5 Reasons Why AI Projects Fail



AI

Below is a list of 5 of the recurring reasons that AI projects can and do fail. 

There are plenty of reasons a new AI project might never come to fruition. Overlooking all the obvious examples of naysayers with low expectations, there is a list of issues that are less overt but just as deadly.

Many projects fail because they don’t know what they’re doing. Some fail because their creators have no clue how to move forward in the face of more problems than solutions. Below is a list of 5 of the recurring reasons that AI projects can and do fail.

 

Poor data management

Bad data management can sink an AI project’s chances faster than anything. Data must be sanitized, labelled (and labeled correctly), and documented.

What this means: A standard should be put in place early on to ensure that data is collected, stored, and managed effectively. This includes variables like the ‘meaning’ of the data to make it useful for training models. These processes should not change over time – they should be set in stone at the start of a project.

If your team can answer these questions, you are in good shape to have an impactful AI project in 2022. You may still hit roadblocks or run into issues with data management but having these processes in place will help ensure that you avoid the pitfalls above.

 

Lack of clear business objectives

AI projects often fail to achieve liftoff because they don’t have clear business objectives laid out. Focusing on a measurable business objective first, rather than first focusing on developing a tool or system to solve a problem, is challenging to the traditional organization.

What this means: AI projects often start as an innovative shiny object with no connection to what really drives value for the business. The following questions should be asked before starting an AI project:

  • “What is the business problem that I’m trying to solve?”
  • “How will the AI be used to solve this business problem in an impactful way?”

If you cannot answer these questions, your project likely has no clear business objective. If this is the case, you should stop developing code. Rather than spending time on building a project that is unlikely to help the business increase its revenue, you should instead focus on properly defining a clear objective.

 

Lack of governance and standards

AI projects can also find themselves dead in the water if there isn’t the proper governance and standards in place. These need to be defined in the ‘early days’ of the project before risks associated with misconfiguration, security holes, or incompatibility accumulate.

What this means: AI projects must put governance and standards in place early on to ensure that they are not doomed from the start because of procedural hurdles. It is important to reiterate that one of the pitfalls of AI is the lack of explainability. Any decision made by an algorithm must be able to be ‘explained’ to a business line owner in a way they can understand and accept. This will require appropriate governance and standards in place from the start.

 

Lack of leadership commitment and ownership

This isn’t just a pitfall specific to AI projects, but all projects. Without the commitment and ownership of leadership, the project will have no resources to be successful.

What this means: Without knowledgeable expertise available or committed to an AI project, it is unlikely that any significant work can get done. The only way for an AI project to succeed is if it has capable leaders committed to its success.

 

Ineffective team composition and skillset

In order to successfully execute a project, the right people need to be on board from the start. In the world of AI, this means expertise in AI as well as domain expertise.

What this means: It is critical to have the right people from the start on an AI project or any other type of project for that matter. This will require leveraging internal and external data sources to identify, recruit, and hire highly qualified staff with strong backgrounds in both machine learning (for developing models) and subject matter expertise (for deploying models).

 

Conclusion 

AI is the future, and the provision of things like AI as software is transforming private, public and corporate life. In order for the project management behind all of these groundbreaking developments to go smoothly, keep in mind and avoid the above common reasons AI projects fail.

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