Reasons Why AI Projects Fail and Possible Solutions That Could Fix Them

Over the past decade, artificial intelligence has made the transition from a sci-fi fantasy movie to becoming an important part of our everyday lives. This includes robotic process automation, chatbots, wearable, and IoT devices, and many other technologies that we rely on every day. However, developing such a project can be difficult since it requires a lot of training data and there are many human factors that need to be accounted for during the development process. Let’s take a look at some of the most common reasons why AI projects fail and some possible solutions that could fix them.

Veracity and Volume of Training Data 

When looking at the veracity and volume of the data, companies need to make sure that they are using the right training dataset, it has been annotated correctly and there is enough training data to allow the machine to perform its core functions with a high level of accuracy. Simply obtaining the training data can be difficult by itself and the annotation process is very time-consuming. This is why a lot of companies choose to outsource such tasks to Mindy Support because we have the necessary human resources to actualize even the largest data annotation projects. 

Also, Mindy Support can assist you in generating training data. For example, we have a client who is working on developing an AI chatbot and we created tens of thousands of dialogues on more than 120 different inquiry topics. This allowed the customer to save time and resources since we offered them a one-stop-shop for all of their data annotation needs. 

Business Processes and People 

Right now, AI is at a stage where it augments human capabilities instead of replacing humans altogether. For example, if we take a look at the way chatbots are used in the customer services area, they are only able to handle certain types of inquiries and human support agents are still required for more complex inquiries. Also, there are many customers who prefer to speak with a real person when they call in. The same is true for other industries that rely on robot process automation to perform repetitive tasks. 

Therefore, prior to implementing an AI solution, companies need to clearly define the role AI will play and they will need to implement strategies to address challenges quickly in advance of an AI initiative. 

Accounting for Unexpected Behaviors A lot of times researchers will train a system to perform tasks that are governed by clear rules, but there are almost always circumstances that the system encounters that do not follow those very same rules. As an example, let’s take self-driving cars. AI development teams assemble a lot of training data and hire annotation teams from companies like Mindy Support to label everything the AI system needs to recognize. This includes everything from pedestrians to traffic signals and all of the rules of the road.

However, very often human drivers do not always follow the rules and will make and will run a stop sign or make an illegal turn, for example. When the AI system encounters such a scenario, it will not know how to respond because it was trained according to the rules of the road. This is why a lot more training data and annotations are necessary to allow the machine algorithms to understand human behavior and better comprehend the real world. 

Data Security and Governance 

A lot of companies only focus on the IT security of their product and pay much less attention to the level of data security offered by their vendors. It is very important to make sure that the companies you work with make security a priority because otherwise, you could end up compromising trade secrets and other proprietary information. Mindy Support places a lot of emphasis on security and we received ISO 27001 accreditation. This best-practice approach helps us manage our information security by addressing people and processes as well as technology. 

Contact us today to see how we can help you.