Artificial intelligence and cloud computing have merged to improve the lives of millions. Digital assistants like Siri, Google Home, and Amazon’s Alexa blend AI and cloud computing in our lives every day. With a quick verbal cue, users can make a purchase, adjust a smart home thermostat, or hear a song played over a connected speaker. However, fewer companies have fully appreciated the scope of opportunity to blend these technologies to enhance Enterprise solutions and Cloud computing.
Cloud computing offers businesses greater flexibility, agility, and cost savings by hosting data and applications in the cloud. But Cloud also gives rise new challenges to manage the complexity of leveraging massive infrastructure and technology effectively and efficiently.
Managing complexity: AI provides unique capabilities in helping companies to manage their cloud infrastructure, costs, and complexities by looking at data to identify patterns and insight in information. This allows them to deliver better customer experiences, enhance automation, and optimize workflows.
Easy access and innovation: Our customers can leverage AI capabilities and techniques built into Cloud services, without the need to deal with the complexities of managing huge data sets, owning massive computing power, or requiring top technical talent.
There are several possible implementations of AI and Cloud computing which an increasing number of customers and organizations are likely to start considering or applying very soon:
- The self-managing Cloud: AI is embedded into IT infrastructure and the Cloud to help streamline workloads and automate repetitive tasks. In some cases, AI can assist in predicting behavior in infrastructure and can help anticipate or alert of unusual, unexpected behaviors. Furthermore, private and public Cloud instances can rely on AI tools to monitor, manage, and propose cost optimization methods, and identify self-healing incidents in the application and underlying infrastructure. Some industry examples include Turbonomic, AIOps Solutions, and ActiveBatch.
- Automation and insight: AI can be used to automate core workflows and then, over time, analytical capabilities can recommend more streamlined and effective processes and tasks. Routine processes can be managed with automation to capture the efficiencies of cloud computing, allowing organizations to focus on high-value strategic activities. Some examples for consideration are testing automation and AI tools (i.e. TestCraft, AppliTools, or Testim.), Cloud Robotic Process Automation (RPA), or the combination of Cloud and AI Capabilities in Azure, AWS, or Google.
- Autonomous databases: There is a wide range of cloud solutions in the market where autonomous systems, online or offline, can be enabled to autonomously manage themselves. An example is an autonomous database which uses machine learning AI techniques to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by database administrators. Unlike a conventional database, an autonomous database can perform all these tasks without human intervention, reducing operational costs up to 90%.
- Monitoring insight: Traditional performance monitoring IT needs to decide which parameters to monitor across all layers, from infrastructure to Application and Business. When the number of parameters rises to the hundreds or even thousands, configuration becomes unmanageable and gaps in coverage grow. Monitoring tools with Machine Learning and AI capabilities can provide deep and dynamic insight about the IT environment including the “unknown unknowns” like unexpected bottlenecks, and detect changes in behaviors.
- Improving data management: Cloud computing solutions are already using AI tools to help with specific aspects of the data process. In banking, for example, even the smallest financial organization may need to monitor thousands of transactions per day. AI tools can help streamline the way data is ingested, updated, and managed, so financial institutions can more easily offer accurate real-time data to clients.
- AI–SaaS integration: AI tools are also being rolled out as part of larger Software-as-a-Service (SaaS) platforms to deliver greater value. For example, Salesforce introduced ‘Einstein’ to help turn data into actionable insight that businesses can use to improve their sales strategies and engage with customers. IA tools can help a business look for patterns in customer interactions and “next step” recommendations based on the identified buying signals.
- Dynamic cloud services: AI in the Cloud can provide sophisticated analysis based on modeling and neural networks to give businesses much better command of their data, with important real-time implications. For example, an AI-powered pricing model can ensure that pricing will always be optimized.
AI and cloud computing are transforming business at every level and the potential is promising. While there are some great examples of this in the market, the combination of these technologies will only continue to grow in the years to come. Now is the moment to explore AI and cloud computing to deliver better experiences, work more efficiently, and capture the maximum value possible from data.