AI is not limited to those with deep pockets

Oct 03, 2022
Artificial Intelligence

Mid-sized enterprises are overcoming AI challenges as organizations develop best practices, and infrastructure that best suits their needs.

Credit: Dell

The benefits of artificial intelligence (AI) are not limited to the largest enterprises with the deepest pockets. A new IDC survey of 2,000 IT and line-of-business executives who influence AI purchases in mid-sized organizations found that about a third of companies are active users of AI, while the others are at various stages of evaluating the technology. Insights from data, analytics, and AI are guiding organizations to operate leaner, more efficiently and more competitively. They are using AI to improve customer satisfaction, lower infrastructure and staff costs, automate decision making to reduce complexity and simulate business scenarios to uncover opportunities.

Like all new technologies, AI does pose some significant challenges for organizations. It can be difficult to assemble the right teams to manage the entire lifecycle of AI development and deployment. Maintaining data privacy remains a concern, namely around data used for AI training and that which is generated by an AI application. And organizations struggle with developing a scalable AI program using the right mix of hardware, software and support.

Choosing the Right AI Infrastructure

Some organizations run AI applications in infrastructure siloes while users have integrated their AI systems with the rest of their data center and cloud environments. More advanced AI users are also future-proofing their AI infrastructure investments with the latest high-performance computing (HPC) technologies, edge functionality, and pay-as-you-go consumption-based models for on-premises services.

Additional benefits of integrated AI systems:

  • Decreased research and development (R&D) costs: Teams conducting R&D in-house report significantly reduced costs with AI on-premise where data can be read and written faster than from a different location.
  • Accelerated AI model training: When performance for AI model training is a key infrastructure requirement, many sophisticated organizations opt for AI-specific infrastructure with much higher GPU attach rates.
  • Scalability:  Moving AI models into production requires infrastructure that can scale for anticipated demand. 
  • Ease of data integration: New AI storage solutions that did not exist a few years ago make integration with data repositories easier.

Deploying AI at Scale

Building a successful AI team can be challenging due to the high demand for technical personnel and the need for training and management approaches across different business stakeholder groups. But investment in education and training for IT and line of business managers can drive innovation across the business as teams see what’s possible using massive amounts of data and analytics.

The study found that organizations that have invested the time, budget and focus to deploy AI applications at scale have enjoyed a higher return on investment (ROI) than those companies who have dabbled with AI. Deploying AI at scale enables companies to roll out new services quickly with minimal additional infrastructure investment. AI can provide companies a competitive advantage by reducing costs through better efficiencies and forecasting risk and customer behavior.

Dell’s AI solutions portfolio is particularly suitable for helping AI evaluators speed up their AI development and deployment, catch up with the early adopters, and improve their competitiveness. With Dell Validated Designs for AI, companies of all sizes can quickly build AI solutions that provide deep business and operational insights fast. Download the new IDC Analyst Brief to learn more about what is separating AI leaders and laggards in the mid-market.


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