Five Mistakes to Avoid for Successful AI Project Execution

Artificial Intelligence (AI) has created the proper buzz and garnered the attention it needs across various industries. Today, many organizations are embarking on their AI journey. However, according to different news sources, analysts, and experts, about 60-80% of AI projects still fail every year. So, where do organizations typically go wrong? Surprisingly, the failure rate has little to do with the people, technology, or products they use. Many successful AI companies use the same products and services from the same vendors. So, what common pitfalls make developing AI projects complex and challenging if it's different from the team or technology?

Let's uncover these in this blog, highlighting organizations' top five mistakes while executing an AI project and ways to avoid them.

Top AI Mistakes that Organizations Make

Everyone wants to see positive results from their investments. AI call for a huge investment and hence while embarking on this journey it is best to ensure we start on the right note. One way to do is learn from the mistakes of others. In this section we list the most common pitfalls while kickstarting an AI project.

1. Identifying the Wrong Use Case

One of the most common mistakes while executing AI projects is identifying the wrong use case. There are many cases where AI may not even be the immediate solution, yet organizations diligently pursue it. It is essential to understand that AI is not the end objective but the means to reach it. Also, there is no one-size-fits-all solution available. Therefore, organizations must identify the correct use case that aligns with their business objectives. Otherwise, it can lead to a significant waste of time and resources. Organizations must thoroughly assess, analyze, and identify their business objectives and the core areas where AI can add value.

At SMARTnCODE Technologies , we conduct in-depth use case evaluation workshops with our customers to help us determine the correct use cases for their AI projects. These carefully crafted evaluation workshops help us uncover the problem statement and assess the possibility of having an effective AI solution. Sometimes, it is surprising that the problem at hand can be easily fixed with other existing tools and a little investment.

2. Not understanding the Complete Picture

Another common problem that is found in the planning and execution of AI projects is framing the problem incorrectly or adopting the wrong approach. The success of an AI project depends on how well the problem is framed and the right approach adopted to solve it.

For example, face recognition and detection are often confusing terms. Both are separate and have different approaches to problem-solving. Therefore, it is essential to articulate clearly and frame the problem or requirement to steer the project in the right direction. Organizations must spend enough time analyzing the situation and understanding its nuances before beginning the project.

3. AI without Data, is a critical mistake

Data is the foundation of an AI project and a critical part of its success. Therefore, it's essential to clearly understand your data and how it can be used before you even begin building your model. Poorly maintained or unstructured data can lead to poor models being built, resulting in inaccurate predictions and results that are less useful than they could be of help. Organizations must plan for data engineering from the beginning of the project. They must identify the data sources, develop pipelines, and ensure the data is accurate, complete, and consistent.

4. Using Incorrect Project Management Practices

AI projects are complex and require effective project management practices to ensure their success. Unfortunately, many organizations make the mistake of using traditional project management practices unsuitable for AI projects. To avoid this mistake, organizations must adopt project management practices specifically designed for AI projects. At SnC, we recommend agile based approach with a backlog of regularly prioritized tasks. It is also necessary to ensure effective collaboration between the data science, engineering, and business teams to guarantee the project's success.

5. Ignoring User Experience

AI projects need to be designed with the end user in mind. However, many organizations ignore user experience, which can lead to a lack of adoption and engagement. Organizations must ensure that their AI projects focus on user experience. They must conduct user research and testing to understand the users' needs and preferences and develop AI models that align with them.

Get the most out of your AI investment with SMARTnCODE Technologies

In conclusion, executing AI projects is a relatively complex and arduous task that requires meticulous planning and execution. However, by avoiding the above-mentioned mistakes, organizations can enhance their winning chances of success and achieve their business objectives. Remember, it's not just about preventing failure--it's also about making sure you get the most out of your investment in AI technology. If you are assessing the AI opportunity and ways to leverage it for your organization you can contact us for a free no obligation expert discussion. Our AI experts would love to hear your problem and help you assess the right next steps. Reach out to us today for booking your free slot.