiveness despite Excel-based workflows, and why standardization remains elusive regardless of company size or resources.
- The AI adoption landscape: Learn which forecasting assumptions are ripe for AI augmentation, and why "forecasting-ready data" and transparency requirements shape technology adoption decisions.
- Three strategic pillars for transformation: Explore the practical frameworks for strengthening innovation governance, reimagining data infrastructure, and building the culture needed for sustainable AI integration.
- Quick wins you can implement now: Identify five low-risk, high-value initiatives that deliver immediate benefits without requiring comprehensive transformation or replacing existing Excel workflows.
Why this report matters now:
The transformation window is open. Organizations that act now, while barriers are practical rather than fundamental, will gain compounding competitive advantages in decision speed, risk intelligence, resource optimization, and strategic positioning. Those who wait risk falling behind as the industry gap widens.
Big Pharma and emerging biotech face remarkably similar obstacles. Team size doesn't determine success, strategic focus does. The insights in this report apply whether you're a five-person team or a 30+ person global function.
The path forward is clear. Despite low current adoption, the roadmap exists. Organizations needn't wait for perfect conditions or comprehensive infrastructure. Quick wins are available now, and strategic initiatives can be sequenced for manageable transformation.
What you'll walk away with:
- Benchmarking data showing where your peers stand on AI adoption, team structure, and capabilities
- Root cause analysis of why the adoption gap exists and how to close it
- Strategic framework covering governance, data infrastructure, and culture
- Practical roadmap for both immediate quick wins and longer-term transformation
- Demographic insights comparing challenges across company types and team sizes
- Peer perspectives on the role of forecasters as "shepherds" guiding strategic decisions
This report is essential for:
- Forecasting Leaders planning AI integration strategies
- Commercial Analytics Teams seeking benchmarking insights
- Finance Leadership evaluating forecasting function transformation
- Data & Technology Teams understanding forecaster requirements
- Strategy & Portfolio Teams optimizing decision-making processes
Understand where the industry stands, what's holding teams back, and how to chart a practical path forward that preserves forecasting's core strengths while unlocking AI's transformative potential. For a detailed conversation around how these insights apply to your specific forecasting challenges, organizational context, and transformation priorities, request a customized readout with our forecasting and analytics experts.
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