- Lack of clear business objectives
AI is a powerful technology that, if correctly implemented, can change businesses for the better. However, most organizations that fail in their projects have a problem defining and understanding their goals. While this is not everything, companies must always try to determine and define business problems and decide whether AI tools and techniques can help solve them. Apart from these, organizations must measure the costs and benefits of AI projects in advance to ascertain if the venture will be profitable. With well-defined business goals, you can get an idea regarding the potential of AI in your organization and whether it is the right tool to solve your problems.
- Poor quality of data
AI without data is nothing, which is why data is an important component of any AI project. Due to the importance of data, businesses must develop a data governance framework to ensure data availability, integrity, security and quality. Doing this will ensure that the data they will use in their project is in the right state and will deliver the desired results. For instance, organizations must eliminate outdated, insufficient and biased data. If this is not done, it can result in misleading results, failure of the project and waste of valuable business resources and time.
- Lack of standards and governance frameworks
Artificial intelligence projects can find themselves dead in the water if they lack the right governance frameworks and standards. Sadly, this is one area in which most organizations fail, either knowingly or otherwise. As you start your AI project, you must define the project and its risks in the early days. Define risks such as misconfigurations, security or incompatibility issues. AI must come up with governance and standards to ensure that they do not fail because of procedural hurdles.
- Lack of cooperation
Dealing with data is something that requires a team effort. However, in some instances, data science teams work in isolation in developing and implementing projects, and this is a source of failure. For a project to succeed, there is a need for collaboration between data scientists, data engineers, designers, business professionals and other IT staff. Creating a collaborative environment will help businesses ensure integrated output, standardize the development process, share experiences, develop best practices and deploy quality AI solutions.
- Lack of the right talent
A 2019 survey indicated that a key challenge in adopting AI for businesses is the lack of skilled personnel to help in projects. Although an organization may have the money needed to create a talented data science team, the professionals to fill the available positions may not be available. Without the right team with adequate training and expertise in business, AI projects will fail to take off. Businesses must therefore do a cost-benefit analysis of creating in-house data science teams and compare it with outsourcing the same services to other companies that offer these services.
