Reasons Why AI Projects Fail, and How to Fix Them

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AI is an attractive feature for new-generation IT systems and applications, and a lot of investment is being put into them here in 2021. But some finesse is often needed to make sure they work as optimally as is promised.

AI Projects Fail- It’s no surprise that artificial intelligence is a key ingredient in the modern tech space. From machine learning to wearables to robotics, the AI across industries is a growing necessity for businesses looking to remain competitive in the long term. Yet there are a few common reasons why businesses often fall short in their AI strategy implementation.

Information for this eWEEK Data Points article was supplied by Dr. Charla Griffy-Brown, Professor of Information Systems and Technology Management, and Associate Dean of Executive and Part-Time Programs at Pepperdine University’s Graziadio School of Business. Here she discusses five key reasons AI strategies fail and what businesses can do to avoid these pitfalls.

Data Point No. 1: Technical performance

Early work on AI solutions usually involves small subsets of data, which require smaller computing resources. When AI expands into broader production systems, performance can be impacted exponentially. Insufficient attention to performance at scale creates AI systems that appear to work well during testing but quickly become unusable by the business at large.

Solution: Businesses should be accurate in computing requirements for scaling up and test, as often as possible, in a near-production environment.

Data Point No. 2: Veracity of data and volume of data

There are fundamental issues that arise from decisions regarding data architecture. The wrong database can easily render a scaled AI working test system unusable. Furthermore, this is enhanced by data cleansing and preparation problems. For example, manual interventions by humans might be effective in preparing test data, but this typically cannot be scaled.

Solution: Make data architecture decisions based on not just growth but an understanding of the processes required for the data training required to build AI.

Data Point No. 3: Business processes and people

One of the biggest challenges facing implementation of new technology is human beings, and AI implementation will only be as strong as the training and support for the staff implementing it. AI solutions must also be developed with a mechanism for ensuring customer facing channels are fully prepared for customer reactions. For example, this could include a temporary spike in phone calls if chatbots aren’t working properly or a tsunami of emails if a phone answering service isn’t getting them where they need to go.

Solution: Realizing that AI requires human work is fundamental to thinking through AI deployment. Businesses will need to implement strategies to address challenges quickly in advance of an AI initiative, including considerations for how it will impact human staff and customers.

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