International Journal of Scientific Research and Engineering Development

The pharmaceutical industry faces compounding structural inefficiencies: average development costs of $2.8 billion per approved new molecular entity, a Phase II clinical failure rate of 92%, and $236 billion in revenue exposure from patent expiries through 2030. Despite AI-originated drug candidates demonstrating Phase I success rates of 80–81% versus 52% for conventionally discovered compounds, 42% of pharmaceutical AI initiatives fail to meet ROI targets — predominantly due to organisational rather than technological failures. No purposebuilt, evidence-weighted framework for guiding AI adoption in pharmaceutical drug discovery exists. This paper presents the Pharma AI Framework Suite, comprising three interconnected instruments: (i) the Pharma AI Readiness Framework (PAIR), scoring organisational AI readiness 0–100 across 7 evidence-weighted dimensions and 42 sub-indicators, including D5 — a pharma-unique dimension formally operationalising Collective Impulsive Skepticism (CIS) as a measurable barrier absent from all existing general AI readiness frameworks; (ii) the Drug Discovery AI Prioritisation Framework (DDAIP), assigning evidence-based Priority Scores to 20 value-chain activities across 8 weighted dimensions to produce a deployment heatmap and 2×2 strategic action matrix; and (iii) the AI Value Realisation Framework (AIVR), measuring post-deployment AI value across 4 domains and 24 KPIs to generate a quarterly Value Realization Score. Grounded in systematic synthesis of Cisco AI Readiness Index (2024–2025), IMD AI Maturity Index (2025), CB Insights Pharma AI Readiness Index (2025), FDA Draft AI Guidance (2025), and peer-reviewed clinical evidence, benchmark analysis of 14 organisations reveals that CIS — not data infrastructure or governance — is the universal binding constraint separating Advanced-tier from AI-Native pharmaceutical organisations.

Ranjan Chakraborty,"The Pharma AI Framework Suite: A Purpose-Built, Evidence-Weighted Methodology for Assessing Organisational Readiness, Prioritising AI Deployment, and Measuring Value Realisation in Pharmaceutical Drug Discovery and Development" International Journal of Scientific Research and Engineering Development, V9(3): Page(1837-1846) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.