The rate of digitalization has skyrocketed, and there is no longer any doubt about the huge impact that AI-driven technologies have on our world. While many companies are reaping the advantages of successful AI and analytics deployments, the reality is that many of these initiatives continue to fall short of expectations or even fail. Let’s see the top reasons why AI projects fail:

Reasons Behind Failed AI Projects

The implementation of an artificial intelligence system entails your company’s digital transformation. You’re hoping to reshape your company’s operations with AI systems. However, you might have realized that the system created isn’t producing the desired results. This could occur due to a multitude of factors. The following are the most prevalent reasons why an artificial intelligence system fails:

Unclear business objective

One of the prime reasons for the failure of AI is the lack of setting well-defined business goals. Rather than beginning with the answer to an ambiguous business challenge, businesses must first identify and define business problems before assessing whether AI approaches and tools may assist in their resolution. Furthermore, determining the costs and benefits of an AI project is difficult due to the following factors: 

• Creating an AI project and training/building an AI model are both experimental processes that may take a long time to complete.
• AI models attempt to solve uncertain business challenges and the outcomes may vary depending on the application case.

A clearly defined business goal might help you determine whether AI is right for your business.

Poor data quality

Every AI project relies heavily on data. Companies must design a data governance strategy in order to make sure the quality, availability, integrity, and security of the data they will utilize in their project. Dealing with inaccurate, outdated, or biased data can lead to project failure and wastage of company resources. Before commencing an AI project, companies should ascertain that they have adequate and relevant data from credible sources that depict their business activities and are compatible with the AI tool deployed.

Also read: Building Accountability Into AI Services 

Lack of team collaboration

Lack of collaboration is another critical factor that can lead to AI project failure. A successful AI project necessitates the collaboration of data scientists, designers, data engineers, IT specialists, and even AI in SEO. Creating a collaborative technological environment would assist businesses to: 

• Make the AI development process more consistent
• Share knowledge and expertise, and create best practices 
• Scale up the deployment of AI solutions

DataOps and MLOps are two sets of strategies for bridging the gap between diverse teams and scaling AI solutions. Additionally, creating an AI Center of Excellence (CoE) will enable professionals from several business areas to work together, thereby improving collaboration.

Lack of talent

The shortage of competent data science workers is the most significant barrier to firms embracing AI. Due to the skills scarcity, assembling a strong data science team can be costly and time-consuming. Companies should not expect to achieve much with AI unless they have a team with sufficient training and business domain experience. Companies must weigh the costs and benefits of building their own data science teams. You have to think twice about the benefits and drawbacks of both developing in-house AI teams and AI outsourcing services. Depending on your business goals and the size of your operations, outsourcing may be a more cost-effective way to integrate AI applications.

Also read: Mobile Banking: Let’s Discuss The Effectiveness Of AI In Reshaping The Customer Experience 

Data security and governance

The security concerns of AI are one of the most pressing issues in this digital era. When using AI, cyber risk must be evaluated from all perspectives. Artificial intelligence will introduce weaknesses and threats to existing cybersecurity systems. Before using AI, businesses should design a risk-based approach to deployment, identifying and reinforcing vulnerability. You may also consider collaborating with a third party to evaluate cybersecurity measures in order to identify weak points and fix them at the earliest.

Also read: AI-Informed Testing and Its Benefits in Business Software Testing | Blogs 

Conclusion

Artificial intelligence has established itself as the flag-bearer of the next-generation industrial revolution. All across the globe, businesses are witnessing the rise and fall of AI systems. In order to be successful, it is critical for companies to understand the reasons why AI system fails and fix them.  At ThinkPalm, we can analyze your project, design a framework, and deploy AI systems that align with your business goals. Contact us and we’ll have one of our experts reach out to you and discuss how we can lead your project to success.

Author Bio

Manju is an enthusiastic content writer working at ThinkPalm. She has a keen interest in writing about the latest advancements in technology. Apart from writing, she is a classical dancer, embraces fashion attires, and loves spending time with her pets.