{"id":42340,"date":"2025-07-28T09:02:31","date_gmt":"2025-07-28T09:02:31","guid":{"rendered":"https:\/\/www.oxfordcorp.com\/?p=42340"},"modified":"2025-09-04T18:02:04","modified_gmt":"2025-09-04T18:02:04","slug":"ai-success-isnt-luck-its-strategy-heres-what-we-shared-in-our-latest-webinar","status":"publish","type":"post","link":"https:\/\/www.oxfordcorp.com\/en\/insights\/blog\/ai-success-isnt-luck-its-strategy-heres-what-we-shared-in-our-latest-webinar\/","title":{"rendered":"AI Success Isn\u2019t Luck – It\u2019s Strategy. Here\u2019s What We Shared in Our Latest Webinar."},"content":{"rendered":"

Artificial Intelligence is no longer just a buzzword – it\u2019s a business imperative. But despite the current push to implement AI as an integral part of doing business, nearly 70% of AI projects fail*<\/strong>. That\u2019s a costly statistic in both time and budget, especially as AI investments continue to surge.<\/p>\n

At Oxford Global Resources, we work with leaders and decision-makers of companies across industries who are excited about AI\u2019s potential but unsure where\u2014or how\u2014to start. That\u2019s why we hosted our recent webinar, \u201cThe Key to Unlocking AI Success: Strategy, Readiness & Real-World ROI,\u201d <\/strong>led by our AI Practice Director, Alie Doostdar, alongside Stefan Feldmeier, VP and AI Practice Leader.<\/p>\n

With global AI investment projected to hit $750 billion globally this year and forecast to reach nearly $3.7 trillion by 2034<\/strong><\/a>, organizations are under pressure to keep up. But speed alone isn\u2019t the answer. Success hinges on strategic readiness, clear use cases, and the right infrastructure to scale.<\/p>\n

In this session, we unpacked the foundational steps every organization must take to move beyond pilot projects and into scalable success. If you missed the live event or just want a quick refresher, here\u2019s a recap of key insights and how to avoid becoming part of the 70%.<\/p>\n

1. Strategy First. Tools Later.<\/h2>\n

Many organizations begin their AI journey by experimenting with platforms or technologies before fully defining their goals, but this often leads to fragmented efforts that don\u2019t scale.<\/p>\n

The right approach?
\nStart with your business objectives, <\/strong>then align AI use cases to those objectives. Only after that should you evaluate tools and platforms. Organizations that clearly define outcomes before implementing AI see higher ROI and are better positioned to scale solutions.<\/p>\n

2. Build the Right Foundations<\/h2>\n

AI is only as strong as the foundation it rests on. Before deploying models or automation, you need to assess:<\/p>\n