97% percent of executives report personal benefits from AI, yet only 29% see meaningful impact across their organizations.1
Most leaders are using AI, and they’re itching to see the business impact. At CMK Select, we often see pharma companies with strong enthusiasm and promising use cases, but without the operating model needed to scale AI consistently. The opportunity now is to connect ambition with structure.
The Illusion of Momentum
On the surface, enterprise AI adoption looks strong. Investment is rising. Announcements are everywhere. Pilot programs are spreading across functions. But activity isn’t the same as scale.
By the end of 2025, AI adoption was widespread, yet enterprise-scale adoption remained limited. McKinsey found that nearly two-thirds of organizations had not yet begun scaling AI across the enterprise.2
Strategy was also lagging execution. Thomson Reuters found that only 22% of organizations had a visible AI strategy, while 43% were moving ahead with GenAI despite having no formal strategy.3
The result: experimentation without impact. MIT Project NANDA reinforced this pattern, finding that 95% of enterprise GenAI efforts yielded zero return.4
Why AI Pilots Fail to Scale
The gap between executive use and organizational impact is clear. Senior leaders may use AI to summarize documents, draft emails, or accelerate research. But teams deeper in the organization are often left with generic training, limited access to tools, or guidance that doesn’t reflect how work actually gets done. That’s where pilots start to break down.
In pharma, AI can’t succeed as a general productivity layer. The work is too specialized, too regulated, and too dependent on cross-functional handoffs. Commercial teams may need AI support for launch planning, segmentation, messaging development, or field enablement. Medical Affairs teams may need help synthesizing insights or preparing scientific exchange materials. Regulatory Affairs teams may need better ways to manage submission dependencies and timelines. Market Access teams may need support with evidence, payer strategy, and value communication.
Each function has different decisions, risks, workflows, and approval requirements. So each function needs enablement that reflects how its work actually moves through the organization. Generic training creates awareness. Role-specific enablement changes behavior.
The Real Failure: Change Management
When an AI pilot in a pharma organization doesn’t scale, the tool often gets the blame. The model wasn’t accurate enough. Adoption was low. The interface was clunky. The data was incomplete. Those issues may be real, but they often point to a deeper problem: the organization wasn’t ready to change. The better questions are:
- Was the workflow clearly defined before AI was introduced?
- Was governance in place before deployment?
- Were compliance, legal, regulatory, and medical stakeholders aligned early?
- Did the right function own the outcome?
- Were teams trained in the context of their actual roles?
- Were success metrics tied to business impact?
The pressure to deploy AI quickly has created pilots that were never designed to survive real organizational conditions. Across industries, that’s wasted investment. In pharma, the stakes are higher. Poorly governed AI can create risks for patients, compliance, brand integrity, regulatory standing, and public trust.
Shadow AI makes the problem worse. Employees may use unapproved tools or workflows without formal governance, visibility, or compliance review. Sensitive information may be entered into unsanctioned systems. Regulatory considerations may be handled inconsistently. Data classification may be misunderstood. By the time leadership sees the risk clearly, it may already be embedded in daily behavior.
That’s why CMK Select approaches AI adoption as a change management initiative rather than a technology rollout. The tool matters, but the operating model matters more: accountable leadership, function-specific enablement, manager coaching, internal champion networks, and KPIs tied to business impact.
What It Takes to Move AI From Pilot to Scale
The pharma organizations that successfully scale AI tend to follow three rules:
- Design for the work, not the enterprise.
AI built for generic users rarely changes behavior. AI built around real roles, real workflows, and real decision points has a much better chance of adoption because it reflects how work actually happens. - Build compliance and governance in from the start.
In pharma and life sciences, governance can’t be added after deployment. HIPAA, FDA regulations, SOX considerations, clinical workflows, medical affairs requirements, data governance, and compliance review all shape how AI can be used. They determine what tools are appropriate, what data can be entered, what outputs require review, and who needs to be involved before AI touches the workflow. - Make adoption a managed change, not a training event.
Successful programs need more than access and instruction. They require clear ownership, accountable leadership, manager coaching, internal champions, and KPIs tied to business impact. When teams understand the boundaries and see how AI applies to their actual work, adoption becomes easier to sustain.
When these rules aren’t built in, organizations are forced to retrofit them later. That slows adoption, increases cost, creates disruption, and can turn a promising pilot into another stalled initiative.
A Practical 90-Day Path to AI Capability
The 90-day mark is a meaningful inflection point. In our work, pharma teams can move from curious to capable within a three-month window when the program is built around:
- Role-specific workshops
- Applied workflow training
- Clear milestones
- Governance
A strong AI capability program starts by identifying where work is slow, duplicative, risky, or too dependent on manual coordination. From there, the program should:
- Map opportunities to role-specific use cases
- Define compliance boundaries early
- Train teams using the language, materials, and decisions they actually work with
- Build confidence through applied practice, not abstract instruction
By month six, the strongest teams aren’t just experimenting with AI anymore. They use AI to reduce low-value work, improve consistency, accelerate analysis, strengthen cross-functional execution, and free up time for higher-value decisions. Teams can save several hours per person per week while delivering impact that leadership can actually see. That outcome doesn’t happen from a pilot alone. It requires a structured, compliance-first, change-led approach.
The Strategic Question for Pharma Leaders
The 97/29 gap shows what happens when AI becomes a personal productivity tool for executives without becoming an organizational capability. For pharma, that gap is also a risk to budget, competitive position, compliance, and, most importantly, the patients and stakeholders who depend on the decisions these organizations make.
The technology isn’t the constraint. The constraints are governance, workflow design, change management, and role-specific enablement. These constraints are solvable, but they require deliberate investment, clear ownership, and a willingness to work through organizational friction rather than avoid it.
CMK Select partners with life sciences and pharmaceutical organizations to close that gap. We help build AI governance frameworks, design role-specific enablement programs, and drive adoption as a structured change management initiative from day one through scale. Connect with our team to accelerate your organization’s journey from pilot programs to real-world impact.
Sources:
- https://writer.com/blog/enterprise-ai-adoption-2026/
- https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- https://www.thomsonreuters.com/en-us/posts/technology/measuring-genai-roi/
- https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf