To ensure that cloud and AI projects are executed successfully and on time, a thorough comprehension of business objectives and technological capacities is essential. This understanding must be kept current and shared by both IT teams and business stakeholders.
In recent years, many enterprises have noted that the failure rate for cloud and AI projects is approximately three times higher than that of traditional IT initiatives. Additionally, these projects tend to take longer to complete. A surprising aspect of this trend is that both cloud computing and AI were expected to enhance the speed of IT processes.
Enterprises have identified three main issues affecting the timelines of their cloud and AI projects, along with suggestions on how to improve them:
3. Lack of Essential Skills
A significant 56% of enterprises claim they lack the necessary skills within their teams. They report that acquiring these skills takes longer than anticipated. Interestingly, many of these organizations believed they were appropriately staffed or expected that the required skills could easily be sourced. However, they found that architects—essential for both cloud and AI projects—were missing from their teams, leading to stagnation in the planning phase.
2. Unrealistic Expectations
Early planning frequently reveals that the initial requirements for projects are unattainable. This finding has been cited by 69% of enterprises and leads to a period of redefinition before the actual work can commence. A significant misunderstanding is prevalent among senior management regarding the cost implications of cloud solutions, with many mistakenly believing that cloud adoption inherently leads to cost savings. Moreover, many enterprises do not fully grasp what AI can deliver, complicating their ability to frame realistic AI projects.
1. Questioning the Execution Approach
The most commonly cited issue, affecting 74% of organizations, occurs when team members start to doubt the project’s approach after it’s already underway. This usually stems from IT’s historical focus on operational aspects, while organizational leaders view AI as a means of obtaining critical insights. This disparity in perception can lead to a disconnect, often only recognized once concrete results are delivered.
Solutions for Success
To address these challenges, enterprises should consider establishing dedicated teams that combine line management and IT AI experts. Both should be well-versed in AI’s capabilities to collaboratively assess its business case and facilitate its adoption. It is crucial that a common language be developed between business objectives and technological implementation.
By developing continuous, shared understanding among stakeholders, enterprises can enhance the potential for their cloud and AI projects to succeed, mitigating the prevalent issues that lead to failure or delays.
