Global investments in artificial intelligence are on the rise, with spending in the United States projected to reach between $76 billion and $121 billion this year and next.

In an optimistic scenario, AI spending could soar to approximately $129 billion in 2024 and $248 billion in 2025.

AI has evolved from experimental projects to a crucial element for gaining competitive advantage in organizations.

Organizations that prioritize data readiness, scalable systems, and trust in AI will drive transformative change and establish themselves as industry leaders in the coming decade.

Business leaders should focus on three key questions: Is our data infrastructure ready? Are our AI investments yielding measurable outcomes? Do we have governance in place for scalable AI?

Effective data infrastructure should unify and cleanse data to support meaningful AI applications and break down data silos.

Many companies struggle with clean and structured data, which is essential for AI to deliver meaningful insights, highlighting a disconnect between investment and data readiness.

Gartner reports that 85% of AI projects fail, primarily due to fragmented data and inadequate scalability rather than a lack of technology.

Despite the surge in AI investment, many projects fail to progress beyond proof of concept, raising questions about companies' understanding of AI's transformative potential.

The fear of missing out (FOMO) on AI adoption drives companies to invest without possessing the necessary infrastructure or strategy for effective implementation.

The rush to invest in AI often overlooks essential groundwork, including proper infrastructure, talent, and organizational changes, leading to project failures.

Governance frameworks are essential for ensuring transparency, ethical usage, and executive buy-in for scaling AI across business units.