Why Building In-House Fails to Deliver ROI

Despite the buzz around AI projects, over 80% of AI projects fail. Enterprises often underestimate the complexity of turning AI demos into fully operational, scalable solutions.
Why do so many AI projects fail?
Very few AI demos become solutions that delivers decision making value. Most projects get abandoned or delayed, making them less cost-effective than market-ready solutions.
  • Shifting goals, no clear ROI. Misalignment leads to failures—even if initial demos are successful at first. Changing priorities derail even promising in-house efforts.
  • Underestimating resources. AI demands extensive resources: cross-team time, investment in data, integrations, and ongoing maintenance.
  • Model dependency. AI models quickly become outdated as new ones emerge. This demand continuous re-evaluation using complex frameworks.
  • Security compliance. Security risks and costs are significant and often increase when IT systems change. This drives up project costs further.
  • Expert shortage. Keeping up with AI innovations demands specialized expertise. Ongoing maintenance pulls IT resources from core tasks, making ongoing support a burden.
  • Lack of explainability. Black-box, inconsistent AI leads to poor adoption. Enterprise-grade explainable AI trusted by stakeholders is critical but costly to build.
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A view of a Thematic dashboard with the natural language interface to ask questions of feedback.

We had a solution but we were looking for something better.

We are a very customer-focussed company so we wanted to be able to really hear and understand and identify those insights. We wanted to make sure the solution we had was best in class.

Munna Ahaduzzaman
Software engineer, Atlassian

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