The artificial intelligence (AI) market is growing quickly and on track to reach a market value of $267 billion by 2027. This is not surprising when you consider how AI has woven itself into everyday business and personal activities. AI regulates room temperatures, autocorrects writing and navigates travel routes. Businesses use AI to conduct comprehensive data analyses that outline more innovative products, strengthen customer experiences and introduce operations-optimizing automations. Enabling these opportunities requires a tremendous amount of data, and as businesses introduce more AI-powered solutions, they will need to ensure their IT infrastructures can provide the network and cloud connectivity, space, power and cooling to support these data-intense workloads.
AI is a broad term that integrates several advanced technologies including machine learning (ML), deep learning (DL) and natural language processing (NLP). According to Britannica, AI is “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.” By applying sophisticated algorithms to increasing amounts of data, AI creates training models that help computers learn from prior experiences and build upon their existing knowledge.
Machine Learning. ML teaches computers to adapt to situations by integrating existing knowledge with new data and complex algorithms to learn, reason, provide insight and enable automated responses with increasing accuracy.
Deep Learning. Deep learning is a sophisticated subset of ML in which the computer identifies patterns and classifies information without human input. Using a network of information, DL creates independent, automated learning models that allow machines to predict outcomes, translate speech and perform other human-like activities.
Natural Language Processing. NLP is a linguistic tool that allows computers to decipher and understand human language. NLP applications include speech-to-text translations and voice-activated or hands-free calling.
AI is poised to take an even more central role in providing better customer experiences (CX), cost savings and a series of competitive advantages.
Improve Convenience. Internet of Things (IoT) devices can offer significant conveniences and costs savings. Virtual assistant technologies like Alexa and Amazon Echo handle simple tasks such as turning on music, setting timers and answering questions. In fact, in 2021, Alexa had nearly 80,000 U.S.-based skills and Google Assistant boasted an accuracy record of up to 98%. In business, chat bots are used to answer questions, direct customers to the right department and more.
Strengthen Decision Making. Businesses use AI’s predictive capabilities to make more informed decisions. By analyzing large quantities of data, AI can identify consumer preferences to create targeted advertisements and products that are better aligned with customer needs. AI can also help healthcare providers diagnose disease and risk factors with greater accuracy.
Speed Decisions and Improve Outcomes. AI-enabled machines possess cognitive qualities that are helpful in automating 9-1-1 calls, detecting and responding to malware or phishing attempts, and developing therapeutics. Credit card companies also use AI to monitor spending habits to more quickly recognize fraudulent purchases.
Enhance the Bottom Line. The speed and accuracy of AI-enabled solutions can also improve a business’ financials. By quickly analyzing large quantities of data, AI can guide businesses to make decisions that minimize expenses, increase revenue and build better customer experiences. One report claims that AI could reduce pharmaceutical R&D costs by nearly $54 billion per year.
The applications for AI are seemingly endless. Yet, they all have the same underlying requirement: The availability and timeliness of data.
The massive quantity of data it takes to develop human-like intelligence cannot be underscored enough. The continuous learning cycles require constant data ingestion to foster ongoing learning and deliver more accurate decisions, predictions and solutions.
The ability to support AI workloads is becoming increasingly essential as AI takes a more prominent role in business and daily life. While a disruption in an AI-enabled application such as a delay in regulating home heating or a lag in a navigational upload would be an inconvenience, an issue with a growing number of AI-applications – particularly those that deal with health and safety – could have more detrimental effects. These processes necessitate network reliability and scalability to meet immediate (bursting) as well as future data processing demands. AI workloads also require more intense power and cooling capabilities than other workloads.
As AI continues to take on more critical roles, data centers will become more significant. However, not every data center can support AI workloads. Enterprise data centers generally lack the necessary infrastructure and flexibility. However, hyper-connected data centers have the requisite network communications equipment and cloud access to support AI applications.
AI-based applications require a lot of data center space to store gargantuan amounts of data and run training models allowing machines to build their intelligence and improve results. Colocation data centers offer the scalable space to match rising capacity expectations. Colocation providers can also custom-design customers’ deployments to meet specific needs and ensure the company can continue to grow within the space. Some providers offer high-density racking to provide more compute power in a smaller space.
AI workloads have more intense compute demands than other applications. While the average rack density is 8.4 kilowatt (kW), AI workloads require significantly more power; it’s common for AI applications to use more than 30 kW per rack. Third-party data centers are designed to support the most intense power demands, including hyperscale deployments. These experienced providers can also build customized deployments to meet specific power requirements. Facilities that offer high-density racking also offer the specialized power and cooling capabilities these configurations require.
Colocation data center providers are constantly evaluating their capacity capabilities and employing more efficient and innovative ways to deliver power and support high-density workloads and other future expectations. That includes building and adapting facilities for sustainability and carbon footprint reduction.
As more power is used to support AI workloads, additional cooling is required to offset the heat generated by the servers. Without sufficient cooling, facilities risk servers overheating, which can result in equipment failures and downtime. Colocation data centers offer more modern and energy-efficient cooling practices and techniques than most enterprise facilities. Cooling solutions can include air-side economization, water-side economizers, hot/cold aisle containment, evaporative condensing units, variable speed CRAHs with humidification control and more.
Networking Demands and Peering
AI workloads demand a reliable and highly scalable network. Enterprise data centers lack access to the variety and depth of network partners a third-party data center can provide. A data center’s portfolio of network “peers” allows organizations to align themselves with multiple carriers to ensure availability if one carrier fails.
The ability to scale bandwidth for low latency processing speeds and data delivery as needed also ensures information is not delayed. This is particularly essential with real-time and near-time AI applications such as self-driving cars and life-sustaining health devices.
Direct Cloud Access
Given the amount of data used in AI learning models, access to the cloud for storage and processing is a must. Third-party data centers can supplement colocation services with direct cloud access to allow enterprises to quickly expand their storage capacity and access cloud services as needed. Providers with direct onramps to major cloud providers can deliver this added capacity at a lower cost by minimizing egress charges.
Diverse Geographic Locations
Colocation providers with a nationwide portfolio of data centers can also help organizations place data and compute in facilities near end users (at the edge), allowing for the extremely low latency performance enabling today’s AI as well as emerging 5G applications.
Long-term evolution (LTE) and 4G networks laid the foundation for the growing role of 5G and AI in next-generation mobile networks. With 5G, mobile users will have better experiences with the services they use, and services providers will more quickly and accurately adapt to user preferences, whether that’s smarter product suggestions or tools to be more productive.
A Commitment to Uptime
As with everything digital, the most basic need of AI workloads is uptime. To power business operations, analyses and decision making, the availability of the IT environment is critical. Third-party data centers employ a series of redundant systems, power feeds and connectivity options to ensure operations remain online if one system or network path fails. They also back up this reliability with 100% uptime SLAs. In addition to these redundancies, third-party data centers purposely choose locations outside of flood zones and other areas affected by extreme weather.
AI in the Data Center
While businesses rely on AI to improve any number of functions, AI has also found its way into data center operations. By automating tasks such as monitoring and maintaining equipment; controlling lighting, power and cooling systems; and monitoring security patterns, AI can improve data center efficiency, performance, safety and more.
Providing the infrastructure to support intense AI workloads is a critical first step in a modern IT solution. However, not all third-party data centers offer the same level of capabilities, innovation, expertise and service.
As a bare minimum, an AI-ready data center should offer scalable space, power, cooling and connectivity. These offerings should be complemented by a strong customer focus and commitment to continual innovation to address evolving requirements. Additionally, third-party data centers should offer the expertise to guide businesses though the rapidly shifting AI landscape.
Finding the right provider with the indispensable combination of infrastructure, skill and a customer-first approach can ease daily operations, ensure a more positive data center experience and serve as a key differentiator as businesses continue to adapt to and integrate advanced technologies.
Chief Revenue Officer
Steve is Chief Revenue Officer, accountable for driving integration and alignment of revenue-related functions within the customer revenue journey.Read more from this author