The energy crisis has made costs for both consumers and businesses critical. Amid the economic downturn, 81% of IT leaders say their C-suite has reduced or frozen cloud spend.
Every business today is faced with the need to modernize. Operational resilience for energy and utilities – especially across various business functions, technology and services – has never been more important than it is today. To compete or survive, they must embrace hyper-digitized business opportunities that enable flexible work for critical operations. That means leveraging advanced capabilities of IoT, advanced analytics and orchestration platforms.
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Artificial intelligence, in particular, will prove to be one of the most transformative technologies to be used in conjunction with the cloud. Companies that can successfully leverage AI can gain an edge not only in their ability to innovate and stay competitive, but also in saving energy, going green and lowering costs amid economic uncertainty.
AI in an energy crisis
While some think AI is overhyped, the technology is built into almost every product and service we use. While the smartphone and voice assistants are prime examples, AI is having a dramatic effect across industries and product types, accelerating the discovery of new chemical compounds that yield better materials, fuels, pesticides and other products with better environmental properties.
AI can help monitor and control data center computing resources, including server usage and power consumption. Production floor equipment and processes can also be monitored and controlled by AI to optimize energy consumption and minimize costs.
AI is used in a similar way to monitor and control cities, buildings and traffic routes. AI has given us more energy-efficient buildings, reduced fuel consumption and planned safer routes for shipping. In the coming years, AI could help transform nuclear fusion into a reliably cheap and abundant carbon-neutral energy source, offering another way to combat climate change.
Electricity grids can also benefit from AI. To operate a network, you need to balance supply and demand, and software helps large network operators monitor and manage the tax increases between areas with different energy needs, such as highly industrialized urban areas versus sparsely populated rural areas.
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Harnessing the power of AI, it adds the additive layer needed to easily adapt the power grid to respond appropriately to prevent outages. Before a heat wave or natural disaster, AI is already being used to anticipate electricity demand and orchestrate the storage capacity of batteries in homes to avoid power outages.
To intelligently use AI and reduce compute resources when not needed, you need automation through cloud-native platforms like Kubernetes, which already widely streamlines the deployment and management of containerized cloud-native applications to reduce operating costs. In the context of a grid or data center, while Kubernetes does not inherently address the growing demand for data or power, it can help optimize resources.
Kubernetes is an ideal match for AI
In the worst-case scenario, when the UK runs out of energy for grids or data centers, Kubernetes automatically grows or shrinks computing power in the right place at the right time based on what’s needed at any given time. It’s much better than a human putting workloads on servers, which creates waste. When you combine that with AI, the potential for optimizing power and cost is huge.
AI/ML workloads are taxing to run, and Kubernetes is perfect for this because it can scale to meet the resource needs of AI/ML training and production workloads, enabling continuous model development. It also lets you share expensive and limited resources, such as graphics processing units, between developers to speed up development and reduce costs.
Likewise, it gives enterprises the flexibility to deploy AI/ML activities across infrastructure in different environments, be it public clouds, private clouds or on-premises. This allows deployments to be changed or migrated at no additional cost. Whatever components a company has running – microservices, data services, AI/ML pipelines – Kubernetes lets you run it from one platform.
The fact that Kubernetes is an open source, cloud-native platform makes it easy to adopt cloud-native best practices and take advantage of continuous open-source innovation. Many modern AI/ML technologies are also open source and come with native Kubernetes integration.
Bridging the skills gap
The downside of Kubernetes is that the energy sector, like any other sector, faces a Kubernetes skills gap. In a recent survey, 56% of energy recruiters described an aging workforce and insufficient training as their biggest challenges.
Because Kubernetes is complex and unlike traditional IT environments, most organizations lack the DevOps skills needed for Kubernetes management. Likewise, most AI projects fail due to complexity and skill issues.
ESG Research found that: 67% of respondents are looking for IT generalists rather than IT specialists, causing them to worry about the future of application development and implementation. To close the skills gap, energy and utility companies can devote time and resources to upskilling DevOps employees through dedicated expert training. Training combined with platform automation and simplified user interfaces can help DevOps teams master Kubernetes administration.
Spend now to bloom later
Cost savings are now inevitable for many companies, including energy suppliers. But even in times of recession, CIOs must balance technology investment spending with improved business outcomes, competitive demands and profitability that come from using cloud native, Kubernetes, AI and edge technologies.
The latest from Gartner prediction claims global IT spending will grow just 3% to $4.5 trillion by 2022 as IT leaders become more aware of investments. For long-term efficiency cost savings in IT infrastructure, they would do well to invest in cloud-native platforms, which Gartner has included in its annual Top Strategic Technology Trends report for 2022.
As vice president of Gartner Milind Govekar said so: “There is no business strategy without a cloud strategy.”
Cutting back on cloud-native IT modernization initiatives can save money in the short term, but can seriously damage long-term opportunities for innovation, growth and profitability.
Tobi Knaup is the CEO of D2iQ.