Here is why your AI projects Are Failing

Artificial intelligence has noted massive growth in the last few years. Many businesses are investing in artificial intelligence to enable them to gain a competitive edge. Sadly, the majority of companies who claim to have invested in Artificial Intelligence have failed to generate value from it.

Based on a survey of more than 2500 executives conducted by MIT Sloan Management Review and BCG Gamma, seven out of 10 businesses report insignificant profits from their AI initiatives. The erroneous idea that artificial intelligence is somehow magical and can invent something from nothing has lead many projects astray.

Before we hurry to shift the blame on the technology, we need to recognize that AI is a machine that learns from the input data you provide; it has no innate intelligence. Various other factors hinder the success of your AI project. Let’s explore them in detail.

Lack of data strategy

Data lies at the heart of every AI project. It is essential for training any machine learning algorithm. Therefore, before you start developing an AI model, you need to build a sound data strategy — what sort of data is required, where can you find the data, and is the data appropriately labelled. If you feed your AI model with a deficient dataset, you’ll get wrong results from it.

Lack of a firm grasp of the business problem

AI cannot solve all business problems. You need to define the issue you are attempting to solve. Your goal must be specific, and it should deliver value to your business and raise your KPIs.

Data is not enough; building successful AI solutions requires a basic understanding of business problems and customer needs.

Lack of Focus on meaningful use cases

Major mistakes companies make is implementing technology only for the sake of having the technology. Companies must be prepared to establish use cases, test multiple use cases, and develop agile innovation methodologies to execute successful projects.

Firms should first concisely articulate the critical business problems it wants to address. Once defined, these objectives then drive and inform what transformational interventions to pursue, including AI

Select projects that create the highest impact on your KPI such as reducing operations cost, Increasing customer experience etc

Unrealistic Expectations

Developing an AI model is a lengthy process with trials and errors. It would be best if you worked patiently through it. Leaders who don’t fully understand AI are likely to have unrealistic expectations and quickly get frustrated with the process; detractors can be quick to call efforts failures and urge abandonment of the projects.

At the heart of the matter may be a general lack of understanding about AI capabilities and requirements. “At this point, many enterprises have inflated expectations from AI solutions,” says Anil Vijayan, vice president at Everest Group. “This often creates a mismatch between what is expected and what is achievable.”

Notwithstanding the set back listed above, AI is still making a significant impact across several industries. In 2020, the global AI software business is foreseen to grow nearly 154 percent year on year, striking a forecast size of 22.6 billion U.S. dollars. We need to incorporate the right attitude and approach to AI solutions from the onset of a project to derive meaningful results