New study finds organizations not prioritizing AI/ML and Deep Learning are losing competitiveness
The artificial intelligence (AI) market is expected to reach $554.3 billion in revenue by 2024 as AI technologies gain adoption across industries. Fueling this growth will be organizations overcoming barriers to deploying and scaling AI,machine learning (ML), and deep learning initiatives.
These organizations need not look far to find companies already actively engaged with enterprise AI. . The research highlights 26% of top companies have already scaled their AI/ML initiatives across the entire organization, leaving the majority of companies well behind in the next wave of technology advancement. To keep pace with the rapid evolution of AI, machine learning (ML), and deep learning (DL), technical leaders and their teams need to determine which use cases will drive revenue and innovation for their business, and identify how to deploy them at an enterprise level.
To gauge where companies are in their AI/ML journeys, SambaNova commissioned a survey of 600 AI/ML, data, research, customer experience, and cloud infrastructure leaders across six industries. The results highlight the motivation behind AI/ML and deep learning spending and the barriers organizations are facing to scale their initiatives.
Despite obstacles, leaders are optimistic about the future of AI/ML and deep learning
The report revealed that while most people are hopeful about the potential of AI/ML, organizations are facing obstacles to scale their initiatives, including skills gaps and difficulty customizing models. As the need to innovate and drive revenue grows, technical leaders face challenges in taking the next steps on their AI/ML paths. By not untangling the complexities of scaling AI/ML, organizations risk losing a competitive advantage that only AI technologies can deliver.
With this in mind, let’s take a closer look at three primary themes from the report,The Race AI Value: How to Scale AI/ML Ahead of Your Competition.
Download the complete report for more insights
More than a quarter of top enterprises’ AI initiatives have reached widespread production. For the other three-quarters of companies, determining how to scale AI/ML needs to remain a top priority in 2022 and beyond. Download The Race to AI Value: How to Scale AI/ML Ahead of Your Competition for a deeper dive into the AI/ML aspirations of today’s technical leaders and learn how to overcome some of the most common challenges organizations face in scaling AI/ML.