worldwide spending on artificial intelligence (AI) systems will reach
$35.8 billion in 2019, and 84% of enterprises believe investing in AI
will lead to greater competitive advantages (Statista).
However, nearly eight out of 10 enterprise organizations currently
engaged in AI and machine learning (ML) report that projects have
stalled, and 96% of these companies have run into problems with data
quality, data labeling required to train AI, and building model
confidence, according to information released today from Alegion.
Data issues are causing enterprises to quickly burn through AI project
budgets and face project hurdles. The new report, “Artificial
Intelligence and Machine Learning Projects Obstructed by Data Issues”
was conducted by Dimensional
Research. The findings include feedback from 227 participants
including data scientists and business stakeholders involved in active
enterprise AI and ML projects, addressing the maturity of ML in the
enterprise, today’s ML project challenges, and the tools and resources
used in these projects.
“The single largest obstacle to implementing machine learning models
into production is the volume and quality of the training data,” said
Nathaniel Gates, CEO and co-founder of Alegion, a training data platform
for AI and ML initiatives. “This research reinforces our own experience,
that data science teams new to building ROI-driven systems try to tackle
training data preparation in house, and get overwhelmed.”
Large businesses with more than 100,000 employees are most likely to
have an AI strategy – but only 50% of them currently have one, according
to MIT Sloan Management Review. Alegion’s survey reinforces this
finding that AI is still nascent in the enterprise:
70% report that their first AI/ML investment was within the last 24
Over half of enterprises report they have undertaken fewer than four
AI and ML projects
- Only half of enterprises have released AI/ML projects into production
To get AI systems off the ground, training data must be voluminous and
accurately labeled and annotated. With AI becoming a growing enterprise
priority, data science teams are under tremendous pressure to deliver
projects but frequently are challenged to produce training data at the
required scale and quality.
Alegion’s survey respondents echoed these observations:
- 78% of their AI/ML projects stall at some stage before deployment
81% admit the process of training AI with data is more difficult than
76% combat this challenge by attempting to label and annotate training
data on their own
63% go so far as to try to build their own labeling and annotation
71% of teams report that they ultimately outsource training data and
other ML project activities
Enterprise data scientists, other AI technologists and business
stakeholders involved in active AI and ML projects were invited to
participate in a survey on their company’s use and development of AI and
ML projects. The survey was administered electronically, and
participants were offered a token compensation for their participation.
A total of 227 participants completed the survey, representing five
continents and 20 industries.
Download a free copy of the report here.
Alegion is an Austin-based technology company that provides the most
powerful and flexible annotation platform for training data in market.
It accelerates model development for the most sophisticated and
subjective use cases. It uses integrated ML and has unique capabilities
like conditional logic, iterative tasks, multi-stage and workflows, that
are essential for high quality at scale. The entire process is managed
by our highly experienced and consultative team that configures and
executes the platform to meet your business needs.
For more information, visit www.alegion.com.
About Dimensional Research
Dimensional Research provides practical marketing research to help
technology companies make smarter business decisions. Our researchers
are experts in technology and understand how corporate IT organizations
operate. Our qualitative research services deliver a clear understanding
of customer and market dynamics.
For more information, visit www.dimensionalresearch.com.