As per a survey by Alegion, 96% of businesses encounter challenges related to training data labeling and quality in Machine Learning projects.
According to the research, Artificial Intelligence is still an emerging field and hence we are facing training data issues for AI/Ml initiatives.
IDC predicted the worldwide spending on AI systems in 2019 to reach $35.8 billion and above 80% of businesses believed in investing in AI can lead to bigger competitive advantages. However, as per Alegion, 8 out of 10 businesses who have invested in AI and ML have their projects stalled and more than 90% of these organizations are facing problems with data labeling and data quality that are required to train AI models and building model accuracy.
Data issues are causing businesses to burn quickly through budgets of AI projects and face hurdles. The findings of the report have feedback from 227 participants that comprise of business stakeholders and data scientists who are involved in active enterprise-level AI/ML projects. The report also addresses the maturity of ML in the organization, challenges of today’s ML projects, and the resources & tools used in such projects.
According to Nathaniel Gates, CEO & Co-founder, Alegion, volume, and quality of the training data is the single largest obstacle for implementing ML models. Since Alegion also offers a training data platform for AI/ML initiatives, this research also reinforces their experience that team of data scientists new to developing ROI-driven systems gets overwhelmed when they try to handle training data preparation in house.
Large enterprises having 100,000+ employees are most likely to have an AI strategy. However, only 50% of them have one currently as per a review by MIT Sloan Management.
From the Alegion’s survey we can confer that AI is still in its nascent stage at the enterprise-level:
- 70% of the businesses report that their investment in AI/Ml was in the last 24 months
- Over 50% of organizations have reported that they have engaged themselves in less than 4 AI/ML projects
- Only half of the businesses have released AI and ML projects into production
Training data has to be large enough and should be accurately annotated and labeled to get the AI models off the ground. Since AI is a growing organization priority, data scientists are under tremendous pressure to deliver projects but are faced frequently with challenges to produce quality training data on a large scale.
The following observations were echoed by respondents of the Alegion’s survey:
- More than 75% of their AI/ML initiative is stalled before deployment at some stage
- Above 80% admit the training process of AI using data is more difficult than their expectations
- About 75% try to tackle this challenge by trying to annotate and label the training data on their own
- Around 63% try to build their data labeling and annotation automation platform
- Over 70% of teams have reported that they outsource training data and other ML activities ultimately
About RightClick.AI
If you are quite overwhelmed with the challenges your AI/ML initiatives are facing due to Training Data, you can reach out to us at info@rightclick.ai. RightClick.AI specializes in providing high-quality and scalable Data Labeling services for enterprise-level AI/ML initiatives.