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.

For successful enterprise AI implementation, initiatives should not be siloed in the central tech function. Instead, empower operational leaders—like the head of a call center—to own the project. They understand the business KPIs and are best positioned to drive adoption and ensure real-world value.

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.

A common implementation mistake is the "technology versus business" mentality, often led by IT. Teams purchase a specific AI tool and then search for problems it can solve. This backward approach is fundamentally flawed compared to starting with a business challenge and then selecting the appropriate technology.

While AI models improved 40-60% and consumer use is high, only 5% of enterprise GenAI deployments are working. The bottleneck isn't the model's capability but the surrounding challenges of data infrastructure, workflow integration, and establishing trust and validation, a process that could take a decade.

The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.

Enterprises often default to internal IT teams or large consulting firms for AI projects. These groups typically lack specialized skills and are mired in politics, resulting in failure. This contrasts with the much higher success rate observed when enterprises buy from focused AI startups.

The excitement around AI capabilities often masks the real hurdle to enterprise adoption: infrastructure. Success is not determined by the model's sophistication, but by first solving foundational problems of security, cost control, and data integration. This requires a shift from an application-centric to an infrastructure-first mindset.

A major barrier to enterprise AI adoption is IT treating licenses as scarce resources, parsing them out one-by-one. This creates long queues for eager teams, even those with clear ROI use cases, which stifles grassroots experimentation and kills momentum before value can be proven.