Future Cloud Trends Of AI And Analytics

Future Cloud Trends Of AI And Analytics

Future Cloud Trends Of AI And Analytics

Artificial Intelligence elements are now working with cloud computing and supporting businesses to handle their data, appear for patterns, get insights into information, optimize workflows, and provide customer experiences. AI basically helps to automate repetitive activities and streamline workloads within the IT infrastructure, successfully inclining productiveness.

In the coming times, all the cloud services that depend on AI tools will supervise, manage and can even do error rectifications in the system without any human intervention. Talking about analytics in the cloud services, they help in real-time analysis of data for both remote private and public computing resources.

Cloud computing analytics can smoothen the business intelligence process of collecting, combining, examining, and giving insights to strengthen business decision making. Therefore, businesses are now incorporating Artificial Intelligence (AI) and Machine Learning (ML) in the broader analytics strategy to accomplish their goals.

Let us look at the expected trends of AI and Analytics in the year 2021.


Any retailer who always want to maximize their income, deliver personalized experiences to their own clients, expand stocks, make better buying decisions, and effectively handle the pricing schemes should go ahead and combine AIoT as their main function.

IoT in retail helps in comprehending and capturing user’s data. Camera systems have different functions like image processing, detectors, and computer vision technologies to accurately ascertain customer behaviour as well as to conduct store operations.


By 2023, it is anticipated that more than 33% of big companies would have analysts who will be practising decision intelligence along with decision modelling. As per Gartner, decision intelligence is referred to as a practical domain that consists of a massive range of decision-making techniques.

It surrounds software such as complex adaptive systems. That consists of a framework that brings together advanced tactics such as machine learning and AI with traditional tactics such as rule-based approaches. This allows non-technical users to reform decision logic without the involvement of programmers.


There are plenty of reasons enlisted below that will let the companies transfer their data analytics project to the cloud:

  • Outsourcing the functions of computer systems to save cost and get access to specialized knowledge.
  • Getting easy access to support and assist operations anyway and anytime.
  • High output to reduce the time of understanding.


Natural Language Processing has a tendency to grow everywhere. This technology is sufficient enough to be used for all the sectors ranging from human to machine conversation.

  • Bots supported by NLP have the abilities of knowledge workers.
  • Businesses can now induce more dark data with the help of NLP.
  • The NLP-oriented multimodal technology and computer vision allow forgery identification and video KYC.
  • Cognitive bots are used as personal de-facto assistants.
  • Escalated access to unorganized data.
  • NLP helps in detecting fraud and maintaining security.


‘X’ stands for any number of words that go before analytics. According to Gartner, AI is used for audio, text, video, vibration, emotion, and other content analytics that is going to boost major transformations and innovations in 75% of the Fortune 500 companies by the year 2025. ‘X’ is mainly for audio and video analytics.

Since this type of data has not been leveraged by many organizations yet, it may open new opportunities. However, the effort for the same is growing. For instance, audio & video analytics is used for traffic and weather management and video & image analytics for supply chain management.


Popular firms have started implementing quantum supremacy to compute Qubits for use in supercomputers. This is because quantum computer systems are faster in sorting out problems than conventional computers with the help of quantum bits. They also support the forecast of varied patterns as well as data analysis.

Quantum computers can aid a range of companies in recognizing severe hurdles and forecasting workable solutions. The future machines will be capable enough to manage a massive range of software in sectors like banking, healthcare, and science.


Companies using machines learning, active metadata, and data fabrics to dynamically optimize, connect, and automate data handling procedures are set to reduce data of delivery by approximately 30% till the year 2023.

AI tactics are helpful in the auto-discovery of metadata, the next best action, or auto-supervision of governance controls, among several others. This Gartner-enabled concept is calling data fabric. Gartner refers to data fabric as something that uses constant analytics over discoverable, existing, and inferences metadata assets to help the deployment, design, and application of reusable and integrated data objects, regardless of architectural approach and deployment platform.


AI-powered chatbots or conversational AI enhances the accessibility, reach, and customization of the consumer experience. As per Forrester, conversational AI software lead to enhanced automation of customer service.

Machine learning and NLP supports the functioning of AI-driven chatbots. Because it offers more realistic as well as human-level communication by deeply comprehending what the human desires for and requests.


In 2021 according to Gartner, it is expected that around 35% of the big companies would be either buyers or sellers of data through formal online data marketplaces 2022. As per Sallam, the weightage was 25% in 2020 and is anticipated to rise only with accelerating data science, cloud, AI, and machine learning.


AI is set to influence businesses of all sizes & shapes of industries. In 2021, AI and Analytics are set to get deeper insights with sophisticated automation. Therefore, enterprises are planning to embrace Artificial Intelligence and Machine Learning as important elements of broader analytics strategy to accomplish their goals.

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Garima Jhawar
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