AI For Pharma Companies: Adoption Challenges & IT Strategies for Growth Explored

Key Takeaways

• Deloitte reports that 71% of surveyed life sciences executives saw AI deployment advance, but only 13% reported measurable improvement at scale.
• Data quality, legacy systems, integration, and fragmented workflows can restrict AI implementation.
• Successful pharma AI programs need clear business objectives, measurable outcomes, and appropriate governance.
• IT strategy should connect AI initiatives with existing systems, operations, and long-term growth plans.

Why AI Adoption Is Moving Faster Than AI Value in Pharma

Artificial intelligence is becoming a practical consideration for pharmaceutical companies, but moving from experimentation to enterprise value remains difficult. This is an area where experience across both technology and business operations can make a difference. Observing these trends, GAMMA SOLUTIONS, led by founder and CEO Gilles-André Morin, known to many as G.-A., points to the relationship between technology decisions, business objectives, and the operational environment in which AI is deployed.

Deloitte’s June 2026 midyear Life Sciences Outlook found that 71% of surveyed executives said AI deployment had advanced at least somewhat, yet only 13% reported measurable improvement at scale. The finding points to a fundamental distinction: deploying an AI capability is not the same as embedding it successfully into an organization.

What Makes AI Adoption Difficult for Pharmaceutical Companies?

1. Legacy Systems Can Limit What AI Can Actually Do

AI applications depend on access to usable data and systems that can exchange information reliably. Pharmaceutical organizations often operate across research, clinical, manufacturing, quality, regulatory, commercial, and corporate functions, each with its own technology requirements.

Adding an AI application to one department does not automatically make those environments interoperable. If information remains fragmented across disconnected platforms, an AI system may have limited access to the context required to produce useful outputs.

This makes systems integration and optimization a strategic consideration rather than simply an IT maintenance exercise. When systems cannot exchange information reliably, AI initiatives can remain isolated from the workflows and data they are intended to improve, limiting their usefulness beyond individual applications.

2. AI Projects Need a Business Case, Not Just a Technical Demonstration

A successful proof of concept can demonstrate that an AI model or application works. It does not necessarily demonstrate that the application deserves enterprise-wide investment.

McKinsey's research on scaling generative AI in life sciences found that all surveyed pharma and medtech leaders had experimented with generative AI, but only 32% said their organizations had taken steps to scale it. The research identified strategy, talent, operating model and governance, change management, and risk among the major barriers to scaling.

For pharmaceutical companies, this means AI initiatives should begin with a defined business problem. The relevant questions are not simply whether AI can perform a task, but whether it can improve a measurable outcome, who will use it, what systems it must connect with, and what operating process will change as a result.

Data, Governance and Workflow Design Matter

AI adoption also changes how information moves through an organization. A system that summarizes documents, analyzes research data, supports decision-making, or automates a workflow still needs defined inputs, appropriate controls, and clear ownership.

Regulatory considerations add another layer. The FDA notes that AI is being incorporated across the drug development lifecycle, including nonclinical, clinical, postmarketing, and manufacturing activities, and has issued draft guidance concerning AI used to support regulatory decision-making for drugs and biological products.

Consequently, governance cannot be treated as something added after deployment. Data access, validation, accountability, security, human oversight, and performance measurement need to be considered as part of the implementation design.

How Pharma Companies Can Build an IT Strategy for AI Growth

The strongest approach is to treat AI adoption as part of a broader technology roadmap rather than as an isolated software purchase. That means identifying high-value use cases, assessing the systems and data required to support them, establishing measurable outcomes, and defining how successful pilots can move into production.

For pharmaceutical companies, this also means assessing whether existing IT operations and organizational structures can support additional digital initiatives. Factors such as technical leadership, program management, organizational alignment, and due diligence can help determine whether an AI initiative is ready to move beyond experimentation.

From AI Pilots to Scalable Pharma Operations

The real test of an AI initiative comes after the initial deployment. A promising pilot can still fail to deliver lasting value if employees do not adopt it, the underlying data is unreliable, or the technology cannot fit smoothly into established processes.

For pharma companies in Atlanta, digital strategy consulting can help connect AI initiatives with the systems, operations, and business priorities that determine whether they can scale. The goal is not simply to deploy more AI, but to identify applications that deliver measurable value and build the technology foundation needed to support them over time.

GAMMA SOLUTIONS, LLC
City: Newton
Address: 45 Nonantum St.
Website: https://www.gamma-solutions.llc
Email: ga.morin@gamma-solutions.llc