A significant implementation roadblock is the ownership battle between IT and business functions. IT wants to control infrastructure and moves slowly, taking years. In response, business units run their own unsanctioned initiatives to move quickly, leading to a disconnected and unscalable approach to AI.

Organizations that default to treating AI as an IT-led initiative risk failure. IT's focus is typically on security and risk mitigation, not growth and innovation. AI strategy must be owned by business leaders who can align its potential with customer needs, talent decisions, and overall company growth.

Many pharma companies allow various departments to run numerous, disconnected AI pilots without a central strategy. This lack of strategic alignment means most pilots fail to move beyond the proof-of-concept stage, with 85% yielding no measurable return on investment.

The biggest mistake in corporate AI investment is buying platform licenses for everyone without first investing in the necessary training and change management. This over-investment in tech and under-investment in people leads to wasted resources, as employees lack the skills or motivation to adopt the tools.

Without a strong foundation in customer problem definition, AI tools simply accelerate bad practices. Teams that habitually jump to solutions without a clear "why" will find themselves building rudderless products at an even faster pace. AI makes foundational product discipline more critical, not less.

Implementing AI tools in a company that lacks a clear product strategy and deep customer knowledge doesn't speed up successful development; it only accelerates aimless activity. True acceleration comes from applying AI to a well-defined direction informed by user understanding.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.

Many AI projects become expensive experiments because companies treat AI as a trendy add-on to existing systems rather than fundamentally re-evaluating the underlying business processes and organizational readiness. This leads to issues like hallucinations and incomplete tasks, turning potential assets into costly failures.

Leadership often imposes AI automation on processes without understanding the nuances. The employees executing daily tasks are best positioned to identify high-impact opportunities. A bottom-up approach ensures AI solves real problems and delivers meaningful impact, avoiding top-down miscalculations.