However, the successful implementation of an AI project is not as simple as acquiring a powerful machine and slapping some code on it. There are many pitfalls along the way, and if any one of these pitfalls is underestimated or ignored, the project could fail.
Factors Contributing to the Failure of AI Projects
One could question why we don’t have the ideal guide for adequately implementing AI yet. In practice, many variables go into creating great AI, making it challenging to recommend prescriptive measures that will always be successful for every case.
Even so, progress is being made in gathering best practices (through lessons learned from successes and failures). As a result, common patterns regarding what frequently causes failure is beginning to emerge.
Here are some instances when businesses could misstep:
Factor #1: Haphazard Planning
Some companies undermine the difficulty of AI projects and launch initiatives without proper planning. In such cases, teams may rush to build products without fully comprehending business requirements or technical capabilities. These teams build products quickly to meet aggressive deadlines, which creates technical debt and leads to implementation issues down the line.
To take full advantage of AI, companies must align all software development life cycle aspects with business strategy, goals, and objectives. The best way to ensure this alignment is by creating a roadmap for AI projects.
These roadmaps consist of three major steps: planning, building and incorporating into business operations, and that can be a lot to keep track of.
To make sure your project stays on track and achieves desired results, here are some key things to remember when planning your AI project:
- Identify who will be involved in the project
- Establish clear goals and objectives that tie in with your overall business strategy
- Define success criteria for each step along the way
- Develop realistic schedules and budgets for each phase
- Use an agile software development methodology that streamlines communication between all stakeholders
Factor #2: Data Issues
Data can be thought of as the fuel that powers AI projects. It’s often not enough to just have a data set that includes a reasonably large sample—you need lots of data that’s been curated and arranged in a way that makes it easy for computers to pick out specific aspects, or patterns, of the information.