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 understand 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.

See also: Skills Gap May Slow Down Real-Time Enterprises

1) Lifecycle of tech

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.

2) Lack of tech knowledge

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. Or maybe they realize that their call center could use the assistance of a digital agent but fail to vet a provider’s capabilities before signing on the dotted line.

The results could be detrimental on numerous fronts. Just as a positive experience can reinforce the use of a particular technology, a negative one could turn off business leaders who don’t realize they simply chose the wrong solution. They may feel they have wasted money and direct their frustrations broadly when the problem lies with either the provider or the lack of finding a definitive problem to resolve.

Second, enterprises may be so preoccupied with the thought of what AI could do that they neglect to recognize the differences among each offering. Up until now, AI required a specialist – maybe even an entire team – that could work with and optimize the technology for outstanding results. The specialists would need to fully comprehend the challenges faced by a particular business, understand what it takes to overcome them, and have the skill set necessary to make that happen. This increased the firm’s expenses and gave a distinct advantage to larger, more established firms that have the budget to hire additional talent.

Smaller enterprises, startups, and other growing businesses may not have had the funds to do the same, putting them at a notable disadvantage when trying to compete. This is especially true for firms striving to become a stalwart on par with their competitors. However, without those individuals on staff to get the most out of AI, many businesses simply had no idea how to proceed and were thus left without the technology that could have propelled their business.

No-code AI is a powerful solution for firms that want to get started with AI quickly, effectively, and without the need for AI specialists. Business leaders can use no-code AI to bring a new level of intelligence into their organizations and take advantage of technology that was once limited to those who had the necessary talent to keep up.

3) Existing technology and selecting the wrong technology

Failed artificial intelligence projects can also be traced to what a company lacked before the tech was deployed. If the right systems weren’t already in place, its AI initiative could fail from that alone. Access to relevant data and integrations to support rapid pertinent data flow are crucial underpinnings of an efficient and responsive workflow.

Chatbots, for example, are not ideal for circumstances in which customers may call to ask a question or resolve an issue. They may have other questions come up throughout the conversation and, as humans often do, interrupt the chatbot, which would be thoroughly confused by this act. A chatbot has no idea how to switch context or identify the context of what is being said. It simply operates on a conversation tree and, when its limitations are reached, it can go no further, leaving behind a trail of frustrated customers.

By recognizing the difference between AI solutions that work and those that don’t, organizations will be in a better position to make a successful selection.

Make smart choices to avoid artificial intelligence failure

AI is an exciting, groundbreaking technology. It can learn over time and increase its ability to perform a wide variety of tasks. The technology can also work alongside humans as a digital employee that empowers them to do their jobs better while enabling them to focus on higher-value work. By understanding the potential pitfalls, businesses will be better positioned to make smart decisions that lead to a successful AI deployment.

Jonathan Crane

About Jonathan Crane

Jonathan Crane is Chief Commercial Officer at Amelia, an IPsoft Company. He has been a communications industry leader for more than 35 years. Jonathan has held numerous executive positions in corporations such as ROLM, Savvis, Lightstream, Marcam Solutions, and MCI. He was one of the key architects of the early success of MCI and was brought back to lead the troubled Worldcom out of bankruptcy. As the Chief Strategy Officer, he orchestrated the acquisitions of key strategic assets. Most recently, Jonathan was the Chairman of the Board and President of Savvis.

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