Why 85% of the Artificial Intelligence Projects Fail? | CustomerThink

Even after opting for AI, problems are not solved because of the absence of suitable data. Algorithms can’t work properly with data which is not good. So, a lot of time of team is wasted. Moreover, AI is not a single process or technology. Skilled employees are required that take high salaries and if your budget is not much then in the absence of AI experts, clients will not interested in taking AI services of your company. These are the common reason of failure of AI in most of the mobile app development companies.

Artificial Intelligence is a system that is capable of planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation. It may also to some extent be capable of social intelligence and creativity. Today AI is capable of recommending what to buy, entertaining its users, detect frauds in credit cards, and even recognize faces in a picture. As of now AI has been categorized into two – Narrow AI, and General AI. Example of Narrow AI can be Apple’s SIRI or Microsoft’s Cortana while General AI example can be cited as the likes of SKYNET in the Terminator (with the later not a reality as yet). In the coming years, AI might be able to write essays, drive vehicles, and even go to the extent of performing surgeries.

Artificial Intelligence has brought about a great deal of success in the various projects it has been implemented. Certain data consulting firms have also integrated AI into their projects that will help advertising and media agencies in furthering their campaigns. However, it is an agreed upon fact that not all companies implementing AI have been successful. A whopping 85% are on the other side of the line. The barriers as per some of the surveys have been pushbacks from senior management and the failure in impressing upon them. The management first of all sees the return on investment. This is a great hindrance. Projects that look great sometimes often find the dust.

A report from dimensional Research states that 8 out of 10 AI projects had failed while 96% ran into problems with data quality, data labelling, and building model confidence. As another example of this failure, representatives from Facebook, Amazon, Microsoft, and Adobe all chose to use the AI powered tool called Neural Machine Translation as it was capable enough to localize content in 72 languages very quickly. However, the technology (and the tool as well) was just being used by 23% of all those present.

Some of the reasons that these projects fail could be because:

1. The Sharks
2. Communication Failure
3. Fail Before You Start
4. Absence of a Data Warrior
5. In-house Talent/ Software
6. Fear of Loss of Job
7. Start simple

1. The Sharks: When the implementation of an AI project is mentioned, then first there will be the sharks around to disrupt like “Let’s go ahead with the (name of different project). It also costs far less. ” The question is not the type of the project, rather it is the return on investment (ROI) from a project that lures them the most. So what you do then? Ensure that your first AI-based project is business oriented, fulfills the KPIs, and also aligns with the vision and mission statement of the organization. Believe the success of such a project means a lot for you and the business. The management is going to love you for that

2. Communication Breakdown: When you are a Data Scientist and are communicating with your management using the technical jargon, this hurdle is bound to show its face. The management has nothing to do with how you are going to go about with the project. They already have enough on their plate to look after. Don’t educate them with AI, tell them how it is going to grow the company. Speak in terms of dollars and not gigabytes. Also, the company’s priorities must align with your project. They will be happy enough to hear you and give you a go.

3. Fail Before You Start: Yes. Something you might not tend to do but it’s a life saver. Imagine having spent chunks of dollars on your project and then hearing the client tell you that the specifications are not what he wanted. You are doomed. So, before you actually start your project, prepare some outputs and reports that you can show to your client and get him to agree to what he just saw. Even if the client might not agree, you have not lost anything. You now know what the client is willing to have, and you can start with the clients specifications.

4. Absence of a Data Warrior: Organizations would generally prefer giving a chance to newbies, kids who just graduated, or have hardly any working experience. The reason is plain simple – save the dollars. That is where the big mistake is. In the name of saving the dollars, they are actually being wasted away. Inexperienced fellas will come up with ever new excuses of not having completed the project (or even started it). What the organization requires is person who has significant experience on his hands, has developed an AI project, and also shipped it some client organization.

5. In-house Talent/ Software: It is a nice option to grow talent in-house, but if every time the same talent is being used by the organization, how can they ensure they in-house talent has the latest knowledge. Is the in-house talent mixing with the other community or not? If no, the company needs to use the developers from overboard.

6. Fear of Losing Job: While AI can bring out drastic changes and profits to the organization, for those who don’t know, AI is capable of doing what we humans do today. From performing physical tasks to making logical decisions, AI can handle all. This in its most advanced stages could be a threat to the employees of the organization that implements it. As such, there might be people who stand in the way of implementing AI else they lose their jobs.

7. Start simple: You will get 0% value for your AI project in the absence of implementation of simple rules. It is rumour that complicated projects get success but overcomplicated projects consume very much time. So, project should be started in a simple way.

Besides, the above mentioned reasons projects may also fail due to the misalignment of expectations versus the reality of the project within a given its time frame. Despite all the halo about AI, certain things can and have gone wrong. As an example, a self-driven vehicle used as an Uber test pilot ran into problems when it killed a pedestrian. One could mention that the algorithm or the program was not properly coded. In other cases, it could be the incorrect data that is provided as an answer to some query by AI.

Another reason for the failure of AI systems could well be the incomplete datasets. Whenever an AI system needs to take over it has to be trained with all the questions and their answers present in a dataset. In case of incomplete datasets during the training sessions, the AI would be unable to respond to the situation in real-time.

Also, algorithms could go wrong. This is because they are developed by some human beings. It is very much possible the person who developed the algorithm is biased towards a particular sect. In a job selection process, if the algorithm is biased towards a particular sect, the hiring company might be at a loss to find the best suitable candidate.

Sometimes the sensors may fail to cause problems for the AI. In such a case, the AI must fail gracefully, maintaining the original state that was (a complete rollback). The AI system needs to be trained comprehensively to understand any scenarios that it may come across. Remember, whenever you cut corners by design, or fall short of the correct information, failures are bound to happen.