What is an AI platform?

Any type of action-conscious automation needs a method that can use artificial intelligence to assist digitalization and provide outcomes that are at least as good as those that would have been produced by humans. Recently, there has been an increase in demand for this skill, which is where AI platforms come in.

The term “AI platform” refers to a hardware architecture or software framework (including application frameworks) that enables software to address key issues in artificial intelligence (AI), such as reasoning, planning, learning, natural language processing, perception, and object manipulation. Platforms are used to imitate cognitive processes carried out by human minds, like reasoning, social intelligence, and general intelligence.

Statistical techniques, artificial intelligence, soft computing, and conventional symbolic algebra are all methods. Al uses a variety of tools, including iterations of search and mathematical optimization, reasoning, and techniques based on chance and economics.

What is the definition of Artificial Intelligence Platforms?

AI platforms may be categorised as either strong AI, also known as artificial general intelligence, which can solve problems for novel tasks, or weak AI, also known as narrow AI, which is often intended for a specific job.

Artificial intelligence is said to include machine learning. You need trustworthy and high-quality data for it to operate. To get started, simply decide what you want to do, locate the data that is already accessible, and then sit back and let machine learning handle the rest. Without explicit instructions, machine learning uses algorithms and statistical models to carry out a specified task, depending on patterns and inference.


If you want to fully profit from your Al, you must have this feature. Making software that can carry out tasks automatically and without human involvement is called automation. One can save time and resources by automating laborious procedures, freeing up staff time for tasks that demand human interaction. The automation platform you choose should be a simple solution that can handle various automation processes with ease and doesn’t require any special knowledge. You may easily automate tasks with the correct system.

Natural language processing and natural language

For your Al solution to be fully optimised, these two qualities are essential. This is due to the requirement for a system capable of supporting complete speech recognition and interaction. With speech recognition, natural language comprehension, and natural language production, this aids in the processing and analysis of significant volumes of natural language data.

Cloud infrastructure: This capability gives you the resources and scalability to deploy even the most complicated artificial intelligence and machine learning systems. For you to truly profit from Al and cloud, you must integrate the two. When introducing Al solutions, it’s critical to make use of the platform as a service (PaaS) and software as a service (SaaS) to guarantee that all resources are always available.

Understanding the basics of AI platforms and their role in business

Whether you’ve just started evaluating AI platforms or have already made investments in AI technologies like machine learning, it’s crucial to be aware of what’s available in the artificial intelligence platform market right now in 2023. A company’s success may be greatly benefited from learning useful insights on how to choose enterprise AI software and devices and how to integrate such AI tools into your current IT infrastructure. This may be a necessary step as we progress toward the next era of intelligent technologies.

Big Data usage has continued to develop and flourish, with some firms experiencing significant benefits. Platforms for artificial intelligence (AI) have lately brought the processing of big data to a new degree of progress. The next ten years are expected to see considerable effects (and disruptions) from AI systems. Business intelligence and analytics will benefit from hitherto unrealized advancements as a result of the usage of AI to handle large datasets, among many other technologies.

The main forces behind raising an AI platform’s quality are machine learning and data training. Algorithms are used in machine learning to assess data, learn from it, and then create predictions. Decision tree learning, clustering, reinforcement learning, and inductive logic programming are examples of algorithmic techniques. Deep Learning creates artificial intelligence by using algorithms and “artificial neural networks.”

What are AI Platform tools?

A framework called an AI Platform is made to work more intelligently and effectively than conventional frameworks. When properly developed, it enables enterprises to collaborate with data scientists and personnel more quickly, effectively, and efficiently. It may save expenses in a variety of ways, including avoiding duplication of effort, automating basic processes, and getting rid of some high-cost activities like copying or data extraction. Additionally, an AI platform may offer data governance, ensuring that a group of AI scientists and ML programmers uses best practices. Additionally, it can help to make sure that work is finished more swiftly and more equally.

What is AI Platforms’ Role for Businesses?

Big Data analysis with artificial intelligence can offer a greater comprehension of a company’s internal and external dynamics. Artificial intelligence is supported by using the most up-to-date Big Data architecture and machine learning techniques. In 2023, the following will serve as the foundation for a successful AI platform that is both cutting-edge and modern:

  • AI with full access to all information
  • AI that can study the past behaviour of customers or prospects
  • Conscious AI can use self-reference to display strategies that have previously succeeded by using experience from prior, comparable customers.
  • AI continuously observes and learns, spotting patterns that humans might overlook
  • The AI can quickly pick up new information and respond in real-time while adapting to it.
  • AI that uses machine learning to forecast and offer insights based on changes in the data

There are a few standard needs to meet in order to optimise the outcomes provided by state-of-the-art artificial intelligence.

Framework for analysis

Methodologies called analytical frameworks were created throughout time to address certain business issues (often complex). Supporting the system’s artificial intelligence and machine learning capabilities requires the use of an analytical framework.

Context is also a necessity. 

At the moment, machine learning and artificial intelligence are terrible at understanding context. AI is capable of seeing patterns and figuring out what is occurring in the data, but it lacks the ability to go beyond trend insights and identify actions that staff members should do. It is envisaged that AIs will eventually be able to understand context, however, this is not yet a reality. Currently, a human must choose the context and add it to the model.

Appropriate technology is the third requirement. 

An AI-supported platform must be scalable for the AI to learn and develop solutions, unlike traditional analytical systems. While an AI would offer suggestions in real time, a standard analytical system would provide insights into the data.

In order to scale databases up to very large volumes while simultaneously pushing ever-faster transaction rates per second, a variety of different techniques are utilised. The bulk of database management systems employs the strategy of partitioning tables with a lot of data.

A database may expand out over clusters of different database servers using this strategy. Additionally, multi-threaded systems that may significantly scale up transaction processing capacity can now be supported by multi-core CPUs, huge SMP multiprocessors, and 64-bit microprocessors.

What is an Enterprise AI Platform?

An integrated collection of technologies known as an enterprise AI platform enables businesses to design, create, deploy, and manage enterprise AI applications at scale. A new subcategory of corporate software is represented by enterprise AI applications. In comparison to earlier generations of business software, creating and deploying this kind of application at scale includes substantially more difficulties and calls for a new technological stack.

What are the capabilities Enterprise AI platforms enable for Businesses?

A corporate AI platform has to include the following 10 key characteristics in order to provide a full solution:

Data gathering throughout the organisation, a federated data picture that is unified

  • Data persistence and multi-cloud computing
  • Edge processing
  • Platform services and data virtualization are already available for accessing data.
  • Business semantic model
  • business microservices
  • Enterprise data security and governance
  • utilising AI and dynamic optimization methods for system simulation
  • open ground
  • Common platform for software engineers and data scientists to work together on projects

The AI hype cycle is reaching its height, and numerous technologies are marketed as “business AI platforms.” But a solution cannot be referred to as an enterprise AI platform if any one of these 10 essential qualities is lacking.

Why is an Enterprise AI Platform Important?

The main catalyst for digital transformation is enterprise AI. Nearly all corporate software applications will be AI-enabled in the upcoming years. Organizations won’t be able to successfully function and compete without the usage of corporate AI capabilities, just as they would not be able to do business without a CRM or ERP system today. Therefore, it is increasingly necessary for businesses to have the skills necessary to design, implement, and run corporate AI systems at scale.

In order to handle several high-value use cases throughout an organization’s full value chain, enterprise AI apps – dozens or hundreds of them – must be deployed at scale. A corporate AI platform offers the tools and capabilities necessary for businesses to successfully design and run these applications while spending the least amount of time, money, and resources possible.

What do AI Platforms offer Telecommunications?

  • AI enabling intelligent networks

The transformation that alters sectors and businesses becomes a reality as 5G, IoT, and Edge gain pace. The coexistence of cutting-edge and antiquated technology, hybrid networks, a wide range of frequency bands and spectrums, and a proliferation of connected devices all add to the complexity of network operations. In addition, the network has to be further optimised and optimised for performance due to increasing requirements from IoT and industrial use cases. In order to reduce complexity, solve the needs of new technologies and use cases, improve network performance, and allow network automation, the AI Platform is working in this environment.

  • AI and automation

AI platform solutions can assist in achieving a high level of practical autonomous operation by combining a variety of currently accessible and well-understood AI approaches inside a flexible framework. This will usher in a period of intelligent, autonomous networks that require almost no human interaction. The true usefulness of AI will eventually be seen in telecom networks, not only in applications that link to the network.

  • Trustworthy AI

The development, deployment, and usage of AI must be based on trust if their full potential is to be realised. In order to handle issues ranging from explainability and human supervision to security and built-in safety features, it is crucial that we use AI platforms. Trustworthiness is a requirement for AI, and AI platforms assist in designing it into the system.

  • Remote robotics and 5G

Zero-touch operations and an environment where the network would automatically change and respond based on the demands of the robots are being made possible by AI.

The adoption of AI is crucial for effective network management and operations in 5G. The complexity of 5G networks may be addressed with the aid of AI and automation, which can also increase productivity, enhance customer satisfaction, and create new income streams.

  • Network Optimization

By 2025, 20% of all connections will be made using 5G networks, which are anticipated to launch in 2019. By that time, there will be more than 1.7 billion users worldwide.

AI is needed to create self-optimizing networks (SONs) for CSPs to facilitate this expansion.

These give network administrators the ability to automatically enhance network quality based on traffic information relevant to particular regions and time zones.

In the telecom industry of AI, sophisticated algorithms are employed to look for patterns in data, enabling telecoms to both diagnose and foresee network problems.

CSPs can prevent problems from negatively affecting customers by using AI in the telecom industry to proactively handle problems.

  • Predictive Maintenance

Telecoms are being helped to provide better services by AI-driven predictive analytics, which makes use of data, sophisticated algorithms, and machine learning techniques to make predictions about the future based on the past. This suggests that equipment operators can track its health and predict failure based on patterns utilising data-driven insights. CSPs can proactively address issues with communication hardware, such as set-top boxes in consumers’ homes, cell towers, power lines, and data centre servers, by using AI in the telecoms industry. The automation and intelligence of the network will enhance short-term root cause analysis and issue forecasting. In the long run, these technologies will help with broader strategic goals like creating fresh consumer experiences and successfully addressing shifting business needs.

  • Virtual Assistants for Customer Support

Another application of AI in communications is conversational AI systems. They are also referred to as virtual assistants and have perfected the art of efficiently automating and scaling one-on-one conversations. The deployment of AI in the telecom sector aids in managing the vast volume of setup, configuration, debugging, and maintenance support requests that occasionally overburden customer care centres. Using AI, operators can provide self-service capabilities that show customers how to set up and utilise their own devices.

  • Robotic Process Automation (RPA) for Telecoms

Human error is possible in each of the daily millions of transactions that the sizable client bases of CSPs take part in. A type of AI-based business process automation technology is robotic process automation (RPA). RPA may boost the effectiveness of telecom processes by allowing telcos to more easily manage their back-office operations and significant amounts of repetitive and rules-based actions. RPA frees up CSP employees for higher value-added duties by automating the completion of difficult, labor-intensive, and time-consuming processes including billing, data entry, workforce management, and order fulfilment. The RPA market is anticipated to reach a value of USD 13 billion by 2030 and almost all businesses would have adopted RPA by then. Cognitive computing is expected to “substantially change,” according to businesses in the telecom, media, and IT sectors.

  • Fraud Prevention

To combat fraud, telecom companies are turning to AI’s powerful analytical capabilities. Telecom-related fraud, such as false profiles and unauthorised network access, has been greatly reduced thanks to real-time anomaly detection capabilities offered by AI and machine learning algorithms. The system may immediately restrict access to the fraudster after detecting suspicious behaviour, lessening the harm. Given that industry estimates indicate that 90% of operators are routinely targeted by fraudsters, incurring in billions in losses annually, this AI application is very essential for CSPs.

  • Revenue Growth

AI can combine and make sense of a wide range of data, including data from devices, networks, mobile apps, geolocation, in-depth client profiles, service use, and billing. Using AI-driven data analysis, telcos may increase their average revenue per user (ARPU) and subscriber growth rate through intelligent upselling and cross-selling of their services. Telecoms may anticipate customer requests using real-time context to deliver the relevant offer on the right channel at the right time.

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