3 Reasons Why Artificial Intelligence Projects Fail

Businesses will be better positioned to make smart decisions that lead to a successful AI deployment if they understanding the potential pitfalls.

Businesses have been challenged by unimaginable forces in 2020 that have precipitated substantial disruption to the traditional business paradigms. Remote work became standard, not optional, as government-mandated stay-at-home orders caused a physically separated employee base, leading to a radical shift in how they performed their tasks. With this widespread disruption to the workforce and the impact upon productivity and customer service, large and medium-sized businesses have invested in solutions like chatbots, RPA, and IVRs for quick fixes. Some turned to artificial intelligence (AI) in a bid for a quick recovery, taking on conversational, analytical, and other forms of artificial intelligence at breakneck speed. Our own research shows that 88% of businesses adopted or scaled AI in order to regain their edge and stay ahead of the curve. The hopes are high, and the need has never been greater.

However, AI and other emerging technologies usually demonstrate a disappointment in terms of ROI, especially with chatbots and RPA, and have fail rates that businesses can’t afford right now. Research from MIT Sloan Management Review and Boston Consulting Group revealed that only 10% of organizations have achieved significant financial benefits with AI. Those looking for operations expense returns alone may have missed the true value of AI utilization, the transformation of call centers infused with digital workers achieving superior customer service, thereby retaining and growing revenue.

This research hits at the core of why some AI projects may fail if the project goals are not properly set and staffed. The good news is that organizations can overcome these issues once they know what to look for and what to avoid.

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Technology is constantly evolving, but the pandemic has inspired some firms to explore chatbots and other solutions that are stuck in the past. While chatbots are, for instance, quick and easy to deploy, they offer lackluster results in a world where customers are diverse in how they speak, what they want, and how they may approach a business. Scripted solutions like chatbots or task automation resources like RPA ultimately lack the sophistication that organizations need as they mature and as their customer bases grow.

If a business can’t maintain a dialogue with its customers, it will be more likely to lose them. Product quality matters, but it can only take a business so far. In many circumstances, service decides the degree to which the customer relationship continues.

Organizations may be tempted to jump on the AI bandwagon and keep up with peers that have already embraced the technology. They will not be able to find an AI solution that can deliver satisfactory results. With so much hype and so many reports urging enterprises to use AI, business leaders may be under the impression that they must take immediate action. They may turn to a provider that automates accounts payable when what they really need is to automate their call center.