99% of participants in ‘The Peltarion AI Decision Makers Survey: Are enterprises ready to go deep with AI?’ expressed belief that deep learning would transform their industry, and 98% said that their company were planning to invest in deep learning over the next three years.
Almost a third (32%) said that deep learning would ‘totally’ transform their industry, while 26% said other kinds of machine learning would have this effect.
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However, only 60% said they were confident that they knew what deep learning is and how it works, despite participants having direct responsibility for overseeing AI.
The sample was made up of 350 CIOs and senior AI decision makers in the UK and the Nordics, all of whom worked for organisations of at least 1,000 employees.
“But the path to reaching that potential is inhibited by lack of familiarity with deep learning. With investment growing, we can expect to see more industries benefiting from this under-explored, yet incredibly powerful subset of AI.
“However, the barriers to adoption must be overcome before businesses can reap the benefits.”
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These barriers, according to Peltarion’s survey, include corresponding tools being too complex for users to reap the technology’s potential, which 71% identified as an issue.
Additionally, 41% said that they weren’t able to collect and organise the appropriate data for deep learning processes, another 41% pointed towards a lack of understanding regarding deep learning models, and 36% cited integration as a setback when it comes to investment in deep learning.
“In order to increase adoption of deep learning, companies need access to the right tools and skills,” Crnkovic-Friis continued. “Operationalising AI, and deep learning specifically, will be key in doing this.
“Not only should experts offer guidance, spreading the knowledge of how it can be used within their companies, but deep learning should be operationalised to increase the speed of model development and experimentation, ease integration and deployments and make deep learning more ‘AI Ready’.
“Once a few of these projects are up and running, the costs, on-site skills and infrastructure required to keep deep learning operational and launch new projects gets lower each time.”