According to the latest report from Gartner, only 28% of infrastructure and operations-related AI projects are successful, with a staggering 20% ending in complete failure. These numbers may come as a surprise to those who have been inundated with promises of AI revolutionizing the business world. But the reality is that implementing AI technology is not as straightforward as it seems, and many businesses are struggling to see positive returns on their investment.

So, what is causing these AI projects to falter? Gartner points to a lack of clear objectives and measurable outcomes as the main culprit. Many companies jump on the AI bandwagon without a clear understanding of what they want to achieve, resulting in half-baked strategies and disjointed implementations. This lack of direction leads to failure in delivering meaningful results and ultimately sours any hope of achieving a return on investment.

Another major issue identified by Gartner is the lack of skilled talent. With AI being a relatively new and complex field, the demand for skilled professionals far outweighs the supply. This means that companies are struggling to find the right talent to lead and execute their AI projects, resulting in delays, errors, and overall project failure.

Interestingly, Gartner also found that a significant number of AI projects are hindered by company culture. Many organizations fail to create a culture that embraces AI and encourages experimentation and innovation. This leads to resistance to change, siloed teams, and a lack of collaboration, which can all cause AI projects to fail.

So, what can businesses do to improve their chances of success with AI? Gartner advises that companies should start with a clear understanding of their business goals and objectives and then determine how AI can help achieve them. This means involving key stakeholders and creating a roadmap that outlines the goals, expectations, and key performance indicators for the AI project. Additionally, investing in upskilling current employees and hiring top talent with experience in AI will be critical in driving successful AI projects.

However, it’s not just about technical