There is an interesting similarity between the impact of the 18th-century Industrial Revolution on the labor market and the 21st century’s generative AI’s role in transforming IT services. Generative AI is transforming IT services by introducing AI automation, fast-paced data-driven decision-making, innovation, and enhanced customer engagement. While traditional IT system offers a few significant advantages like uninterrupted data access even in a network failure, and full control over Infrastructure, however, the legacy system is weakened with a few limitations including limited scalability, data silos, high maintenance costs, and reduced agility.
Against this backdrop of limitations and drawbacks of traditional IT systems, the emergence of generative AI has revolutionized the industry. In this article, we will explain how generative AI transforms IT services and creates a new era of automation.
Understanding Generative AI in IT Services
Let’s first understand what generative AI is. Before diving into a detailed explanation of the term, let’s try to understand it in simple words. Generative AI is a powerful software that can generate unique content which could be texts, images, videos, voice, and many other types when it receives a prompt or query from the user. If you are a user of Chatgpt or see it in action, you already understand what generative AI is.
Generative AI combines several other AI technologies, including NLP, LLM, and GANs. Understanding these technologies first will help us to understand generative AI. NLP or natural language processing is a branch of artificial intelligence that uses machine learning to enable computers to understand human languages and communicate with humans. Machine learning is the application of algorithms and data to train computers in the way humans learn and perform tasks, and it improves their performance and accuracy by exposing them to more data.
Large language models represent the next generation of basic NLP. What sets them apart from NLP is their use of deep learning. Deep learning is another subset of machine learning that can generate human languages on a large scale. Deep learning is a subset of machine learning that uses multilayered neural networks, also called deep neural networks, that can simulate the human brain’s decision-making process. Deep learning powers our everyday artificial intelligence apps like ChatGPT. Lastly, a neural network is a program or algorithm that works the way biological neurons work in a decision-making process, which involves identifying problems or issues, weighing options, and arriving at a decision.
GANs or generative adversarial networks refer to a machine learning process where two competing neural networks, a generator and a discriminator, work exclusively to generate new content that appears to be more realistic data. In the GENs process, the generator creates synthetic data, while the discriminator verifies the real data and synthetic data and then instructs the former to generate more realistic data.
The main difference between generative AI and robotic process automation is that the former works in a dynamic environment, generating new content using unstructured data, while the latter performs repetitive tasks by using structured data and predefined rules. Generative AI uses human-like cognitive skills to analyze extensive data and generate output such as texts, images, videos, and ideas according to a user’s prompt. In simple words, generative AI is more like a beneficial friend of humans available all the time, providing necessary content when instructed.
Robotic process automation, on the other hand, is ideal for routine, repetitive tasks that require no instant decision making. RPA could be a good replacement for humans for a range of work such as data entry and extraction, report generating, invoice processing, customer care answering, form filling, email notification, and many other similar tasks.
Generative AI’s main capability is depending on user’s different types of prompts or requests, including text, images, animation, video, and many other forms, it could generate unique content that matches the content that the user asked for. Unlike traditional artificial intelligence, generative artificial intelligence’s algorithm uses advanced forms of machine learning, and deep neural networks that simulate human brain functionality to analyze vast unstructured data, encode it, understand natural languages, and then generate unique content that the user is asking for.
Integration of generative artificial intelligence with IT service delivery makes the process efficient, predictive, and less downtime, all of which resulted in greater customer satisfaction. Armed with advanced machine learning, and deep neural networks, creating and delivering content of different types using generative artificial intelligence has become a breeze. The technology helps IT organizations to streamline workflows and provides predictive insights to make preemptive measures to prevent the occurrence of IT issues beforehand. Generative AI’s ability to analyze vast unstructured data helps the IT team to work more efficiently.
Key Benefits of Generative AI for IT Services
Generative AI will drive future IT transformation. Unlike traditional AI integration, which relies on pre-defined rules to perform repetitive tasks, generative AI’s automation, on the other hand, is supported by machine learning and deep neural networks, which allow it to automate complex tasks that require human-like contextual understanding. For example, automating the customer care response task is possible with generative AI. It can also automate the generation of comprehensive business reports. Business automation with the integration of generative AI allows businesses to reduce operational costs, boost productivity, and drive innovation.
Generative AI has revolutionized customer interactions in IT services. Gone are the days when customers needed to spend hours searching for information on half a dozen or more websites to find product information or resolve product-related issues. Intelligent chatbots and virtual assistants developed by generative AI allow customers to find the information they need, provide step-by-step processes to troubleshoot an issue, and even offer personalized FAQs based on previous interactions. This virtual support, powered by generative Ai enhances customer experiences, which in turn boost sales and revenue earnings.
Generative AI has significantly improved the service of help desk software. The software has transformed from reactive to proactive, which means it can detect an issue and send a warning before it occurs. Thanks to the integration of generative AI with help desk software, the software can analyze thousands of millions of real-world IT incident parameters and send warning signals and remedies to the end users to take steps before a major incident.
The introduction of generative AI has radically changed software development. It accelerates the whole workflow of software development by helping in planning, testing, and deployment while minimizing errors in all of the steps.
One of the positive impacts of generative AI on the economy is its potential to create new business opportunities. The technology is being utilized to get AI-driven insights about new business models by analyzing vast business data.
Challenges and Considerations
While generative AI arrived as a blessing to many key business sectors, data security and privacy are two major issues of this evolving technology that business leaders cannot ignore or overrule. The overnight popularity of generative AI occurred due to its capability of generating new content. However, the risk lies if the training data on which generative AI is developed contains someone’s sensitive personal data and inadvertently generates it. For example, a ChatGPT prompt may ask the software a piece of sensitive information about a celebrity. Chances are pretty high that the generative AI software may respond with the correct information and thus jeopardise the privacy of the celebrity.
Data security is another big concern for generative AI-powered software. The backbone of the software is algorithmic programming of trillions of natural language data points in conjunction with the use of deep neural networks. How would you ensure the security of this enormous data when many attackers are roaming to breach the security of the system?
While generative AI has limitless capabilities, from automating tasks to generating unique content, It’s cognitive skills have not reached human level yet. Its limitations lie within it, not from outside sources. Yes, generative AI performs well when it is asked to make a decision when that request falls within its training parameters. However, if the request is complex and its training parameters are not trained enough to process the request and require additional context, it fails to provide a suggestion or answer.
Generative AI’s impact on the labor market is quite similar to what the Industrial Revolution in the early eighteenth century. However, in the case of the Industrial Revolution, when companies needed to train employees for the jobs, generative AI is asking to re-skill the existing workers to fit into the changing job environment. In addition to workers, generative AI requires a change in management as well.
Integration of generative AI with current IT infrastructure is another challenge. Data compatibility, API integration, and security are three major issues that companies must consider when they have decided to integrate generative AI with the current IT infrastructure.
The Future of IT Services with Generative AI
The future of IT services with generative AI is that generative AI will redefine the relationship between IT service providers and companies, evolving from a donor-recipient relationship to a strategic partnership. You may be surprised to know that this relationship-changing process has already begun. In April 2024, for example, the Coca-Cola company and Microsoft signed a 1.1 billion strategic partnership deal. Under this deal, Microsoft’s Azure OpenAI will transform Coca-Cola’s manufacturing, marketing, and supply chain operations. The deal is already benefiting Coca-Cola as it sees enhanced productivity across its global network. The profound power of generative AI will inspire more strategic partnership deals between IT services providers and companies such as science, technology, and innovation companies.
The emergence of new business models will be another prospect of generative artificial intelligence. Several new business models are already emerging on the horizon, such as AI-generated design of clothing products, highly personalized content of product descriptions, and virtual customer care agents.
The global market value of generative AI is showing a bullish trend. In 2024, its market value was 20.7 billion which then started to increase at a compound annual growth rate of 35.5% and is projected to reach 89.9 billion by 2029.
Heavy investment and training will primarily dictate generative AI’s future. The sector, for example, is seeing a surge in investment, starting with $3 billion in 2022 and skyrocketing to $25 billion in 2025. Instead of a slowdown, the pace of investment will accelerate, which will hit $150 billion by 2027.
Another aspect of generative AI’s future would be that it would disrupt the labor market by creating new jobs while abolishing many. It will also force many companies to spend money on re-skilling their existing workforce to make them ready to work in the new work environment of generative AI.
Generative AI is reshaping IT services in several ways. By going one step ahead of traditional artificial intelligence, which can do many automated tasks, generative AI, on the other hand, in addition to automation, can generate new content such as texts, audio, video, images, and designs, and even possess cognitive power like humans. Because of its advanced machine learning algorithm and extensive unstructured data in the cloud storage, generative AI has the potential to transform services of many industries, such as finance, banking, healthcare, entertainment, publication, and many others. With the rapid proliferation of generative AI, integrating generative AI with the existing IT infrastructure is inevitable since the technology is efficient, cost-effective, and more revenue-driving. Most importantly, in today’s fierce competitive business world, embracing generative AI is the demand of time.
