6 Reasons Why AI Projects Fail

6 Reasons Why AI Projects Fail

It seems AI is present everywhere but the reality is that many businesses are experiencing problems in successfully implementing AI. According to the MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report, seven out of ten executives whose organizations have invested in Artificial Intelligence (AI) claimed they had seen little to no impact from them.

Additionally, 40% of businesses investing heavily in AI do not report any business benefits. The failure rate shouldn’t be so high given the number of intelligent brains, resources, and effort invested in these projects. The issue is the failure to adhere to best practices for managing AI initiatives, not inferior technology or unmotivated people.

In this article, we discuss 6 reasons for AI project failures, along with some advice on how you can avoid becoming another statistic of failed AI initiatives.

1. Poor Data Management

You need sufficient high-quality data that follows a coherent and understandable methodology for AI solutions to be useful. Many businesses lack the necessary resources or experience to deal with data that is not clean, kept in incompatible formats or locations. Data scientists waste too much time (often up to 70% of their time) wrangling data rather than applying their knowledge to create useful solutions or to derive insights to support business decisions.

2. Lack of AI Capabilities and Awareness Among Employees

A Gartner poll found that 56% of businesses had trouble using AI because of a skills gap in their staff. Employees may lack trust in AI, reject it outright, or have complete faith in it so accept all outputs of an AI model without question. A common misconception among working professionals is that AI will replace them. Due to all of these factors, businesses should consider boosting ML / AI literacy among their staff members and training them about new technological processes.

Technological literacy ensures that both your technical and non-technical employees are informed about AI, what it can do for them, the strengths and weaknesses of the technology, and how it can benefit them. It is also crucial that employees do not just rely on AI for decision-making without understanding how the decision is made.

3. Unclear Business Objectives

Instead of choosing projects where one sees potential for a technical breakthrough, organizations need to identify use cases with Return on Investment (ROI) that can have the most influence on their KPI ( increasing topline, decreasing operations cost, increasing customer experience, etc.). AI projects usually fail because of poorly defined goals, lack of data and insufficient resources.

4. Underestimating Time and Cost of the Data Component of AI Projects:

Organizations frequently underestimate the time and resources needed to effectively manage AI projects. Too frequently, projects begin without first considering data requirements and without having a dedicated person to be in charge of gathering the right kind and amount of data. These projects are often slowed down by a lack of access to necessary data. This is why assembling the data required to power AI is the first step of managing an AI project.

AI requires a data-centric strategy and companies should carefully examine whether they have the time and resources to devote to collecting sufficient quantities of high-quality data for their projects.

5. Lack of Leadership Commitment and Ownership

This is a common mistake in all projects, not just those involving AI. Without the dedication and ownership of a cross-functional leadership team an AI initiative won’t have the resources or talent necessary for success. An AI project can only be successful if it has capable leaders who are dedicated to it.

6. Vendor Misalignment on Promise VS Reality

Companies frequently fall for the marketing blitz and promises made by vendors about their products. Or businesses could choose a particular vendor’s solution only to discover that it isn’t the best fit for their requirements. Vendor-driven factors are frequently overlooked, which is one of the main causes of an AI project failure.

A frequent cause of this is failing to ask the correct questions up front, which prevents you from seeing that, despite the product being fantastic, it just does not meet your needs. To avoid falling victim to the hype, do your homework, ask the correct questions, and have an understanding of how to manage AI initiatives.

How can Skim AI Help?

AI is revolutionizing how we conduct business, and it takes time and effort to discover how to create useful solutions with a quantifiable ROl.

We advise our clients to build a framework for implementing ML and AI solutions based on best practices. The returns on AI investments are not linear. Rather, they increase as you layer on good work on top of a foundation created with best practices.

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