The increasing adoption of artificial intelligence (AI) across federal agencies represents one of the most transformative technology disruptions of our generation. This profound development places government leaders at a pivotal juncture, demanding strategic foresight and adaptation. From a leadership standpoint, this shift transcends mere technological change; it signifies a comprehensive reimagining of government operations, citizen interactions, and national security measures. The extent to which agencies are prepared to navigate the irrevocable impact of AI will be dictated by the proactive steps their leaders take today and in the forthcoming months, embracing AI while enhancing the capacity to manage it. Susan Shapero, Vice President of the U.S. Public Sector at Hewlett Packard Enterprise, underscores the importance of ensuring agencies possess the foundational infrastructure and flexibility vital for leveraging AI effectively to support their operations, employees, and the American public.
The Cloud’s Limits in the AI Era
Cost and Time Consuming Data Transfer
For the past two decades, cloud technology has played a critical role in democratizing and accelerating access to modernized technologies. However, the unique demands of AI and the evolving nature of data collection and processing are now compelling leaders to adopt a more nuanced approach to infrastructure investments. The transmission of enormous datasets over the internet for AI processing within remote cloud environments is becoming prohibitively expensive and excessively time-consuming. The vast amount of data generated and consumed by AI applications, combined with the necessity for real-time processing and analysis, results in escalating costs, increasing latency concerns, and impeding critical decision-making and operational efficiency.
To address these challenges, agencies must explore innovative solutions that minimize data transfer costs and enhance processing speed. This may involve adopting hybrid architectures that leverage both cloud and on-premises resources optimally. By strategically distributing workloads, agencies can achieve a balance between cost-effectiveness and performance, ensuring efficient data management and processing capabilities. Furthermore, investing in advanced data compression and optimization technologies can significantly reduce the volume of data requiring transmission, thereby mitigating associated expenses and latency issues.
Edge Computing Demands
Enterprises are collecting and analyzing more information than ever before from users and devices at the edge of their operations. This accumulation places high-performance computing demands far removed from the cloud in environments often lacking high-bandwidth connectivity. Consequently, this shift necessitates a reevaluation of data processing and management methodologies, emphasizing the need for robust edge computing solutions capable of handling the intensive demands of AI workloads. Processing data closer to its generation point not only reduces latency, enhancing the overall efficiency and responsiveness of AI applications, but also alleviates bandwidth constraints.
Implementing edge computing frameworks involves deploying localized computing resources, such as edge servers and gateways, equipped with substantial processing power and storage capacity. These frameworks enable real-time data analysis and decision-making, reducing dependence on centralized cloud infrastructures. Additionally, edge computing fosters greater data privacy and security, as sensitive information can be processed locally without needing extensive transmission over potentially vulnerable networks. By seamlessly integrating edge and cloud capabilities, agencies can build resilient, high-performance AI systems capable of operating efficiently in diverse and decentralized environments.
Data Sovereignty and Compliance
As AI workloads continue to grow, maintaining data sovereignty, confidentiality, and compliance becomes increasingly complex and dynamic. The critical need for private, on-premises AI-enabled enterprise clouds within agencies’ hybrid multi-cloud environments underscores the importance of ensuring data sovereignty. This entails stringent adherence to regulatory requirements and the safeguarding of sensitive information by maintaining data within national boundaries. Investing in on-premises solutions empowers agencies to exercise greater control over their data, mitigating risks associated with cross-border data transfer and ensuring compliance with evolving legal and regulatory standards.
Ensuring data sovereignty requires meticulous planning and implementation of secure, scalable on-premises AI-enabled infrastructure. This includes robust data encryption mechanisms, access controls, and audit trails to protect sensitive information from unauthorized access or breaches. Agencies must also stay abreast of emerging regulations and standards to ensure compliance with domestic and international data protection laws. Collaborating with industry experts, such as Hewlett Packard Enterprise (HPE), enables agencies to leverage cutting-edge technologies and best practices in data governance, positioning them to navigate the complexities of data sovereignty confidently and effectively in the AI era.
Reassessing Hardware for AI
Purpose-built AI Accelerators
Planning for an AI-driven future necessitates a fundamental rethink of data processing hardware. Agencies must transition beyond general-purpose processors and embrace specialized hardware solutions designed to meet AI’s unique demands. While AI workloads increasingly demand high-performance computing capabilities, deploying fleets of graphic processors is not always necessary. CPUs with built-in AI accelerators, such as Intel’s Xeon processors with P-Cores, or specially designed inference accelerators like Intel’s Gaudi3, are game-changers. These chips, engineered for inference workloads, deliver exceptional performance and energy efficiency, particularly at the network edge, where minimizing resource consumption is crucial.
This shift towards purpose-built AI accelerators offers significant advantages, including reduced energy consumption and lower operational costs. Agencies that prioritize floating point operations (FLOPS) per watt over FLOPS per second in evaluating hardware procurements can achieve substantial cost savings and smaller environmental footprints. Optimizing infrastructure for overall performance and sustainability ensures that agencies can meet the rigorous demands of AI applications while adhering to environmental goals. By investing in specialized hardware, agencies are better positioned to harness AI’s full potential, driving innovation and operational excellence across government services.
Specialized Networking Infrastructure
Handling the substantial data flows generated by AI applications requires agencies to invest in specialized networking infrastructure. These investments ensure seamless connectivity between users, devices, and systems to the cloud and on-premises data centers in a unified and secure manner. High-speed, low-latency networks are essential for efficient data transfer and processing, facilitating real-time analytics and decision-making. By deploying advanced networking technologies, agencies can optimize the performance and reliability of their AI applications, meeting the demands of modern government operations and enhancing overall service delivery.
Specialized networking infrastructure involves implementing high-capacity fiber optic networks, advanced switching and routing solutions, and robust cybersecurity measures to safeguard data integrity and confidentiality. Additionally, leveraging software-defined networking (SDN) frameworks enables greater flexibility and scalability, allowing agencies to dynamically allocate resources based on workload demands. This holistic approach to networking infrastructure ensures that agencies can effectively manage the immense data volumes associated with AI applications, supporting seamless integration and interoperability across diverse systems and platforms while maintaining stringent security and compliance standards.
Building an AI-Ready Enterprise
Leveraging Expertise from Industry Leaders
Agencies can gain a significant advantage by examining the accomplishments of other agencies and organizations with the assistance of industry leaders like Hewlett Packard Enterprise (HPE). With decades of experience in high-performance computing, HPE is uniquely positioned to support government agencies in planning for AI’s intensive computing demands. HPE systems power seven of the top ten fastest supercomputers globally, as listed on the TOP500 list, constituting 49% of the operational capability of the world’s top 500 supercomputers. Furthermore, HPE’s and Intel’s recent delivery of the Aurora exascale supercomputer for the U.S. Department of Energy’s Argonne National Laboratory exemplifies HPE’s expertise in designing, manufacturing, installing, and managing AI-optimized infrastructure to meet virtually any requirement.
Collaborating with industry experts like HPE enables agencies to leverage best practices, cutting-edge technologies, and proven methodologies in developing AI-ready systems. HPE’s extensive portfolio of AI solutions encompasses hardware, software, and services tailored to the unique needs of government agencies. By tapping into HPE’s experience and resources, agencies can accelerate their AI initiatives, optimize infrastructure for high-performance computing, and ensure seamless integration of AI capabilities into existing workflows. This collaboration fosters innovation, operational efficiency, and strategic growth, positioning agencies to effectively navigate and lead in the AI era.
Energy Efficiency and Sustainability
HPE also leads in energy efficiency, with four of the top ten most energy-efficient supercomputers operating on HPE hardware. As agencies seek to reduce their environmental footprint while addressing growing AI workloads, this focus on sustainability is crucial. By investing in energy-efficient hardware and infrastructure, agencies can achieve cost savings and contribute to broader environmental goals. HPE’s commitment to sustainability extends beyond hardware, encompassing eco-friendly data center designs and renewable energy initiatives, further solidifying its position as a preferred partner for government agencies pursuing green initiatives.
Incorporating energy-efficient technologies into AI infrastructure involves adopting advanced cooling systems, energy management software, and resource optimization techniques to minimize power consumption and carbon emissions. Agencies can also explore renewable energy sources, such as solar and wind, to power their data centers, reducing reliance on fossil fuels and enhancing energy security. HPE’s expertise in energy-efficient computing empowers agencies to balance AI’s computational demands with sustainability objectives, driving responsible innovation and environmental stewardship. This holistic approach ensures that agencies can meet their operational goals while contributing positively to global sustainability efforts.
Conclusion
The AI revolution has arrived at federal agencies, presenting unprecedented opportunities and challenges. Agency leaders must ensure their IT investment plans reflect the latest thinking on what constitutes an AI-ready environment and what is required to achieve it. Embracing AI is essential not only for advancing technology but also for realizing fundamental improvements in government operations, citizen interaction, and national security safeguarding.
Final Thoughts
Over the past twenty years, cloud technology has been essential in making modern technologies widely accessible. Yet, the specific requirements of AI and the changing landscape of data collection and processing are forcing leaders to rethink infrastructure investments. Sending massive datasets over the internet for AI processing in distant cloud environments is becoming incredibly costly and time-consuming. AI applications generate and consume enormous amounts of data, necessitating real-time processing and analysis, which drives up costs, increases latency, and hinders essential decision-making and operational efficiency.
To tackle these issues, agencies need to explore innovative solutions that reduce data transfer costs and improve processing speed. This could involve using hybrid architectures that combine cloud and on-premises resources effectively. By strategically managing workloads, agencies can balance cost and performance, ensuring efficient data management and processing. Additionally, investing in advanced data compression and optimization technologies can decrease the volume of data needing transmission, thus reducing costs and latency concerns significantly.
