The AI industry is picking up steam. IDC predicts that spending on AI systems will grow to nearly $35.8 billion in 2019 worldwide and will more than double to $79.2 billion in 2022.
Breaking news: A global survey shows AI is still in its infancy and poor data is cited as a major barrier of development.
Enterprise AI | Datanami | Robotics Business Review
We sponsored Dimensional Research to conduct a global survey of nearly 300 data scientists and other AI professionals in large companies across 20 industries to gauge the maturity of machine learning (ML) in the enterprise, to understand today’s ML project challenges, as well as the tools and resources used in these projects.
The most staggering stat to come out of the survey was that 96% of companies pursuing AI projects have run into problems with data quality, data labeling required to train AI, and building model confidence. 4 out of 5 respondents admit that training AI with data is more difficult than expected citing issues such as:
- Bias or errors in the data
- Not enough data
- Data not in a usable form
- Don’t have the people to label data
- Don’t have the tools to label the data
These difficulties have led 71% of companies to outsource some AI/ML activities including data collection, data specialist personnel, and data labeling. In a fascinating cross analysis they found that companies that outsource data labeling are more likely to be in production.
For a more in depth understanding of the intricacies of these findings check out our white paper What data scientists tell us about AI model training today. This white paper analyzes the findings and clearly defines what data science teams are looking for to advance their projects.
