Artificial intelligence (AI) has gone from industry-specific conversations to a household phrase. In 2023, discussions around AI on LinkedIn have increased by 70%. The global artificial intelligence market was valued at 136.55 billion in 2022 and is expected to increase by a CAGR of 37.3% from 2023 to 2030. 64% of business leaders believe that AI will improve productivity and customer relationships. Specific AI branches, such as natural language processing and deep learning, have driven much of this growth, and cloud computing is a natural venue for AI.
Here’s how AI in cloud computing has transformed, popular branches of the technology, and how businesses can work to implement AI into their growth plans.
How Has AI Transformed the Cloud?
AI in cloud computing can be powerful partners for digital transformation. AI adoption has transformed the cloud in profound ways, through automation, threat detection, and more personalized experiences, to name a few.
Intelligent chatbots and virtual assistants can connect users with the services and information they need in key decision-making moments. Adaptive threat protection can grow and change with the evolving threat landscape. Self-healing infrastructure can prevent issues before they become widespread problems. Cloud environments that offer flexibility, scalability, and high performance, are the perfect home for AI tools.
The Advantages of Deploying AI in the Cloud
AI in cloud computing is reshaping the business landscape, and for good reason. The technology provides several benefits that can augment human capabilities, making work more productive, innovative, secure, and cost-efficient.
Efficiency and Productivity
Artificial intelligence can streamline workflows, help with predictive maintenance, and lead to better decision-making driven by data. By identifying manual tasks that can be easily automated and replicated, AI can streamline common operational workflows. While most machinery has predicted lifespans, AI can identify early signs of wear and tear. AI can also help businesses make decisions with better efficiency by analyzing large sets of data more efficiently and systematically than humans.
Innovation and Competitive Advantage
Businesses looking to stand out in the evolving digital landscape can become more competitive by innovating with the use of artificial intelligence. The technology can help create personalized customer experiences, improving retention and customer satisfaction.
AI can also play an important role in rapid prototyping by generating designs, selecting materials, and automating the model creation process, moving businesses from concept to product much faster. New product and service development can also be aided by AI. The technology can monitor and analyze market trends, leveraging competitive intelligence and user feedback to create and improve current products and services.
Cost Optimization
Cloud waste and overprovisioning allow costs to balloon. Monitoring and automation powered by AI tools in the cloud can:
- Cut out repetitive tasks
- Identify potential areas of wasted cloud spending
- Automate scaling in real-time and adjust resource allocation to match demand fluctuations
These tools also offer actionable recommendations for cloud cost optimization, including downsizing underutilized instances, suggesting more cost-effective alternatives, and proposing the use of reserved instances or spot instances to maximize savings.
Data Security
Security threats are constantly changing. In 2023, the largest initial attack vector for data breaches was phishing, followed by stolen or compromised credentials. These vectors swapped places since 2022, and there’s no telling what will be most vulnerable in the years to come.
Humans can’t anticipate every threat on the horizon, but AI can enhance cybersecurity measures through improved threat and fraud detection abilities. Leveraging predictive analytics to analyze past data and identify emerging patterns, AI can anticipate potential cyber threats before they materialize. This foresight allows organizations to proactively shore up their defenses, thwarting attacks before they even have a chance to land.
Additionally, AI can be used to automate tedious security tasks, empowering cybersecurity teams to shift their focus toward tackling intricate threats and crafting proactive defense strategies. This not only boosts operational efficiency but also enables organizations to stay ahead of the curve.
Data Management
Businesses now must contend with large, often untenable sets of data. Data lakes and data warehouses can contain a lot of valuable insights that have yet to be uncovered. AI data management tools can take these unwieldy data sets and find patterns and takeaways that may have sat untouched for years. For example, AI can leverage advanced data analytics for data storage optimization. Tools can efficiently identify redundant and infrequently accessed data, enabling organizations to implement robust tiered storage solutions. This approach ensures high-performance access for critical data while optimizing storage costs for rarely used files.
Moreover, fueled by a continuous learning loop of data interactions and user feedback, AI progressively refines its data management and analytical capabilities. This iterative process ensures enhanced insights and improved data-driven decision-making over time.
Popular Branches of AI
Some of the most popular, and frequently encountered, branches of AI include machine learning (ML), natural language processing (NLP), data mining, computer vision, neural networking, and fuzzy logic.
Machine Learning
Machine learning is a subset of artificial intelligence that may be more familiar in experience than in terminology. With ML, developers build algorithms and other models to learn from previous inputs and data collected to make improvements to future processes. This is done in an automated way, as opposed to setting manual rules to respond to certain insights.
The average person may encounter ML when shopping on eCommerce sites and receiving recommendations based on past purchases. Chess engines have also been built on machine learning to adapt to player activity and come up with more effective ways to counter moves. Self-driving cars can use machine learning to better adapt their driving based on traffic conditions and what is learned on the road.
Natural Language Processing
Natural language processing is a branch of artificial intelligence that has significantly increased in popularity over the past few years. The NLP market is set to exceed $63 billion by the end of 2030. With NLP, machines can interpret words the way humans can and respond logically based on previous and future input. ChatGPT, Bard, and IBM Watson are all popular NLP AI tools.
Data Mining
Humans can spend weeks, months, or even years sorting through large sets of data. Manual coding and observations can help form important insights but can slow down progress or mean that certain takeaways are missed altogether. Data mining, also known as the Knowledge Discovery Process, involves taking large sets of data and identifying patterns and correlations that may otherwise have gone unnoticed.
Computer Vision
The trained human eye can identify minute changes based on years of experience and training. Computer vision serves as a valuable supplement to this expertise. Starting from inputs gathered from digital images and videos, computer vision can identify anomalies or important details and make recommendations based on aggregated information. This technology can be used in medical diagnostics, on the factory floor, and in traffic cameras.
Neural Network or Deep Learning
Much like our own brain processes and forms connections between neurons, an AI neural network connects nodes to more effectively process information and create an output after passing through multiple layers of processing. Search engines have been using neural networks for years to deliver relevant search results to end users. These include not only the keywords that are being used to search but are also cross-referenced with past searches and other contexts, including location, to emit the most relevant results possible. Neural networks can be used in combination with natural language processing, computer vision, and predictive analytics to parse through layers of data and produce vital outputs.
Fuzzy Logic
We may think of computers as black-and-white thinkers – binary code can only include zeros and ones, after all. Fuzzy logic brings more of the gray area into play, with degrees of truth and uncertainty that may be important for more nuanced decision-making. If there is some ambiguity in the data, and time is critical, fuzzy logic can help the person interpreting the data make an appropriate choice. This is also an important technology when there are multiple variables at play.
3 Industry-Specific Applications of How AI is Used in Cloud Computing
The applications for AI in cloud computing are virtually limitless, but here are a few possible industry-specific use cases.
Supply Chain Optimization in Manufacturing
Every optimization made in a manufacturing supply chain can cut down on total production time, protect machinery from expensive breakdowns, and save money. Dynamic route planning can help businesses identify the most efficient routes for delivery. Sensors on machines can anticipate equipment failures in manufacturing before production is impeded. AI analysis can also identify market trends and cut down on over- and underproduction.
Power Consumption Monitoring and Optimization in Energy
The more that power consumption can be managed, the more we can build a future for sustainable energy. Energy companies can use smart grids to predict peak demand and identify inefficiencies in power usage. At home, personalized energy management can make small tweaks that add up to big differences in consumption and spending. AI algorithms can even predict the output for renewable energy sources, such as wind farms and solar panels, based on historical weather patterns, helping energy companies supplement properly.
Drug Discovery and Development in Healthcare
We are quickly approaching the age of personalized medicine in healthcare, where tailor-made solutions can be created for patients based on their health history and specific biological makeup. AI can analyze extensive data from one patient or many patients to find patterns in large sets of data and target drugs and interventions to specific patients based on observed trends.
How to Integrate Cloud-Based AI into Your Business
Before you integrate cloud-based AI into your business, you’ll need to evaluate whether AI adoption is viable. Key stakeholders should be on board with implementing AI, and businesses should understand the internal and external skills and resources needed to adopt AI effectively.
You should also have best practices and handbooks in place for how your organization will and won’t use AI. Creating a framework for your AI adoption can help you stay on track and avoid overextension or implementing AI tools that aren’t going to be as relevant for reaching your business goals or overcoming your unique challenges.
Need Help Embarking on Your Cloud and AI Journey?
Cloud and AI can work together to enable your business innovation, and TierPoint can serve as a valuable partner in your journey. We can leverage our expertise and deep knowledge of cloud computing and AI to identify appropriate use cases for your business and implement solutions that address your particular challenges and goals. TierPoint is also able to house AI workloads through high-density colocation in our data centers. The full potential of AI is at your fingertips, but having experts on your side can help you harness this potential. Learn more about TierPoint’s advisory services.
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