These Are The Reasons Why More Than 95% AI and ML Projects Fail

Published in

VSINGHBISEN

4 min readAug 19, 2019

Artificial Intelligence (AI) market is posing to become billions of dollar industry in next few years, as global spending by nations on AI is likely to touch around $35.8 billion in 2029 which reports a growth of 44% over the amount spent in year 2018.

Such, impressive growth shows, AI holds huge potential to attract big organizations as well as small enterprises attracting them to implement AI-enabled services for better growth in the business.

However, working with AI you need an immense amount of meticulous data to train the model so that it can give the precise results.

Actually, to train an AI or ML model a high-quality training data is required, which is a challenging task for AI developers or machine learning engineers.

As, to get the human-like complex decisions from machines you need enormous volumes of accurately labeled and annotated training data through images or videos.

With the growing AI demand, data science team are under pressure to complete the projects but acquiring the training data at a large scale is the real challenge they are facing right now.

Why Do Enterprises Face Data Issue for AI Strategy?

As per the research by Dimensional Research and Aiegion survey, enterprise machine learning is just beginning, machine learning engineers or data scientist team size is smaller and the expertise of growing data science is not yet compatible to matured ML projects expertize.

And acquiring the training data is the biggest challenge for the success of an AI project. As per the survey, 96% of the AI projects fail or not started due to lack of training data technology that leads to the inability to train the ML algorithms resulting failure of the project.

Half of the AI Projects Never Get Deployed

Nowadays, big organizations or enterprises having more than 100,000 employees are more keen to implement AI strategy into their business model — but only 50% of such enterprises currently have one.

The survey reinforces that AI is at nascent in the enterprise, as 70% of them firstly invested in AI/ML projects in the last 24 months.

While on the other hand, over half of the enterprises report they have undertaken fewer than four AI and ML projects. And only half of the enterprises have released AI/ML projects into the development to build a fully-functional model.

And as per the survey research only, less than two-thirds of them indicated that their ML project reached the completion point that is being trained on labeled training data sets which are relatively at the initial stage in the ML project life cycle.

And more revealing immaturity of ML in the enterprise, is that why half of the projects never deployed.

Survey Statistic Why AI/ML Projects Fail:

78% of AI/ML Projects Shut ate some stage Before Deployment
81% Admit the process of training AI with data is more difficult than they expected
76% struggle by attempting to label or annotate the training data on their own.
63% try to build their own labeling and annotation automation technology.

And as per the research, around 40% of failed projects reportedly stalled during training data-intensive phases like training data preparation, algorithms training model validation, scoring and post-deployment enhancement.

Top Reasons for AI Projects Failure:

Lack of Expertise (55%)
Unexpected Complications (55%)
Training Data Problems (36%)
Lack of Model Certainty (29%)
Deficient Budget (26%), and
Lack of Efficient Staff (23%)

As already bespeak, around two-thirds report that ML projects not able to progressed beyond proof of concept and algorithms development to the phase of training data.

Mostly this phase is not favorable for such developments, as 80% report that training the algorithms is more challenging than the AI engineers have expected.

Reasons Why Training Algorithms Data is Challenging:

Not enough data.
Data not in a usable form.
Bias or errors in the data.
Don’t have the tools to label the data.
Don’t have the people to label data.

Nevertheless, less than 4% have reported that training data has presented without any problems. Almost three-quarters of the AI engineers indicated that they try to label and annotate training data on their own.

While around 40% suggested they rely wholly or partially on off-the-shelf, pre-labeled data to train their AI model.

Such issues, lead to 70% companies utilizing external services for their AI or ML projects with most of them focusing on data collection, labeling and annotations.

As AI and ML engineers are rare to find and also expensive, the enterprise should find out external solution service providers for critical activities like data labeling and model scoring. This evidence is enough to outsource data annotation for more improved outcomes.

Summing-up

Enterprises designate a strategic value to their machine learning initiatives and expect AI and ML shall improve their businesses aspects and would be also disruptive in their sectors.

However, AI and ML projects are still at an early stage of development at enterprises. And data science and AI engineer teams are relatively small and experienced which affects the efficiency and outcome of these projects.

This article was originally submitted at www.vsinghbisen.com