With the fall of Indian startup funding, how will the AI market sustain itself in India?

The uncertainty ruling global markets has affected Indian startup funding too, as startups found themselves unable to convince investors to move faster during this period. That has shown up in the numbers for July during which the total investment fell below the US$1 billion mark, a new low for 2022. Homegrown startups have managed to raise US$870 million across 118 deals in July, according to our data tracking platform Fintrackr. This comprises 14 growth-stage deals and 104 early-stage deals. This is the lowest funding month of 2022 and the second lowest since January 2021 when the total funding was recorded at US$807 million. Indian startup funding fall creates doubt about whether the AI market in India can sustain itself for long. But here are some developments and growth that show how AI is implemented in different areas.

Amidst the fast-paced advancements in technology, Artificial intelligence has been the most revolutionary creation and is expected to have a more noticeable impact on the evolution of mankind. Artificial intelligence has the potential to drastically improve the efficiency of a workplace by augmenting the work humans do. By taking over repetitive tasks of business operations, AI frees up the human workforce and allows them to focus on studies involving creativity and empathy.

AI Landscape in India

The artificial intelligence market in India is expected to grow at a five-year compound annual growth rate (CAGR) of 20.2% and reach US$7.8 billion in total revenues by 2025, according to a report by the International Data Corporation (IDC). Businesses in India are expected to accelerate the adoption of both AI-centric and AI-non-centric applications for the next five years. AI is clearly making an impact across a diverse range of industries, organizations, and government sectors. It's clear that companies are implementing AI in their core business, organization, and agency operations, and not just talking about it.

The World Economic Forum this year identified cybercrime as posing a more significant threat to society than terrorism. Hacking and cybercrime will inevitably become a problem as machines take over more of our lives. Adding a device to a network increases the risk of an attack. Identifying those points of failure becomes more difficult as networks of connected devices become more complex. However, this is where AI can help.

Conversational AI is a technology that enables speech-based interaction between users and platforms to improve user engagement at scale. It requires speech recognition, speech synthesis, natural language processing, and machine learning to construct.

Speech Recognition, NLP, and Speech Synthesis have advanced significantly. Language models like Open AI's GPT-3 (the most advanced and most extensive language model ever built) have demonstrated AI's ability to generate human-like speech. Almost there: bots can respond with human-like intelligence, evident in their word choice and prosody (the rhythm, stress, and pitch of speaking based on context).

During the pandemic's last two years, we've seen massive shifts in purchases from offline to online stores, requiring enormous foot traffic. For example, a US$4 billion autonomous driving joint venture between Hyundai and Aptiv, has announced an Uber Eats delivery service. Likewise, Amazon uses drones to deliver packages and test self-driving cars.

Metaverse simulates real-world environments and characters using speech API, computer vision, and natural language understanding. It allows humans to interact and collaborate in their life-like avatars. The end of 2021 saw significant announcements from Facebook, Nvidia, and Amazon about their forays into the digital future. Due to the pandemic-driven shift to a digital world, Metaverse would grow outside gaming and media.

Every industry is looking into how to use AI to improve their business. But unfortunately, very few people know how this technology works and how to use it effectively. In the past, more people could make AI-based business ideas. However, the more people aren't good at AI and ML, the more companies will look for low-code or no-code AI and ML tools. So, while only a few experts will work on the machine learning models, their implementation and use will be open to everyone.