Artificial Intelligence (AI) is reshaping the telecommunication practices with next-gen technologies such as machine learning and data analytics. AI in telecommunications can streamline business functions from network management to wireless...
Artificial Intelligence (AI) is reshaping the telecommunication practices with next-gen technologies such as machine learning and data analytics. AI in telecommunications can streamline business functions from network management to wireless operations to provide seamless customer services across devices. Market forecaster, Technavio suggests that artificial intelligence services will penetrate the global telecom IoT market to post an extensive CAGR of over 42% by 2020. In this article, we take a close look at how AI is heralding a digital reinvention for the telecommunication industry.
AI in Telecommunication | Rewriting the Success Formula
However, the enormous network of intertwined devices used by telecom companies can become their greatest asset with AI-enabled Predictive Analytics. Telecom’s vast troves of data include customer profiles and products, offers, usage and rebate history, network experience, weblogs, pricing, and promotion. AI-powered analytics and machine learning algorithms enable operators to predict customer churn and use insights to develop constructive solutions.
McKinsey’s insights reveal that telecom companies that implement a data-driven and analytics-based approach to base management can reduce churn by almost 15%.
In addition, artificial intelligence can drive telecom growth with the following analytics-enabled solutions-
a) Predicting Equipment Health
AI’s data-driven insights can be used to monitor the condition of network equipment, study patterns to anticipate failure, future outages. Predictive analytics can enable operators to fix technical glitches in communication hardware, power lines, and servers efficiently.
b) Improving Real-time Customer Experience
AI can use customer data to identify real-time customer satisfaction in the form of the Net Promoter Score (NPS) or Customer Experience Index (CEI). Predictive analytics can assist telecom companies to personalize services to targeted customers by analyzing their viewing habits and patterns.
The rising demand for telecom services, network capacity, and configuration changes can be well addressed with artificial intelligence. AI in telecommunications shows great potential to optimize end-to-end network visibility and performance.
The telecommunication leader, Nokia, states that AI has already penetrated Self Organizing Networks (SON) applications to build more adaptive and scalable mobile networks.
Here’s how AI-powered automation is strengthening network bandwidth and security-
a) Real-time network visualization for seamless network orchestration.
b) Quick and accurate identification of performance issues.
c) Efficient design and development of high-scale networks such as complex non-linear modeling of 5G networks.
Reinventing Business Opportunities with AI in Telecommunications with Oodles
The market size and growth rate of the telecommunication industry is expanding at a massive scale. At Oodles, we are assisting the telecommunication businesses to capitalize on their expansive profit margin to harness next-gen technologies. With emerging technologies such as IoT, artificial intelligence, and data analytics, we are empowering telecom companies with scalable solutions such as-
a) Predictive Analytics for extracting key business insights from large volumes of customer data, profiles, usage records, etc.
b) Predictive Maintenance for IoT devices, network equipment, and communication hardware.
c) Computer vision techniques to generate in-depth video analytics for real-time improvement in customer experience and engagement.
d) AI-powered chatbot development services for interactive, useful, and efficient conversational applications to handle customer queries and complaints.
To know more about our artificial intelligence services, reach out to our AI development team here.
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Artificial Intelligence (AI) Tutorial - Getting started with Artificial Intelligence. In this Artificial Intelligence tutorial you will learn end to end about AI and it's vast domain. So this AI tutorial for beginners is an exhaustive tutorial for you to get started with AI.
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Artificial Intelligence (AI) and Machine Learning( ML) is a very common and important aspect of today’s life. [AI development services](https://artificialintelligence.oodles.io "AI development services") have opened up new business opportunities...
Artificial Intelligence (AI) and Machine Learning( ML) is a very common and important aspect of today’s life. AI development services have opened up new business opportunities for the advancement in every possible human activity, be it medicine or gaming.
In such an atmosphere, it is important to discuss the importance and methods of AI and Machine Learning Testing.
How AI can optimize Testing?
1: Accelerating Timelines
As a manual tester, we have to go through thousands of lines of codes to test any functionality. Artificial Intelligence is a much better approach in this case. AI can scan codes, sort through log files and detect errors in a very short span of time. Additionally, AI is very helpful as it does not get exhausted or make human errors, so it yields more accurate results. By implementing AI into repetitive tests, Testers can focus on testing new features or pay particular emphasis to important parts of the software.
2: Better Automation
A very important part of a QA’s job is to ensure that the new code does not affect the functioning of the existing code. Since more features are developed, the amount of code to be tested expands which can give burden to the Testers.
Artificial Intelligence bots are able to evolve with change in the code. New functions can be adapted and easily identifiable by AI bots. When any modification in code has been identified by AI bots, they can be programmed in a way that it can decide if it is a new feature or there is any defect emerging from changes to the code.
3: More clarity on client requirements
The most important part of Software Testing to understand client expectations. By using Artificial Intelligence, it is possible to examine similar websites and apps to determine what contributes to success among the target audience. AI can help study a large number of competing products to identify key selling points so that both developers and tester know what users want out of a particular kind of software.
Testers can use AI to detect common flaws and errors in code that might affect the flawless functioning of software. So, by having a proper understanding of what the client wants, QAs can create test cases to ensure that the product does not break when it comes to achieving those particular goals.
It is important to remember that incorporating AI into testing processes is still a process in motion. But it is also important to keep an eye on innovations and advancements in this regard.
AI will help tester’s lives easier in aspects of greater accuracy and speed. By detecting bugs quickly, AI can provide testers with the time and mental energy to create better testing methods, write better test scripts and provide ways to the best possible user experience.
Some Artificial Intelligence/Machine Learning Test tools are as follows:
Applitools are basically used for Visual Testing.
The algorithms used in Applitools are entirely adaptive in the aspect of AI and Machine Learning.
Possible AI Type Features using Applitools:
1- Leveraging ML/AI-based for automated maintenance (being able to group together similar groups of changes from different pages/browsers/devices)
2- Modification of comparison algorithms to identify what changes are meaningful/noticeable.
3- Being able to automatically identify which changes are more likely to be bugs vs. desired changes and prioritize functionality.
Testim tries to maximize the advantage of machine learning to speed up the authoring, execution and most importantly the maintenance of automated tests.By this Testers can make sure that their test execution is accurate.
Testim focuses on reducing test maintenance, which is the most common challenge for most of the Organizations.
The main goal of Testim is to help liberate test automation from the exclusive realm of developers and make it simple enough for anyone on the team to create.
Mabl is similar to Test.AI.Mabl terminology provides an interaction between the tests and the application.The interacted tests will run at a predetermined amount of time and alert the Testers.
Three main features of Mabl are:
1- Mabl can automatically detect the changed or modified elements of the application and dynamically updates the tests to handle those changes.
2- Mabl can continuously compare test results to test history to quickly detect changes and regressions, resulting in more stable releases.
3- Mabl is helful in identifying and locating problems quickly, which is helpful in alerting the Testers to possible impacts before they affect the customers.
ReTest tries to bake AI intelligence into their tool, without having any specific programming skills.
Using ReTest tool, Testers do not need to select element IDs to work with when creating a script also. ReTest also automatically takes care of wait times.
ReportPortal provides a machine-learning algorithm which helps Testers to analyze test results automatically.
This machine learning algorithms use all the historical data that is already in the dashboard database for any project. Through which it can analyze the latest execution of test cases which gives them the confidence to the testers about the status of their test cases and execution.
The ability to analyze large amounts of data is the perfect use of machine learning.
Test.AI is an AI tool that provides cognitive abilities to Selenium and Appium. It requires no programming or coding skills and Tests are defined in a simple format that can be easily understandable.
AI can identify screens and elements dynamically in any app which can automatically drives the application to execute test cases.
Sealights is a Cloud-based platform.
With their machine learning-like technology that analyzes both the code and the tests that run against it, it lets you know exactly what your tests are covering and what they’re not. But when Sealights say “tests,” they not only mean the unit tests, the “Tests” mean here is any kind of test, from functional, manual, performance, you name it.
Sealights enable “Quality Testing” which helps in checking the exact files/methods/lines that are updated or fixed by the last build and not tested as it provides high stability. It ensures that untested code will not reach production before undergoing minimal validation.