A decade of active M&A left large pharmaceutical companies with a tangled mess of disparate technology platforms and data standards. The immense difficulty of integrating these acquisitions became a primary catalyst for investing in unified, scalable data foundations and modern IT infrastructure.
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Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.
The nature of biopharma M&A changed dramatically in a year. After a period with no deals over $5 billion, there are now seven or eight such transactions, reflecting a pivot by large pharma to acquire de-risked assets with large market potential to offset looming patent expirations.
The pharmaceutical industry's historically high profitability created a lack of urgency for technological innovation beyond basic ERP systems. It wasn't until patent cliffs and messy M&A integrations squeezed margins that companies began seriously investing in modern data platforms and cloud infrastructure to improve efficiency.
Many pharma companies have breakthrough AI results in isolated functions, or "pockets of excellence." However, the ultimate competitive advantage will go to the company that first connects these disparate successes into a single, integrated, enterprise-wide AI capability, thereby creating compounded value across the organization.
Vanguard's ability to adopt AI was a direct result of a prior project to consolidate its fragmented "marketing tech pile" into a unified platform. This foundational investment was the critical enabler, proving that future innovation depends on present-day infrastructure cleanup.
Before deploying AI across a business, companies must first harmonize data definitions, especially after mergers. When different units call a "raw lead" something different, AI models cannot function reliably. This foundational data work is a critical prerequisite for moving beyond proofs-of-concept to scalable AI solutions.
The pandemic acted as an unavoidable wake-up call, compelling the slow-moving pharmaceutical industry to rapidly adopt digital engagement models and embrace a more agile, customer-focused commercial approach, achieving in one year what would have taken ten.
Recognizing their lag in technology adoption, pharmaceutical companies are now recruiting executives from consumer goods (CPG) and retail. These industries have a more mature approach to data and customer-centricity, and pharma aims to inject this DNA into its traditionally conservative corporate culture.
Current AI-health partnerships are just the prelude. The next grand strategic move for Big Tech will be to acquire major pharmaceutical companies, which represent a far larger and more impactful market than media.
Large enterprises inevitably suffer from "data sprawl," where data is scattered across on-prem clusters, multiple cloud providers, and legacy systems. This is not a temporary problem but an eventual state, necessitating tools that provide a unified view rather than forcing painful consolidation.
