Why Entrepreneurs Need to Pioneer the AI ​​Revolution

AI is also waiting for its first concrete mass-market applications. Image: Shutterstock

AhArtificial intelligence is a buzzword these days. From students to politicians, everyone is discussing how artificial intelligence (AI) will shape the future world. With all this AI hype, there are plenty of new job opportunities and opportunities to build new businesses from scratch. To understand the scale of the opportunity, one has to wind the clock back to his late ’90s.

In the late 90’s, the now ubiquitous Internet was in its infancy. The Internet was in its infancy and most of the world used dial-up connections. Entrepreneurs saw this opportunity and rushed to start and build dot-com companies. This led to a market crash known as the dot-com bubble burst. This market crash wiped out companies and products that didn’t have a great value proposition or a poor product-market fit. But looking back on the events of those days, I can now clearly accept the benefits of the experiments that were carried out at that time. A large internet and e-commerce company of our time, dotcom emerged from his burst unscathed and went on to change the world. Another benefit for human society is the emergence of free internet his browser.

Just like in the late 90s, when everyone was convinced that the Internet would change our lives, in today’s world there is a broad consensus that AI will do the same (hopefully for the better). direction). Just like entrepreneurs jumped out to build dotcom companies in the late 90s, today’s entrepreneurs are pushing the boundaries of what’s possible with AI. Do you mean that we are headed for a bubble? I certainly hope so. The AI ​​bubble, like the dot-com bubble, leads to a period of experimentation in which entrepreneurs acquire the resources to push the boundaries of possible applications. Some speculators and even real investors will bear the brunt, but the result will be the next revolutionary company that will change the way we live. AI companies with the right value proposition have the potential to guide and shape human life for thousands of years to come.

So how far are we in this cycle as far as AI is concerned? The rush to build AI-driven companies is yet to come. Data is at the heart of any artificial intelligence we can build today. We are now in an era where data is mostly collected and managed by internet companies and some of the big financial services companies. The debate over whether the data is owned by the individual who created it or by the platform on which it was created is still unresolved. Regulations like his PSI Directive in the EU are just beginning to loosen their grip on data. It remains to be seen if this kind of law will become commonplace in other parts of the world.

Also read: How AI will democratize strategy for the next industrial revolution

AI is also awaiting its first concrete mass-market applications. Self-driving cars and AI-powered security bots could portend an AI rush. For early-stage entrepreneurs, there are headwinds such as access to data and funding. I believe these headwinds will abate significantly over the next few years. AI has been mostly applied in areas such as computer vision and natural language processing, which is very impressive, but there are no concrete mass-market applications yet. As mass-market applications emerge and the few companies’ control over data loosens, they will rush to build the next multi-billion dollar company. The best bet for early-stage entrepreneurs is to look for applications where AI has not yet been adopted. Just as internet entrepreneurs have redefined our shopping habits, AI entrepreneurs should look to redefine the old ways of doing things. For example, AI drones can be used to detect potholes, or AI-powered patrol bots can be used for security. The opportunity to use AI to incrementally improve trivial tasks such as perimeter protection has tremendous potential and applications. AI-assisted agriculture is another area where there remains great opportunity for experimentation. The best opportunities for applying AI are in the real world, not the virtual or digital world.

Early-stage entrepreneurs should first learn the basics of AI. A basic understanding of the algorithms that drive AI can help entrepreneurs understand the nuances of data collection for building AI. It also helps us understand how AI applications are built and used. This foundational knowledge enables early-stage entrepreneurs to seek out and recruit the right talent to build their products. Also, professional investors often look for founders who have some background and understanding of the field.

Armed with just the basics, early-stage entrepreneurs can go out and find real-world applications. Identifying these opportunities is the first big step in the right direction. After this, entrepreneurs can start thinking about collecting core data for their startup. At least at the current stage of AI evolution driven by supervised models. With the right team and the right data build, AI applications in the cloud are within the reach of every entrepreneur. Pitch your idea to professional investors with a minimal viable product (MVP) built in the cloud. With some tailwinds, a good idea and a working MVP, fundraising isn’t the hardest part. Finally, the funds raised will allow us to expand the company and take it to the next level.

This seems easy on paper, but it’s probably the hardest thing on the planet. However, if you want to try something hard, always do it in a booming industry. The AI ​​growth story is just beginning.

Dr. Aditya Narvekar, Assistant Professor and Deputy Director, Student Engagement and Empowerment – Bachelor of Science in Data Science, SP Jain School of Global Management