A curious trend has emerged in pharmaceutical commercialization. Nearly half of life sciences companies dedicate more than 20% of their commercial budgets to artificial intelligence (AI) yet remain stuck in perpetual exploration with isolated pilot programs. The gap between AI ambition and advantage has become the defining challenge of modern life sciences organizations.

Recent research paints an increasingly clear picture of what distinguishes companies that have achieved “AI Advanced” status from those trapped in endless experimentation. AI Advanced organizations are defined as those that have broadly adopted AI technologies with a clear strategy and process in place for ongoing optimization. The difference lies not in technology or investment but in how organizations approach integration, governance, and execution.

The shift from AI ambition to advantage requires more than financial commitment. It demands a fundamental rethinking of organizational structures, data strategies, and partnership models. According to a recent IQVIA survey,¹ organizations that successfully scale AI share common characteristics: They treat it as an enterprise capability rather than a departmental initiative, they invest in data governance before deploying models, and they measure success through business outcomes rather than technical metrics. Understanding these patterns provides a roadmap for organizations seeking to move beyond perpetual experimentation.

Investment alone falls short

Investment alone does not determine success. To realize impact, organizations must also have several critical strategic elements in place.

First, leading organizations integrate AI into daily operations by tying projects to concrete business goals. They connect every initiative to measurable outcomes, whether that’s increasing sales, accelerating product launches, or expediting market approvals. They also build robust data foundations. For organizations falling behind, this is arguably the most persistent barrier to scale, given how only one in 10 companies reports having comprehensively organized and accessible data for AI applications.

Additionally, successful organizations establish enterprise-wide governance with cross-functional teams that share accountability for results. This governance structure prevents duplication of effort and ensures that learnings from one department benefit the entire organization. Without a coordinated approach, companies risk creating overlapping AI initiatives that drain resources without delivering enterprise value.

These organizations treat AI as a shared capability, as opposed to isolated experiments. Rather than relying on siloed personalization engines or fragmented outreach strategies, leading organizations are deploying AI that dynamically adapts messaging, timing, and channel mix based on enterprise-wide data. This approach enables them to achieve the strong returns that elude companies still testing individual use cases.

The promise-performance disconnect

Sales and marketing departments identify AI as their highest strategic priority, yet these same functions show the lowest implementation rates. IT and data operations, by contrast, lead in actual deployment. This misalignment reveals why organizations struggle to capture value from their AI investments.

Successful organizations may initially run pilots but eventually abandon the model where each function runs independent initiatives. Instead, they create unified governance that treats AI as an enterprise capability. The decision-making structure must evolve from departmental to enterprise-wide.

Misalignment here can have significant consequences. For example, if IT departments build sophisticated AI capabilities without understanding the workflow needs of sales teams, those sales teams may fail to adopt the new tools. Similarly, if marketing invests in AI-powered personalization engines that are disconnected from sales intelligence, teams are left with a collection of impressive technical achievements that lack the practical relevance required to transform commercial operations.

To bridge this disconnect and achieve real transformation, companies must address the following foundational pillars, which can impact whether AI investments deliver returns or disappoint.

Building blocks that matter

1. Data

With only 11% of organizations reporting comprehensively organized and accessible data for AI applications, data readiness represents the most significant barrier to scale. Another 53% describe their data as “mostly sufficient,” while 36% acknowledge theirs as “moderately insufficient” or “mostly insufficient.”

The issue isn’t volume. Companies have abundant data scattered across systems. The problem is governance and utilization. AI-ready data must be connected and consistent across functions. For example, healthcare professional records must be unified across sales and medical affairs systems.

Data must be tracked, auditable, and structured for cross-functional use. Without investing in integration and governance that allows data to move smoothly across the enterprise, even sophisticated AI models cannot deliver their promised value.

2. People

Technology represents only one dimension of readiness. The entire industry is learning as AI evolves at breakneck speed. Having sufficient resources aligned on change management becomes critical for success.

Companies must position AI as a tool that augments team capabilities rather than one that replaces human expertise. They need a clear, measurable return on investment for each case, whether quantitative improvements in prescription rates or qualitative gains in approval speeds.

Teams must understand how intelligent tools enhance their roles instead of threatening them. This human element often determines whether technically sound solutions achieve adoption or abandonment.

3. Partners

To help address these challenges, 89% of organizations co-develop AI solutions with external partners. Thirty-eight percent now use performance-linked contracts, where compensation is tied to business results. Fifty-five percent operate through joint governance structures with shared key performance indicators.

What separates strategic partners from vendors is shared accountability. Both parties must take responsibility for outcomes, not just deliverables. The most valuable partnerships combine data expertise, technology capabilities, and deep domain knowledge. Partners who understand workflows and regulatory requirements at a granular level can deliver production-ready solutions instead of experiments that won’t scale.

Tomorrow’s operating model

Current use cases focus on operational automation, patient interaction tools, and forecasting. The biopharma organizations pulling ahead are already building toward more sophisticated, interconnected systems.

Four key trends will reshape the future of commercial operations:

• Automation will extend from individual tasks to end-to-end processes, connecting previously isolated workflows into seamless operations.
• Patient support will shift from basic chatbot interactions to systems that measurably impact adherence and health outcomes.
• Supply chains will evolve from static forecasting to real-time adaptation, detecting and responding to disruptions as they occur.
• Analytics will become increasingly prescriptive and automated, triggering commercial actions with minimal human intervention.

The moment of truth

Leaders demonstrate several key characteristics. They maintain strategic clarity about which use cases to prioritize. They build operational readiness to execute at scale. They measure success through business metrics rather than technical benchmarks. These organizations align investments with measurable goals while building the foundations and partnerships needed to scale.

Several questions determine readiness for this transformation. Does your organization have executive alignment on AI as a strategic priority? Have you invested in data governance and integration? Are teams empowered with shared accountability? Have partnerships evolved beyond traditional vendor relationships?

Organizations that address these questions and make AI part of their core commercial strategy will be best positioned to capture value while minimizing friction and growing pains.

Shravan Kotakonda is VP, Strategic Operations, Commercial Solutions, at IQVIA

Reference

1. Kotakonda, S.; Awasthi, P. AI in Life Sciences Commercialization: Strategic Insights and Practical Recommendations from 2025 Survey of Commercial Leaders. IQVIA white paper. 2025. https://www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/ai-in-life-sciences-commercialization-white-paper.pdf