The Race to AI Value: 3 Themes From New AI/ML and Deep Learning Research Report
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.
- Leaders are optimistic about their AI/ML initiatives. Two-thirds of respondents said their organizations are planning to significantly increase their AI/ML investments over the next five years, citing the technology’s ability to power innovation as their top motivation. Organizations understand they need to leverage their AI/ML investments to do more than just automate simple tasks: They need to innovate to stay competitive and drive revenue. But just because they know it’s important for AI/ML initiatives to drive revenue doesn’t mean companies necessarily are there yet — especially since many organizations are in the early stages of implementing AI/ML.
- Organizations are prioritizing deep learning. Deep learning uses artificial neural networks to process unstructured information like text and images. This subfield of AI/ML is driving the next wave of innovation powered by recent advances in computer vision, large language models, and recommendation algorithms (i.e., learning a user’s preferences and providing content unique to their needs). Three-fourths of respondents said deep learning is very important for fostering competition and innovation in their industry. Scaling this type of compute-intensive AI/ML presents an array of challenges, which we’ll dive into next.
- Organizations face barriers to AI/ML scale. AI/ML initiatives need to scale significantly to drive innovation and revenue. Survey respondents flagged several challenges hindering that scale, with the difficulty of customizing models as the top pain point. An AI/ML talent shortage is one reason why: Organizations lack adequate staff with the expertise to train and tune models in house. Additionally, many organizations lack sufficient infrastructure for the compute-heavy workloads associated with advanced AI/ML. Considering 65% of respondents already struggle with limited space for server racks, scaling AI/ML will only exacerbate this issue.
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.