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The Secret to Remaining Competitive in the AI/ML Landscape? Identify and Overcome Barriers to Scale

In this special guest feature, Marshall Choy, SVP of Product at SambaNova Systems, focuses on several trends that are affecting the future of AI/ML. Marshall is responsible for product management and go to market. He brings extensive experience leading global organizations to bring breakthrough products to market, establish new market presences, and growing new and existing lines of business. He previously served as Vice President of Product Management and Solutions Engineering at Oracle until 2018. Prior to joining Oracle in 2010 when it acquired Sun Microsystems, Marshall served as Director of Engineered Solutions at Sun, focused on the areas of HPC, Webscale infrastructure, and Enterprise Applications.

Artificial intelligence (AI) and machine learning (ML) are taking center stage in 2022 as more than three-quarters of technical leaders see AI/ML as essential to driving revenue at their organization. But while AI/ML is powering market and vertical trends, many organizations still face pain points that prevent them from scaling effectively. 

Meanwhile, 26% of top enterprises do have viable AI/ML initiatives deployed at scale, winning a competitive advantage over companies that haven’t yet. To gauge where enterprises are in their AI/ML journeys, SambaNova surveyed 600 AI/ML, data, research, customer experience and cloud infrastructure leaders at the director level and above. The survey captured 100 responses from each of six industries, including financial services, healthcare and life sciences, retail and e-commerce. 

To solve scale issues, the survey results revealed two key trends for a path forward. As the AI/ML talent shortage continues, more companies are customizing at scale through a partner to successfully deploy AI. Additionally, businesses are harnessing the increased efficiency of chip architectures tailored to AI/ML — and reaping the benefits of reduced power consumption. 

If you’re unsure where to start on your AI/ML journey, you’re not alone. Whether you’re improving existing initiatives or building entirely new infrastructure, keep one thing in mind: Invest in better AI/ML tools today to keep pace with competition in 2022. 

The survey results are in: AI/ML is here to stay—but scaling is hard 

The ultimate paradox persists for organizations in 2022: Industry leaders recognize a need to innovate, increase revenue and drive operational efficiency with AI/ML solutions — yet they can’t effectively scale in a competitive landscape. To succeed with AI/ML enterprise-wide and come out on top, industry leaders need to invest in deep learning, reinvent their infrastructure and customize their strategies to specific use cases. 

Our report revealed several trends about the changing face of AI/ML in 2021. Let’s explore our key findings, and what these results mean for you and your business in 2022. 

1. Organizations have high hopes for their AI/ML initiatives. 

From creating new products and services to investing in new lines of business, technology leaders are ready to adapt in a rapidly evolving market by investing in ML to power innovation, improve operational efficiency and keep up with competitors. Over two-thirds of organizations

(70%) plan to allocate more than $100 million of IT budget toward strategic technology goals. It’s clear that organizations are looking to push their AI/ML investments further than simply automating tasks — and you should, too. 

Based on these results, it’s no secret that competition will be fierce in 2022. Much like the internet boom of the early 2000s, AI will significantly shake up the Fortune 500 — startups and investors alike are recognizing the potential of AI solutions to help them remain competitive. The financial industry is investing particularly heavily in AI/ML, with a staggering 81% of financial services respondents planning to increase their investments in AI/ML — the highest percentage in any industry. 

2. Organizations are diving head-first into deep learning. 

Deep learning, a subfield of AI/ML that uses artificial neural networks to ingest and process unstructured data like text and images, is increasingly essential in almost every industry. Three-quarters of respondents (75%) say improving access to deep learning is very important for fostering competition and innovation in their industry. 

From recommendation algorithms to natural language processing (NLP), the widespread use of machine learning presents a challenge for all but the most advanced computing infrastructure. Despite the clear benefits of deep learning, organizations remain limited by insufficient infrastructure and a lack of clear understanding of specific use cases. Furthermore, few business leaders grasp its transformative potential. Be sure to improve your infrastructure, and focus on education on the business side by training team members on how deep learning can support business goals. 

Deep learning is perhaps the best example of a rising trend in the future of AI: Accessibility is driving new AI use cases across industries. Against the backdrop of a global pandemic and supply chain shortages, 2022 will see a profound push for AI accessibility as countries seek to secure an economic advantage in the global market. As more organizations gain access to the power of AI, there will be new, transformative use-cases across industries, including innovations in biotechnology, supply chain and logistics and financial services. 

3. Overcoming barriers is key to AI/ML scale. 

As organizations struggle to handle compute-heavy workloads with dated infrastructures, one thing is clear: AI/ML specific chip architectures are essential to scaling effectively. We can no longer rely on Moore’s Law, meaning the number of transistors in a microchip won’t double every two years. Facing an infrastructure crisis, more than half of respondents (53%) strongly agree they’ll run out of computing power in the next decade without new computing architecture. By overcoming these challenges, your business can effectively scale AI/ML in a competitive market.

To overcome barriers to scale, a full-stack approach to AI is key. AI is pushing computing processing past its limits. Current GPUs and CPUs aren’t able to keep up with the runtime requirements for probabilistic computing applications. As the market continues to mature and evolve, AI providers will focus more extensively on full-stack hardware and software systems that are designed specifically for AI deployments. 

Deploy AI/ML at scale or risk falling behind 

If your inbox is anything like mine, you’ve been flooded with predictions for 2022. Beyond the state of the supply chain and the longevity of the Great Resignation, one theme remains top-of-mind: Artificial intelligence is here to stay. 

Companies that aren’t innovating with AI to scale faster than the competition won’t win out. Across industries, we will see an increased focus on software and hardware systems that are specifically designed for AI and can handle massive amounts of data. As companies prioritize accessibility amid ongoing supply chain challenges, a need for new AI use-cases will accelerate technology adoption across sectors. 

Beyond using AI/ML to streamline operations, companies need to drive innovation by deploying AI/ML at scale to come out ahead. The AI/ML revolution is so disruptive that companies who fall behind, will be left behind. It’s time to move quickly to embrace, scale and innovate with AI. Read the data study to learn more.

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