Three Transformational Compute Technologies Verified to Accelerate AI and Business Value


The field of analytics has long empowered businesses to extract value from their data. Given the competitive demands of the modern digital economy, however, businesses want and need to do more. Fueled by a desire for greater accuracy along with faster, more predictive, and more granular insights, businesses are turning to the rapidly progressing field of artificial intelligence (AI).

The concept of AI has existed for decades. But now, machine learning (ML), and a subset of ML called deep learning (DL), makes it possible to train AI software faster and help it to deliver far greater accuracy. ML algorithms learn from the data, which empowers the software to make predictions and act on them. These algorithms are now even improving over time, based on the data they collect.

The availability of relatively low-cost compute, the abundance of seemingly unlimited data, and new breakthroughs in AI models and algorithms mean that the possibilities for AI initiatives are now practically endless. Today, AI is automating decision making, improving customer interactions, streamlining operations, and helping companies to take advantage of new product and market opportunities.

Through AI, data offers one of the greatest-ever chances for businesses to attain a competitive advantage. Even with all this excitement, questions remain:

• What business benefits are realistic with AI initiatives?
• What technologies, if any, foster better results?

ESG recently conducted a detailed study to answer those questions and better understand and quantify the relationships between modern compute technologies, the use of AI-based workloads, and resulting business outcomes. From this research, ESG identified that:

• AI presents a tremendous opportunity to extract and capture value trapped in data.
• By using AI effectively, firms are becoming stronger, more successful, and more competitive.
• Some compute technologies are far better positioned than others to support corporate AI initiatives.

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Type: White Paper


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