Generative AI can best be seen as a human-algorithm symphony that centers around a deep collaboration between humans and machines. It falls under the umbrella of machine learning (ML) and sits at the crossroads of deep learning (DL) and natural language processing (NLP). The unique positioning grants it the potential to revolutionize the fundamental way we conduct our work.

Generative AI can be understood as a branch of artificial intelligence (AI) that utilizes computer algorithms to produce outputs resembling content created by humans. This content can take various forms including text, images, graphics, music or computer code. Generative AI algorithms are trained using data that consists of examples representing the desired output. By analyzing patterns and structures using the training data, these algorithms generate new content that exhibits characteristics like the original input data. As a result, generative AI has the capability to create content that appears authentic and human-like.

In recent years, generative AI has experienced remarkable advancements, capturing significant attention from the public and especially content creators. Various tools have emerged, stirring excitement within the technology community. These tools often rely on input prompts to guide them towards generating desired outcomes, as exemplified by ChatGPT (an AI language model that can generate human-like text based on provided prompts), DALL-E (specifically designed to create images and artwork based on text-based prompts), and GitHub Copilot (a collaboration between GitHub and OpenAI, and it suggests code completions for users of popular development environments).

Impact of generative AI on various industries

Generative AI is set to have a profound impact on numerous industries, including pharmaceuticals, manufacturing, media, architecture, design, engineering, automotive, aerospace, defence, medical, electronics and energy. It will augment core processes within these sectors by leveraging AI models as well as influence supporting processes that span across organizations, affecting areas such as marketing, design, corporate communications, training and software engineering.  

In the pharmaceutical industry, generative AI shows promise in reducing costs and time in the drug discovery process, and in the coming decades, new drugs and materials will be systematically discovered using generative AI techniques. In marketing, outbound marketing messages from large organizations will be synthetically generated. Text generators like GPT-4 are already capable of creating marketing copy and personalized advertising, enhancing marketing campaigns.

In manufacturing, automotive, aerospace and defense industries, generative design can play a pivotal role. It can create designs optimized to meet specific goals and constraints, such as performance, materials and manufacturing methods. This accelerated design process offers engineers a range of potential solutions to explore.

Generative AI’s contribution to the global economy

Generative AI has the potential to create substantial value and boost global economic productivity by trillions of dollars. Its impact will be felt across various industry sectors, with banking, high tech and life sciences anticipated to experience significant effects in terms of revenue percentages. In the banking and retail & consumer packaged goods sectors, fully implementing generative AI use cases could deliver an additional value in billions of dollars annually.

Creating value for IT Services

Generative AI holds significant potential for technology companies and their value streams. By leveraging this technology, these companies can enhance various aspects of their operations. Generative AI empowers technology companies to streamline their processes, increase efficiency and deliver innovative solutions, which enhances their value stream and enables them to stay competitive in a rapidly evolving industry. Let’s look at four key areas where generative AI will become integral in its daily use and how it creates value:

1. Business process operations: Be it the front office (customer care/service), middle office (supply chain and risk) or back office (finance and procurement), generative AI has a place in various uses like process analysis, execution and improvement. Synergy is created with these uses in the form of prompt automation, faster text generation and improved productivity, SOP generation, knowledge summarisation, language translation and assisted process re-engineering. This allows for a much better customer experience.
2. Application development and support: Creating custom software, data management and analytics and the software development process will be impacted by generative AI. In places like process mining, experimentation, governance or change management, AI can help increase the value by coming up with intelligent apps. Generative AI finds itself helping in model development, automating workflows, knowledge mining and automating code generation.
3. Infrastructure & operations: In various areas of hybrid infrastructures, end-users and devices and cybersecurity, generative AI can have a real impact, especially when it comes to root cause analysis, troubleshooting and remediation or restoration and support. Generative AI can help boost customer value by increasing service assurance. This comes in the form of assisted threat modelling, enhanced employee performance, analysis, potential RCA extraction and much more.
4. Systems and product engineering: A logical place for generative AI is in places like software product engineering, silicon, mechatronics and operational technologies. Value is created in the form of AI-enabled devices and systems for various customer requirements during planning, designing, building and support. In such cases, generative AI can help improve efficiency through feasibility analysis, enable smart factory automation, test and optimize code or even provide automated support.

Streamlining automation and enhancing problem-solving

Generative AI offers significant benefits in terms of automation and efficiency. By using the correct prompts, generative AI can produce original outputs such as code or complete business applications in real-time. This reduces development time and allows IT professionals to focus on more complex tasks, fostering creativity and problem-solving. Moreover, generative AI enables a seamless interaction between humans and IT systems, empowering technology companies to identify and resolve issues or optimize inefficiencies through natural language generation. It also facilitates the creation of advanced systems like chatbots or customer support systems that can respond to user queries in a more emotionally intelligent manner.

Another notable impact of generative AI lies in its ability to generate synthetic data, which mimics real-world data. This synthetic data is valuable for testing systems without the risk of exposing personal or sensitive information, addressing the scarcity of quality data in the industry. Many organizations struggle with fragmented and low-quality data scattered across various silos, making it challenging to identify and utilize effectively. Synthetic data generated by generative AI can help mitigate these challenges, particularly in regulated industries that require compliance standards to operate smoothly.

HCLTech and Generative AI

Recognizing its massive potential, HCLTech is an early adopter of generative AI technologies, taking part in an OpenAI and Microsoft Copilot focused tech development. In addition, HCLTech has recently launched a Generative AI Labs, which supports teams in building solutions and services across various roles and domains, including System Engineering, Process Operations and Support. These labs will also drive a generative AI skills academy that train people on how to best use the technology. With a philosophy of consulting, creating, infusing, embedding and integrating AI from silicon to infrastructure, applications, data and business processes.

HCLTech, with its engineering heritage, has been involved in co-creating AI technology for the last two decades. Demonstrating this, it has deployed AIOps in operations and engineering business at scale for over a decade and has carved those solutions to fuel the intelligent automation (DRYiCE) product line in HCLSoftware. With a robust AI ecosystem and extensive range of AI services, HCLTech serves its clients through entire spectrum of their AI needs across hardware, software, data, algorithms and cloud.

HCLTech sees generative AI as a promising field that opens potential new services opportunities across four key domains:

1. Prompt engineering: Testing and refining prompts to optimize them for specific tasks
2. Data engineering: Creation and capture of generative AI driven engineering insights
3. Integration and orchestration of intelligent apps: Orchestration across NLP, generative AI systems and knowledge systems for text, image, video and audio
4. Responsible AI: An AI system that enables privacy and security, in addition to other attributes like inclusion, fairness, accountability, trustability and reliability

What next?

Generative AI is reshaping the technology landscape by automating tasks, enhancing cybersecurity and fostering innovation. By leveraging the decades of experience in machine learning and neural networks, HCLTech has unlocked new opportunities, improved operational efficiency and delivered cutting-edge solutions to meet the evolving demands of the digital era.

With responsible implementation and a human-centric approach, generative AI has the potential to drive transformative change across the technology industry and empower businesses to thrive in a rapidly advancing technological landscape.

Kalyan Kumar was speaking to HCLTech's Nick Ismail, Global Head of Brand Journalism