Case Study:
AI to Pressure Test Strategy
How AI is helping pressure test Pharma strategy
In an industry where a single strategic misstep can cost hundreds of millions of dollars and years of development time, pharmaceutical companies have long relied on experience, intuition, and limited data sets to make critical business decisions, lacking a quantifiable method to truly pressure test strategic assumptions before committing significant resources. Traditional strategy development, while thorough in its use of available information, has suffered from the gap between strategic planning and outcome prediction, contributing to the industry’s notorious high failure rates and inefficient capital allocation. Enter artificial intelligence as a game-changing solution: by harnessing the power of natural language processing and machine learning to analyze vast repositories of both internal and external data—from clinical trial results and competitive intelligence to regulatory filings and real-world patient outcomes—pharmaceutical companies can now subject their strategies to rigorous, data-driven stress testing before implementation. This approach represents a fundamental shift from reactive strategy adjustment to proactive strategy optimization, offering the potential to transform pharmaceutical strategic planning from an educated gamble into a precision-guided process with measurable confidence intervals and predictable outcomes.
The Client
- Strategies are created with all available data but there is no quantifiable way to pressure test them.
The Solution
- Taking real world data and outcomes, and internal data allows a far more comprehensive analysis of strategies and their likely outcomes using AI (NLP and ML together). Data types included a subset of these but the aim is to add more of these:
- Internal data – Previous results data, Government filings, Consulting reports and white papers, Clinical research findings, Competitive intelligence, CRM data, Call centre data, Market research data on patients, Market research data on target physicians, Market research data on target payers, Complaint data, Sales force effectiveness metrics and territory performance data, Pricing elasticity studies and discount/rebate analysis, Budget allocation and ROI tracking across therapeutic areas, Manufacturing cost data and capacity utilization, Partnership and licensing agreement performance metrics, Supply chain disruption patterns and vendor performance, Regulatory submission timelines and approval success rates by indication, Clinical trial recruitment rates and site performance metrics, Quality control data and manufacturing deviation reports, Project management data showing resource allocation and timeline adherence, Key opinion leader (KOL) engagement scores and influence mapping, Sales rep productivity metrics and physician relationship strength, Employee turnover patterns in critical roles, Training effectiveness data and competency assessments, Internal innovation pipeline and R&D productivity metrics, Website analytics and digital engagement patterns, Patient portal usage and adherence monitoring data, Mobile app performance and patient interaction data, Social media sentiment analysis from owned channels, IT system performance and digital transformation metrics, Legal case outcomes and settlement patterns, Audit findings and corrective action effectiveness, Environmental, social, and governance (ESG) performance metrics, Intellectual property portfolio strength and patent cliff analysis, Pharmacovigilance data and adverse event reporting patterns
- Public data – Media articles, Public filings, Corporate PR announcements and annual reports, Corporate website statements, Industry-specific websites, Government freely available data, Commercial Data, Claims data, EHR/EMR, Disease registries, Sensor data from devices, Physician and Patient associations, Think tank publications, Commercial industry newsletters, Investment banking research reports, FDA/EMA advisory committee meeting transcripts and voting patterns, Regulatory guidance documents and draft policies, Congressional hearing transcripts on healthcare policy, CMS coverage decisions and reimbursement determinations, International regulatory harmonization documents (ICH guidelines), PubMed abstracts and full-text research publications, Conference abstracts from major medical meetings (ASCO, ASH, etc.), Clinical trial registries (ClinicalTrials.gov, EudraCT), Patent filings and patent landscape analysis, FDA Orange Book and Purple Book listings, Healthcare spending trends and budget allocations by country, Insurance formulary coverage decisions and tier placements, Pharmacy benefit manager (PBM) formulary changes, Healthcare provider consolidation patterns, Venture capital and biotech funding trends, Patient advocacy group communications and policy positions, Healthcare provider social media discussions and sentiment, Medical journal editorial content and expert commentary, Healthcare conference presentations and panel discussions, Physician rating sites and patient experience platforms, Earnings call transcripts and investor presentation materials, Trade association reports and industry white papers, Consulting firm reports (when publicly available), Academic business case studies, Healthcare technology adoption surveys and reports, Medicare/Medicaid claims databases (when available), Disease foundation patient registries, Health system quality reporting data, Pharmaceutical pricing transparency reports, Drug shortage databases and supply chain alerts
The Outcome
- The ultimate outcome is transforming strategy development from an art into a science, dramatically improving the clients’ success rates in an industry where strategic missteps are extremely costly.
- Specific outcomes were:
- Enhanced Strategic Confidence: By combining internal performance data with comprehensive external market intelligence, the client gained quantifiable validation of their strategic assumptions, replacing gut-feel decisions with data-driven confidence scores.
- Early Risk Detection: The AI system identified potential strategy failures before implementation by spotting patterns in competitor outcomes, regulatory trends, and market dynamics that human analysis might miss, potentially saving millions in misdirected investments.
- Competitive Advantage Identification: By analyzing competitor filings, clinical results, and market positioning alongside internal capabilities, the system revealed strategic gaps and opportunities that aren’t apparent through traditional analysis.
- Improved Resource Allocation: The comprehensive data analysis provided clear prioritization metrics, helping companies focus resources on strategies with the highest probability of success based on historical patterns and current market conditions.
- Regulatory and Market Timing Optimization: By incorporating government data, regulatory trends, and market dynamics, the AI predicted optimal timing for strategy execution, potentially improving launch success rates and market penetration.
- Measurable Strategy Performance: Unlike traditional strategy development, this approach provided ongoing quantitative assessment, allowing for real-time strategy adjustments based on emerging data patterns
To achieve these kinds of results, contact Eularis today.
Latest News
Read our latest blogs here.
Pharma’s Patent Cliff is Here. Why AI Strategy Matters Now More Than Ever.
The pharmaceutical industry is entering a decisive period. Over the next several years, many of its most valuable brands will lose exclusivity, putting a substantial
The Hidden Cost of Having No AI Strategy (Spoiler: It’s Not What You Think)
In the past few years, almost 70% of biopharma companies have initiated AI initiatives. Yet, fewer than 20% report a cohesive AI strategy at the
Are LLMs Already On Their Way to Being Obsolete?
The AI world is racing forward with near-religious confidence in large language models, yet one of the field’s most influential pioneers is already drafting their