Leaders mistakenly treat AI like prior tech shifts (cloud, digital). However, those were deterministic, whereas AI is probabilistic and constantly learning. Building AI on rigid, 'if-then' systems is a recipe for failure and misses the chance to create entirely new business models.

Unlike traditional software, AI adoption is not about RFPs and licenses but a fundamental mindset shift. It requires leaders to champion curiosity and experimentation. Treating AI like a standard IT project ignores the necessary changes in workflow and thinking, guaranteeing failure.

People overestimate AI's 'out-of-the-box' capability. Successful AI products require extensive work on data pipelines, context tuning, and continuous model training based on output. It's not a plug-and-play solution that magically produces correct responses.

Pega's CTO warns leaders not to confuse managing AI with managing people. AI is software that is configured, coded, and tested. People require inspiration, development, and leadership. Treating AI like a human team member is a fundamental error that leads to poor management of both technology and people.

AI is not a silver bullet for inefficient systems. Companies with poor data hygiene and significant technical debt find that implementing AI makes their bad systems worse, simply scaling the noise and dysfunction rather than solving underlying problems.

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.

Companies fail at AI strategy because their leaders haven't invested in understanding the technology's core capabilities, such as reasoning and multimodality. Without this literacy, any strategic plan for org charts, tech stacks, or workflows will be suboptimal and incomplete.

Stalled AI projects often stem from cultural issues. Leaders rush for big wins instead of adopting an experimental "build to learn" mindset. They fail to address poor data quality and the organizational fear that leads to automating old processes instead of innovating new ones.