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New Insights from EMA Research on AI Network Preparation

Introduction
The landscape of enterprise technology is rapidly evolving as artificial intelligence (AI) begins to permeate various operational sectors. To effectively leverage AI applications, organizations must first ensure their networks are equipped to handle the unique demands posed by these technologies. A recent report from Enterprise Management Associates (EMA) has unveiled how early adopters are realigning their networks to prepare for the influx of AI-driven workloads.

Research Overview
Authored by Shamus McGillicuddy, the report titled "Readying Enterprise Networks for Artificial Intelligence," is based on a survey conducted with 269 IT professionals across North America. It aims to serve as a practical guide for IT decision-makers who are venturing into AI initiatives. As the research indicates, the volatile nature of AI workloads necessitates an adaptable and robust network infrastructure capable of ensuring high-speed connectivity with low latency and non-deteriorated service quality.

Understanding AI Workloads
AI workloads significantly differ from traditional data traffic; they often produce unpredictable surges in data flow, requiring organizations to have optimally configured data center networks and wide-area networks (WANs). Many enterprises are compelled to rethink their current architectures and implement substantial upgrades to address these complexities effectively.

Key Findings from the Research
1. Security Risks and Budget Constraints: A significant challenge that organizations face in preparing their networks for AI implementation is the potential risks to security, cited by 39% of respondents. Furthermore, budget issues, which affected 34% of organizations, and the struggle to keep pace with AI innovations, indicated by 33%, present hurdles that must be methodically addressed.

2. AI Centers of Excellence: The report highlights that 42% of companies have established dedicated AI centers of excellence. These centers play a pivotal role in guiding strategies across technical teams and business units, fostering a streamlined approach to AI integration.

3. Automation and Quality of Service: Organizations that have successfully automated quality of service or specific routing policies tailored for AI-related traffic report better preparedness for AI technologies. This can facilitate smoother transitions and adaptations to network configurations as demands evolve.

4. Network Observability Tools: The research also emphasizes changes to observability tools that improve network insights and management, ensuring teams can promptly respond to the dynamic nature of AI workloads.

The Cost of Inaction
According to McGillicuddy, failing to adequately prepare networks for the impending wave of AI technologies can lead to disappointing returns on investment in technology. He stressed, "Networks will make or break enterprise investments in AI technology," urging businesses to prioritize network readiness as a critical success factor. As multitenancy across cloud environments, data centers, and enterprise edges becomes standard, the infrastructure must be upgraded seamlessly to accommodate these innovations.

Strategic Recommendations for IT Leaders
To successfully navigate the challenges ahead, IT leaders should consider the following strategies:

• - Invest in Upgrades: Prioritize necessary upgrades to infrastructure, focusing on both data centers and WANs to support enhanced AI performance.
• - Establish Leadership in AI Initiatives: Designate leaders for AI projects who can ensure alignment among technical teams and business objectives.
• - Adopt Security Protocols Early: Implement robust security measures early in the AI integration process to mitigate potential risks.
• - Leverage Automation: Explore automated solutions for managing network quality of service relative to AI workloads.

Conclusion
EMA’s findings underscore the importance of proactive preparation in enterprise networks as AI adoption continues to gain traction. The report encourages IT departments to address these aspects thoughtfully to harness the full potential of AI capabilities effectively. By heeding these insights, organizations can navigate the complexities of AI integration more smoothly, ultimately driving innovation and efficiency across their operations.

Upcoming Webinar
For more insights, EMA will present these findings in a detailed webinar on July 8, providing an avenue for further discussion among industry peers and thought leaders.

About Enterprise Management Associates
Established in 1996, EMA has long been recognized as a premier IT research and consulting firm offering in-depth analysis and actionable insights into technology trends and best practices. Companies aiming to make informed decisions about technology investments can rely on EMA's expertise and research, available through their website and associated platforms.