Artificial Intelligence (AI) has revolutionized the way we do business. It has made it possible for organizations to process and analyze massive amounts of data, automate repetitive tasks, and create new products and services. However, like any other technology, AI projects can fail, and organizational issues can be a significant contributor to that failure.  So how can you look to maximize the ROI in AI Projects?

In this blog post, we will discuss the impact of organizational issues on AI projects and how organizations can avoid these issues to achieve successful outcomes.

AI doesn’t build itself

First and foremost, it is essential to understand that AI does not build itself. It requires human input, and if that input is flawed, the resulting AI model will also be flawed. Additionally, if the data fed into the AI system is biased or incomplete, the resulting model will also be biased or incomplete. Therefore, the people involved in developing and implementing AI projects play a significant role in their success.

The Importance of Organizational Alignment

Organizational alignment is crucial to the success of AI projects. It is essential that everyone involved in the project understands the overall objective, and how their contribution will help achieve that objective. This includes even the interns who may be tasked with labeling data. It is vital that they understand the broader goal of their work and how it fits into the bigger picture.

Lack of alignment can also lead to a lack of standards and procedures, which can result in mistakes, missed deadlines, and budget overruns. Therefore, it is crucial that the project team has a clear understanding of the project’s objective, and everyone knows their role in achieving that objective.

Five Factors for Successful AI Projects

To achieve a successful AI project outcome, organizations need to follow five essential factors:

1. Clear Articulated Objective: Organizations need to have a clear understanding of why they are building an AI project, the type of data required, the input, the output, and the expected outcome.
2. Data Gathering: Organizations need to source and gather as much data as possible about the problem they are trying to solve, making sure that all data is usable together.
3. Explainability: AI models must be explainable. Organizations need to be able to explain how their AI model made a decision or prediction, and provide proof that it was based on the data and would have been made by a person in a similar manner.
4. Static Model: Organizations should avoid retraining AI models unless there is a valid reason to do so. Retraining models just to beat an arbitrary metric will not work and can introduce bias into the model.
5. AI Input and Output: Organizations should define all inputs and outputs for their AI model, making sure that they are well-defined and understood by everyone on the project team. Garbage in, garbage out is a common adage in the world of AI, and if the input is flawed, the output will also be flawed.

Conclusion

AI has enormous potential to transform businesses and society, but it requires a significant investment of time, resources, and human input. Organizational issues can derail even the most well-planned AI project, so it’s crucial to have a clear objective, ensure that everyone understands their role, and follow the five essential factors for successful AI projects. By doing so, organizations can mitigate the risk of failure and achieve the desired outcomes from their AI projects.