Top Reasons Why AI Projects Fail
Artificial Intelligence (AI) is a powerful technology, and with each passing year, more and more top-level executives are adopting it to automate their business. The adoption and implementation of AI are bringing positive transformations in many industries. Although, implementing AI without a well-defined approach can be challenging and hamper the entire project’s success.
Let’s discuss some of the mistakes that lead to AI projects’ failure and how companies can avoid them.
In 2018, Gartner predicted that 85% of AI projects would deliver misleading outcomes through 2022 due to bias in data, algorithms, or the teams managing the projects.
Companies can spend a lot of time, effort, and money on resources to adopt AI and still have zero progress and a failed AI project. Such failure affects the reputation of the technology, and some might be convinced that AI is not worth the effort. Building an AI system from scratch is challenging, and several reasons can contribute to its failure.
Is Your Business Ready for AI?
Why do businesses adopt AI? The answer is simple; to reduce costs, automate workflows, improve productivity, and become better at what they do. Many companies start the AI adoption process with great excitement. Still, as the process advances, only a few are successful, some abandon in the middle of the process, and some projects fail in the initial stages. Let’s discuss some of the top reasons why AI projects fail.
1. Clouded Business Objectives
AI is a powerful technology, and many industries are adopting it. Going ahead with an AI project is beneficial when the decision-makers have a clear idea about the business objectives. Let’s say your company is planning to adopt AI, but it lacks a clear business objective. This will lead to unfinished or failed AI projects.
Decision-makers should determine the business objectives and identify the problems they are trying to solve with AI before opting for AI transformation. Ask these basic questions to get clear business objectives.
• What are the business problems that need to be solved?
• How can AI help solve these issues?
• Is adopting AI affordable to solve these problems?
However, if decision-makers cannot answer these questions, most likely, their business objectives are clouded and require more focus.
2. Improper Strategic Approach
Whether a project is small-level or big-level, a strategic approach is necessary for executing it successfully. One primary reason many AI projects fail is the lack of a proper approach. Improper strategic approach causes confusion, hampers quality, and reduces the chances of success.
AI has great potential, and the right strategic approach helps build AI projects successfully. Adopting AI is a step-by-step procedure. Starting with smaller tasks will help AI understand your requirements and move towards big projects. Slow and steady wins the race, and patience is the key to adopting and deploying AI projects.
3. Poor Data Quality and Quantity
Data is the primary source in creating a practical AI system. So, a proper data governance strategy is required to ensure the reliability, quality, and security of the available data. Companies collect data in structured, unstructured, and semi-structured forms. For AI projects, companies usually collect a massive amount of data that includes many unwanted, outdated, and unorganized data which can cause project failure. Companies should bring in relevant, high-quality data from reliable sources to represent their business objectives and operations.
To address this issue, companies can consider involving stakeholders, data analysts, machine learning experts, IT analysts, and DevOps engineers before embarking on the project. This helps identify data requirements to build an AI model that can be tested to ensure that the AI project runs fine.
4. Lack of AI Awareness and Skills
According to a survey by Gartner, 56% of companies face challenges in adopting AI due to a lack of skills in their workforce.
Employees may have trust issues that result in rejection of AI or blind faith in it, leading them to accept all AI-made decisions blindly. Many working professionals also assume that AI will replace them, which is invalid. Due to all these reasons, companies should consider educating their employees about emerging technologies and promoting data literacy.
Data literacy ensures that your technical and non-technical workforce is aware of AI, what it does, and how it can help them, and also ensures that they do not entirely rely on AI for decision making.
5. Lack of Governance
After deployment of an AI project, proper maintenance is essential because the decision-making of AI systems depends on the type of data it is fed. Correct data will give the expected results, but the project is prone to failure without any data monitoring.
Proper data governance can save your company from mishaps due to incorrect and corrupted data. Constantly monitor your AI system to identify issues. Making changes in data according to business requirements and updating the AI system regularly can be very helpful in maintaining a sustainable AI system.
6. In-House vs. Outsourcing
Companies should decide whether to implement an AI system in-house or outsource the project. Many companies make the wrong decision of starting the project in-house without proper tools and skilled professionals, which ultimately leads to the failure of the AI project. However, with an experienced team, companies can move forward with in-house AI project development and deployment.
On the other hand, companies that do not have skilled professionals prefer outsourcing AI projects, but some of these companies end up with failed projects. This is because they fail to choose the right vendor that can help them complete the project. To skip such issues, invest some time researching and finding experienced and trusted AI implementation specialists.
Calculating the cost, resources, and effort is essential while making this decision. Evaluate your options carefully and choose which one works best for you to complete your AI project carefully.
