Generative Artificial Intelligence (AI) is more than just a buzzword now. A cutting-edge technology that completely changes how businesses operate, from automating activities like creating content and image generation to coding. In this context, Cigniti recently conducted a highly-engaging roundtable discussion with IT CXOs and other senior business leaders from global enterprises to discuss generative AI's real-world implications.

Speakers who were part of the thought-provoking discussion conducted jointly with NASSCOM were:

• Lakshmana Kumar Kothapalli - Global Head of Enterprise Data Platforms at S&P Global, Digital Technology Services
• Abhinandita Mudgal - Chief Operating Officer, DBS Tech
• Abhilash Rao Duggyala - Executive Director, DBS Tech
• Jitendra Bahri - Head P&C Solutions Operations, Swiss Re
• Kishore Seshagiri - Executive Director, Broadridge
• Sanghamitra Pati - Managing Director, Strategy and Analytics (AI and Data), Consulting, Deloitte
• Shisheer Kotha - Director, Smart Manufacturing and AI, Micron
• Raghu Krovvidy – Chief Delivery Officer, Cigniti
• Srinivas Atreya – Chief Data Scientist, Cigniti
• Ankit Bose - Head of NASSCOM AI | CEO for Center of Excellence Data Science and AI
• Raghu Kumar - Regional Head, Telangana & AP, NASSCOM

The roundtable culminated in an enriching conversation where the eminent speakers discussed generative AI's real-world use cases and applications of enterprises, new areas for growth, ways to address concerns around IP and quality, and more. The CXOs highlighted where the technology stands globally and how business leaders challenge the status quo.

The rise of Generative AI

In today's hyperactive business environment, the use of generative AI is rising, and why not? After all, if properly used, this technology has the potential to significantly reduce the time, effort, and cost required to run a business.

Business leaders, especially CEOs, are naturally curious about whether generative AI is just a fad in the IT world or a potentially game-changing opportunity amid the buzz around it following the introduction of ChatGPT, Bard, Claude, and Midjourney. And if the latter is true, what benefit does that have for their company? 

Amid all speculations and skepticism, the market for generative AI is expected to expand at a CAGR of nearly 36% by 2032. Since 2020, venture capital companies have invested more than $1.7 billion into generative AI technologies.

Ethical concerns around Generative AI

According to Gartner, generative AI will continue to impact business operations across industries. The analyst firm also forecasts that by 2025, generative AI will account for 10% of all data generated, up from 1% in 2021.

However, amid all the buzz, the rapid development of generative AI capabilities raises some ethical issues too.

Artificially created content, including photos, videos, and audio, can be produced using generative AI, especially machine learning techniques like Deepfakes. AI-generated content can be used to distribute false information, sway public opinion, and even harass or defame people.

According to Harvard Business Review, there are legal risks associated with this procedure, such as intellectual property (IP) violations. Organizations must be aware of the hazards and how to take precautions before embracing the advantages of generative AI in its complete form. Although generative AI can be a new technology in the market, existing legal rules can significantly impact its application and usage. Legal entities are now analyzing and deciding how to apply the existing laws.

Real-world use cases of Generative AI

During the roundtable discussion, the CXOs elaborated on generative AI's potential to revolutionize several industries. Here are a few key use cases discussed.

Banking 

Generative AI-powered chatbots can offer 24/7 customer care help, freeing human agents to concentrate on more complex problems. This feature can increase productivity and eliminate the issues associated with labor-intensive processes. This can promote cost-optimization for banks by increasing productivity and customer happiness. ML models can be created using generative AI and trained on private data, enabling enterprises to develop error-free models without disclosing sensitive data.

Insurance

For insurers, generative AI dramatically speeds up digitalization. It can help insurers evaluate risk, spot fraud, and minimize application-processing human error. It is revolutionizing the insurance sector with its solutions for fraud detection, fraud optimization, automation, and optimized claims.

E-commerce 

Generative AI presents various opportunities for online merchants to suggest goods and services to customers based on their past purchases, historical data, most-searched items, browsing habits, wishlist items, and other data points. These opportunities range from personalized product recommendations to tailored content, discounts, and deals.

Software development 

Generative AI will improve development speed, efficiency, creativity, and accuracy as it gets integrated into tools and workflows. Once AI is appropriately trained on security measures, it can help companies ensure compliance as the code is being written, resulting in more robust applications from the outset. As security becomes more integrated within the SDLC, developers will have more time to focus on the creative aspects of app ideation. 

QA – Testing

Software testing with AI is not a new concept. Numerous software testing and deployment systems have long provided AI-powered "bots" that can browse around applications, choose what needs testing automatically, and then execute the tests. Automation engineers can use these bots to design test cases and run tests. These engineers have traditionally seen AI as a tool that can help minimize some of the labor and dullness involved in doing simple tests.

Drug discovery

The potential of generative AI models to generate new data samples based on the patterns discovered in their training data can aid in developing new chemical structures or drug candidates in drug discovery. Generative AI helps analyze large datasets, predict innovative drug candidates, and optimize chemical structures. By 2025, generative AI will be used in 50% of drug discovery and development efforts, according to Gartner.

Manufacturing 

Generative AI has the potential to develop the manufacturing and production industry significantly. Manufacturers collect information from an endless supply of sources. Data is essential to daily operational decision-making, whether it is time-series data from traditional sensor equipment, real-time video feeds, or haphazardly and manually filtered reports. Generative AI enables manufacturers to overcome data-quality constraints and unleash the full potential of AI in manufacturing.

Generative AI to touch and transform every industry

Organizations across the industry must develop technical skills and upskill their employees to effectively utilize generative AI for business benefit. This requires a coordinated effort by the company's leadership to determine the necessary capabilities based on the use cases given the highest priority. This effort will probably go beyond technical jobs to encompass a talent mix spanning engineering, data, design, risk, product, and other business activities.