The next major thing in human-AI collaboration could be generative artificial intelligence (Gen AI). It uses powerful machine learning to supplement, expedite, and automate content creation via the assimilation of unstructured, multimodal data such as text, code, photos, audio, 3D, and video. Its rapid evolution over the last 6 to 8 months (2023-24) has seen several releases across models, datasets, and applications. The potential economic benefits of generative AI are enormous, delivering higher productivity, automation, and enhanced decision-making capabilities, boosting growth, efficiency, and competitiveness across a wide range of industries. In India, generative AI is predicted to grow at a CAGR of 36% from US$ 11.3 billion in 2023 to US$ 51.8 billion by 2028.
The annual economic value of generative AI in 2023 is predicted to range between US$ 2.6 to US$ 4.4 trillion globally. Approximately 75% of this value is predicted to focus on a few functions: sales and marketing, software engineering for product development and corporate IT, customer operations, and product and R&D. The global AI market was expected to reach US$ 450 billion in 2022, expanding at a rate of more than 20%. In India, AI expenditure reached US$ 665 million in 2018 and is predicted to reach US$ 11.78 billion by 2025, with a 39% CAGR between 2019-25. The revolutionary potential of generative AI to alter sectors and drive economic growth is well recognised. According to a report in 2022, Generative AI could enhance global GDP by 7% (US$ 7 trillion) and productivity by 1.5% over the next decade. Furthermore, it is anticipated that harnessing the power of Generative AI might contribute US$ 15.7 trillion to the global economy by 2030. According to the World Economic Forum, there will be a 40% increase in the number of AI and machine learning experts by 2027. Gen AI has become a significant instrument for India, opening up economic potential in a variety of industries.
Generative AI in India
Artificial intelligence (AI) is the process of making robots replicate human intelligence, and Generative AI is a branch of AI that uses machine learning technology to generate new content. To create fresh outputs that closely match the original input, generative AI works on the notion of learning from patterns and structures discovered in large datasets. To emulate human creativity in a machine-driven manner, this technology draws influence from domains such as deep learning, neural networks, and probabilistic modelling. Large Language Models (LLM) are neural networks capable of analysing and comprehending natural language. They are commonly trained on big datasets and can be used for tasks like as text production, classification, question response, and machine translation.
Generative AI models
GAI models can be categorised into 2 categories:
It takes instructions from the same modality as the created content modality, whereas multimodal models accept cross-modal inputs and produce outputs from many modalities. These models can be utilised for a variety of NLP applications, including dialogue systems, translation, and question-answering. Decoder models and encoder-decoder models are examples of this model. Other types of unimodal models include vision generative models.
Multimodal generations are more difficult to learn than unimodal generations. The development of innovative multimodal technologies in visual language generation, text audio generation, text graph generation, and text code generation contributed to addressing this issue. Chatbots, AI art generation, music generation, coding for AI-based programming systems, and education are all examples of how these architectures are used.
Key Factors Shaping India’s Success in Leveraging AI
Generative AI is automating, augmenting, and speeding a variety of service offerings, resulting in four areas of opportunity for technological services:
1. Expansion in the addressable market
Over the next five years (by 2028), generative AI is expected to generate a new market for related services and potentially unleash an additional 15 to 20% topline growth for leading providers who take an aggressive approach and make early moves. This expansion will be fuelled by four factors:
2. Delivery excellence
Service delivery operations across service lines are projected to become more efficient using Gen AI. Early tests suggest that this could result in a 20-30% increase in productivity for specific use cases in service lines such as application development and BPO. The productivity boost at the organisational level is predicted to be incremental, with a 10-15% rise in the first 12-18 months after deploying the technology at scale, with the potential to reach about 20 to 30% in the next 2-3 years (2025-26).
3. Sales excellence
Gains in productivity can be expected across the sales and marketing value chain, from lead generation to speedier sales plan formulation. According to NASSCOM, 90% of CXOs in leading sales teams believe generative AI will become mainstream in sales within the next 2-3 years (2025-26), with lead identification being the most influential use case across sales processes, according to more than 60%.
A pricing-assist LLM, for example, can aid in recommending the best business model for an account or a certain agreement. Recommendations would be based on the integration of data from numerous sources, such as historical deal data. Large providers have conducted pilots to increase ROI and sales effectiveness. For instance, Accenture and Salesforce collaborated to automate some prospecting duties in order to increase seller productivity. They are also concentrating on hyper-personalized outreach, a contextual conversational bot, and customised training content for sales personnel.
These solutions are based on proprietary datasets that rely on a mix of external and internal data sources. Company reports, public articles, and market deal databases are examples of external data sources. Internal proprietary data sources include pricing data from past proposals, historical deals in the last 5 years (2017-18), account plans, and deal renewal data for all clients.
4. Productivity gains in G&A
Over the next three years (2025-26), automation, and augmentation of tasks in F&A, legal, and HR are predicted to increase productivity by 40% (including sales). It can aid in the automation of typically time-consuming operations such as summarization in order to develop workflows, contracts, and reports.
Generative AI tools from India
KissanGPT is an AI chatbot that exclusively serves India's underserved agriculture domain by leveraging the power of GPT 3.5 and the Whisper model. KissanGPT, which was launched on March 15th, 2023, has already won the hearts of farmers all over the country with its exceptional ability to help farmers in irrigation, pest management, and crop production.
The Bengaluru-based company Plum has launched PolicyGPT to revolutionise the insurance sector. The GPT-3-based chatbot's objective is to educate clients about their health insurance policies. Furthermore, the chatbot is intended to simplify insurance policies by addressing coverage questions and clarifying inclusions and restrictions.
The AI chatbot known as GitaGPT was created by Mr. Sukuru Sai Vineet, a software developer for Google India. It leverages GPT-3 technology and the Bhagavad Gita to provide solutions to life's issues. Users can ask questions on the GitaGPT app, and a chatbot will respond by investigating the teachings of the Bhagavad Gita.
CoRover, a conversational AI platform located in Bengaluru, just announced BharatGPT. In contrast to OpenAI's ChatGPT that only supports 95 languages and mostly understands English instructions. CoRover's chatbot can analyse rich data kinds other than text, such as photographs, audio, video, and maps, which the current version of ChatGPT cannot. While the accuracy of ChatGPT has yet to be confirmed, CoRover claims that BharatGPT is 90% accurate.
Lexi is an AI chatbot created by Velocity, a finance business powered by ChatGPT. Lexi aims to assist e-commerce companies by streamlining company analytics. Lexi, which is connected with Velocity's analytics tool, Velocity Insights, supports firms in measuring market expenditure, sales, and more, and sends daily business reports over WhatsApp. Furthermore, clients can now use Lexi, the AI chatbot, to get answers to their questions, enhancing their business operations.
Under the Bhashini objective, Jugalbandi is a free and open platform that combines the capabilities of ChatGPT and Indian language translation models to power conversational AI applications in any domain.
Even though the Indian Constitution mandates free legal help for all citizens, even for those who cannot afford a lawyer. Former Chief Justice of India (CJI) Mr. Ranjan Gogoi stated that the lawyer-to-population ratio in India is much lower than in developed countries. One lawyer is available for every 1,800 people. Mr. Mandaar Mukesh Giri, a lawyer from a legal family, created LawBot Pro with the goal of delivering much-needed legal assistance to India's enormous population.
The number of generative AI startups in India has more than doubled between 2021-22 and 2023-24.
Total private investments in AI were estimated to be around US$ 8 billion between 2013 and 2022, with US$ 3.24 billion raised in 2022 alone across 1,900+ AI businesses in India. Indian generative AI start-ups have already raised more than $590 million in total funding as of May 2023. More than US$ 475 million, and 80% of this funding, was received from 2021 onwards. Over 90% of the investments went to
Some of the active Domestic Investors are Venture Catalysts, Titan Capital, 9Unicorns, Mumbai Angels, Kalaari Capital, etc. Some of the active Foreign Investors are SOSV, Artesian, Y Combinator, BPEA, Alpha Wave Global, etc.
Institutional investors hold 72% of the total investor composition, whereas corporate investors hold 28%. Some of the active Corporate Investors are Rosebay Consulting, Cosme Matias Menezes, Zoom, Fractal, Times Internet, etc., and active Institutional Investors are SOSV, Venture Catalysts, Titan Capital, Artesian, Nexus Venture Partners, etc.
The Greater Bengaluru region has the highest percentage of generative AI startups in India in 2023, at 45%. The city's entire environment, comprising deep tech, startup landscape, high-end innovation-driven institutions, vast industry presence, and a burgeoning class of domestic angel investors, is a big draw. The second-largest pool is available in the Mumbai and Pune regions in 2023, at 21%. This region is home to some of the most well-known institutional investors and venture capitalists, as well as a varied talent pool.
The knowledge-based advisory industry and BPM firms have a substantial presence in Delhi NCR. Startups with applications that fit into these categories also benefit from the innovation ecosystem of established educational institutions and a huge pool of college graduates. Hyderabad has a nation-leading innovation infrastructure, notably for the deep tech ecosystem. Chennai has the advantage of being the country's main SaaS hub, with a significant presence of product organisations, as well as targeted innovation and R&D labs of the global capability centres (GCCs) in India.
India is well-positioned to make use of generative AI to promote innovation across many industries and drive economic growth owing to its rich data resources, creative sector, and diversity of linguistic groups. The generative AI growth has also prompted widespread calls for the technology to be regulated. It represents an unparalleled chance to democratise the powerful tool of the computer, making AI available to millions of people who can utilise it for their profit, the benefit of enterprises, and the advancement of the country. The advent of LawBot Pro and other generative AI tools indicates an important turning point in India's technology landscape. India can open a world of innovation, empowerment, and inclusive development by embracing generative AI responsibly and tackling its challenges. With better productivity, automation, and decision-making skills, generative AI encourages growth, efficiency, and competitiveness across industries, and it has the potential to contribute US$ 7 trillion to global GDP and increase productivity by 1.5% over the next decade (2023 onwards).