Artificial intelligence (AI) has made significant inroads into many diverse areas of modern life. Virtually every industry has been affected to some degree by the introduction of AI tools and applications. It is nearly impossible not to interact with some AI-powered app or system daily.
AI and the related field of machine learning (ML) are becoming increasingly integrated into various aspects of cloud computing. Companies must be prepared to utilize these solutions to thrive in today’s ultra-competitive business landscape. This post examines some of the short-term and long-term impacts of AI on cloud computing.
More Intelligent Cloud Services
Major cloud computing vendors are currently utilizing AI to automate and optimize their operations. The goals are to minimize human intervention, reduce errors, lower costs, and increase operational efficiency. AI is impacting the following specific areas of the cloud environment.
- Predictive scaling – AI agents can forecast demand based on historical usage and dynamically predict resource requirements.
- Automated patching – Intelligent AI applications can monitor the environment for potential vulnerabilities and proactively apply patches and updates.
- Intelligent load balancing – Vendors will utilize AI solutions for smart load balancing to optimize resource utilization and minimize latency.
AI-as-a-Service (AIaaS)
Cloud service providers (CSPs) will make AI capabilities such as machine learning, computer vision, and natural language processing available to their customers as a service. AIaaS levels the playing field by enabling companies of any size to leverage the technology to address their business objectives and requirements. Startups and SMBs without the resources to build AI infrastructure in-house can compete with bigger companies with AIaaS solutions.
AIaaS can provide custom ML training platforms to meet specific client requirements. Vendors may offer AI tools designed for non-technical users that streamline development by reducing the need for coding skills. Trained AI models, such as those from Microsoft Azure’s OpenAI, enable developers to incorporate AI functionality into their applications easily.
High-performance Infrastructure Demands
The demand for high-performance cloud infrastructure is dramatically increasing as more companies adopt the technology to address competitiveness and operational business requirements. Supporting and training AI models require extensive computing and storage resources. Specialized AI chips, such as the NVIDIA H100, and distributed computing are necessary to facilitate model training. Providers need advanced graphics processing units (GPUs) and tensor processing units (TPUs) to support deep learning applications.
Cloud vendors will continue to invest heavily in building cloud infrastructure to support AI and ML. Research predicts that the market value of AI infrastructure will increase from $47.23 billion in 2024 to $499.33 billion by 2034, representing growth at a CAGR of 26.60%. Companies may face challenges in obtaining reliable energy sources to power this level of infrastructure growth.
Enhanced Cloud Security
AI will become an indispensable component of cloud security, helping organizations manage the growing risk posed by threat actors. Unfortunately, AI as it relates to cybersecurity is a double-edged sword. Cyber criminals are leveraging the power of AI to develop sophisticated methods of attacking cloud environments. Organizations must utilize the advanced capabilities of AI to create effective cybersecurity solutions.
The specific ways companies can leverage AI to enhance cloud security include:
- Automated incident response for real-time threat identification and containment;
- Advanced anomaly detection to improve intrusion prevention;
- Intelligent threat detection with behavioral analytics and pattern recognition;
- Real-time fraud detection systems;
- Adaptive authentication with AI supporting MFA and zero trust security;
- Automated threat hunting and response;
- Behavioral biometrics for identity verification.
AIOps for Cloud Environments
AI tools for cloud management and DevOps help companies optimize their cloud infrastructure and maintain operational efficiency. AIOps platforms utilize data analytics and machine learning to manage and automate complex IT environments in various ways, including:
- Self-healing infrastructure capable of recovering from failures without human intervention;
- Anomaly detection in system logs and performance metrics;
- Automated root-cause analysis to resolve problems quickly and prevent their recurrence;
- Predictive analytics for resource optimization.
Intelligent Data Management
Cloud providers can offer customers cost savings and improved data governance with AI assistance. Examples of implementations include:
- Streamlining data search and classification with AI-powered indexing;
- Automated data lifecycle to delete obsolete information and recover storage capacity;
- Smart data movement between storage tiers to minimize costs while maintaining accessibility.
Efficient Cloud Networking
CSPs can manage their complex networks more efficiently with the aid of AI tools. Intelligent traffic routing can optimize data flow by choosing the most efficient paths to address outages or network congestion. Vendors can use AI analytics for predictive bandwidth allocation to handle fluctuating customer demands. The latency reduction resulting from these activities supports more efficient data movement and increased customer satisfaction.
Intelligent Automation
AI enables advanced automation solutions with decision-making capabilities. Companies can deploy virtual agents and chatbots to provide customer assistance through their helpdesks. Support team productivity can be improved with automated ticket triage and resolution. Intelligent workflows can streamline tasks like scheduling backups, provisioning new systems, and patching security vulnerabilities.
AI and Sustainable Cloud Computing
The data centers being constructed to support AI can put a tremendous strain on a region’s natural resources and energy generation capacity. The industry as a whole must manage resources responsibly or risk causing negative impacts to society. CSPs can use AI technology to improve their sustainability posture in several ways.
- AI-powered predictive cooling systems can reduce the amount of water necessary to cool cloud data centers.
- Providers can utilize AI workload scheduling solutions to load balance and minimize their carbon footprint.
- AI-based dynamic power management can identify patterns in energy usage and interact with smart grids to utilize renewable sources whenever possible.
VAST’s Position on AI Innovation in the Cloud
VAST’s longstanding partnerships with the major cloud players like AWS, Google, and Microsoft put us in an excellent position to help your company use AI to support your business objectives and initiatives. We can provide the experience and insight to deliver cloud solutions that leverage the power of AI and ML to address your unique business needs.
Get in touch and partner with us to leverage the IT innovations driven by artificial intelligence.
