Lack of Strategic Alignment: Many AI initiatives aren’t aligned with overall business goals to solve the things that really matter to move the needing - leading to disjointed efforts with no clear path to ROI.

Getting Caught Up in the AI Hype Without a Clear Problem to Solve: Many companies have been swept up by the hype and vendor-driven promises, chasing shiny AI tools without first defining the problem they need to address. This often leads to wasted efforts, no meaningful outcomes, and uncontrolled "shadow AI" deployments without a clear roadmap or proper guidance on usage

Failure to Involve Key Stakeholders Early: Pilot projects frequently operate in silos, without buy-in from leadership or collaboration with key departments, leading to delays and lack of adoption.

Inadequate Data Quality & Availability. AI relies on data to be successful. Poor data quality, access rights issues to data often leads to project failure before real progress can be made.

Lack of Expertise & Skills: Many teams lack the internal expertise to navigate the complexities of AI, resulting in poor implementation planning, execution and missed opportunities for optimization.

No Clear Path to Scale: AI projects often lack a roadmap to transition from pilot to full-scale deployment, leaving teams stuck in perpetual 'testing' mode with no tangible results.

AI Governance Concerns: Uncertainty around AI's ethical and compliance implications can prevent projects from moving past legal, security and compliance teams, especially in highly regulated industries.