Digital Pharma: AI, Data Governance and Value Chain Modernization

The pharmaceutical industry is facing a historic crossroads: maintain heavy, highly regulated processes or accelerate toward a digital, agile, data-driven value chain. This is not a technological whim but a competitiveness requirement spanning from drug discovery to commercialization and pharmacovigilance, already generating tangible economic and operational impact.

Much of today’s interest centers on generative artificial intelligence. According to studies by McKinsey & Company, Gen AI applications could contribute up to US$110 billion annually to the pharmaceutical sector. But the leap is neither universal nor instantaneous, as the industry faces the significant challenge of scaling the technology across its operations.

Telemonitoring tools, wearable devices, digital health platforms, and real-world patient data solutions are maturing and being incorporated into decentralized clinical trials as well as commercial strategies. Industry reports show sustained growth in the digital health ecosystem and highlight how pharmaceutical companies can accelerate study recruitment, monitor adherence, and enrich real-world evidence.

Consulting firms and analysts emphasize that digitalization is a priority in executive plans for 2026: cloud, advanced analytics, regulatory process automation, and integrated data platforms rank among the top bets. For senior leadership, the question has shifted from “why?” to “how do we govern and scale safely?,” a critical point in a sector where traceability and document validation are mandatory.

From a market perspective, enterprise digitalization is a massive phenomenon. The global digital transformation market (across industries) will reach US$1.86 trillion by 2031, according to the report “Digital Transformation Market by Solution (Customer Experience, Process Automation Platform), Services (Application and Infrastructure Modernization), Transformation Focus Area (Financial, Operational, Workforce Transformation) – Global Forecast to 2031” published by MarketsandMarkets.

This reflects the scale of investment available to modernize value chains, including pharmaceutical ones. This capital flow enables cloud providers, data platforms, and consulting firms to develop life sciences-specific solutions. Modernizing pharmaceutical value chains requires profound restructuring: it is no longer enough to manufacture and distribute medicines using traditional processes; digital technologies such as artificial intelligence, big data, the Internet of Things (IoT), digital twins, and traceability systems are now integrated throughout the chain, from production to final distribution, to provide greater visibility, efficiency, quality control, and resilience.

Thanks to these advances, pharmaceutical companies can anticipate demand, optimize inventories, reduce waste, monitor in real time the condition of sensitive products, such as vaccines or biologic therapies that require refrigeration, and respond more quickly to logistical disruptions.

Additionally, digitalization enables information to be linked across the entire product life cycle, from design to delivery, improving data governance, regulatory compliance, traceability, and cross-department coordination, thereby shortening time-to-market and boosting operational efficiency. This shift not only increases internal efficiency but also strengthens the industry’s ability to respond quickly to global challenges such as quality assurance, variable demand, and increasingly strict regulations.

However, one of the main challenges the pharmaceutical industry faces is information management, as data is often dispersed across silos, from research and production to patient information, preventing a unified view of the patient’s “digital history” and complicating the delivery of personalized services. The information handled by the industry is highly confidential, so implementing digital solutions requires robust cybersecurity standards, encryption, access control, anonymization when applicable, and regulatory compliance (privacy, health regulation, traceability).

Complicated interfaces, lengthy procedures, and validation requirements can lead patients or physicians to reject using apps, portals, or platforms. On the other hand, when systems cannot communicate, the user experiences a fragmented journey, as many pharmaceutical companies depend on legacy systems not designed to integrate with new tools. This dependency complicates the incorporation of modern solutions (AI, mobile apps, digital health platforms) and results in a disjointed experience.

What are the main barriers and risks the industry faces in this regard? First, the talent gap: data scientists, ML engineers, and digital regulation experts are scarce and compete with other industries. Second, data governance: models learning from clinical records or literature must be implemented with controls that prevent bias and ensure reproducibility. Third, the regulatory environment: health agencies are adjusting guidelines for medical software and AI-assisted decisions, requiring pharmaceutical companies to invest in compliance-by-design. Finally, organizational culture, which drives real adoption among scientists, internal regulatory teams, and commercial units, is often slower than the technology itself.

With respect to the regulatory framework, on Feb. 28, 2024, a federal bill to regulate AI was presented before the Senate. This proposal classifies AI systems according to their level of risk and establishes that systems considered “high risk” would be subject to evaluations, testing, and human supervision before deployment. Consequently, AI systems in health (developers and providers of AI systems for healthcare) must ensure the protection of sensitive data, register with the federal health authority, and submit their systems to security controls, supervision, evaluation, and potential suspension or cancellation if they fail to comply.

Even with these initiatives, there is growing consensus among specialists and authorities that regulation must be designed carefully: the goal is not “only” to control AI but to encourage its ethical and responsible use, an especially sensitive requirement in areas such as healthcare, where the risks associated with diagnostic errors or automated decision-making are real.

For the private sector and investors, digital transformation sends two clear signals: in the short term, it can improve operational efficiency; for example, reducing clinical trial timelines and automating documentation. In the medium term, it redefines sources of competitive advantage, such as the ability to generate evidence, personalize patient services, and accelerate the pipeline.

The arrival of agentic AI marks a turning point for the pharmaceutical industry, which is moving toward a more transparent and results-oriented environment. Companies that succeed in governing their data, scaling AI responsibly, and comprehensively modernizing their value chains will be the ones that define leadership in the coming decade. More than adopting new tools, the challenge lies in integrating them with scientific rigor, traceability, and a patient-centered vision. In a sector where evidence must be verifiable, decisions auditable, and efficiency measurable, competitive advantage will belong to those who turn digitalization into a structural capability rather than an isolated initiative. These companies will not only be prepared to compete but also to set the course for the future of the pharmaceutical sector.