10 success mantras for AI and ML startups

Success for an artificial intelligence (AI)/machine learning (ML) startup is offering AI-inspired products and services that lead us to a more comfortable future.

Inspired by entrepreneurial success and lessons learnt the hard way, here are 10 mantras which may help you.

1. Solve a genuine problem

AI, when employed effectively, is a brilliant tool for increasing efficiency and reducing our stress. But, with virtually limitless potential today, it’s easy as an AI entrepreneur to get sucked into the pitiful group of businesses that develop meaningless and insignificant "AI for X process."

While you zero in on the type of AI model to build, ensure that it has the potential to revolutionise a process, and not just make it slightly more bearable. And, considering the impact of the coronavirus on world economies, building a unique product and solving a genuine problem will be the only legitimate way to survive.

2. Enter AI to innovate, not to trend

I get it! In 2020, it's trendy to be a part of or run an AI startup. Hysteria similar to that of the dotcom era surrounds the AI space, and every business out there is attempting to roll out an AI solution. While that's fine, and it's great to be a part of emerging technology, ask yourself this question: why are you an AI entrepreneur?

Is it because you're passionate about AI's potential and have a great AI-inspired solution that will add a remarkable value to customers? Or, is it because it looks fancy for a business to have "AI" next to its name, and because it might grab more interest from investors and prospects? If the latter is where you stand, I'm afraid to tell you that just like the dotcom bubble, the AI one will burst too. And in the end, only businesses with legitimate offerings will remain standing.

Understand what it truly means to be an AI startup and venture forward.

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3. Constantly improve your systems

AI as a discipline is a growing one, and researchers and developers are regularly sharing updates on improved AI model development strategies and techniques. Stay on top of such industry news and determine whether the model you are working on can benefit from the latest research. AI models today by nature can easily find themselves outdated, so it's crucial to keep refining them.

4. Avoid false AI claims

Branding has become so powerful that a savvy marketer can make fake services seem terrifyingly real. If you're guilty of branding yourself with terms such as "AI-inspired" and "powered by AI" while all your business offers is a simple automation tool, you've already begun on the wrong footing. Clients will be able to eventually see through the modern Mechanical Turk you've built, and investors will know too. Exercise caution!

5. Be picky with your training data

Training data is the fuel that brings AI models to life. Similar to how a diesel engine cannot operate when filled with petroleum, AI models will malfunction when fed the wrong training data. Be it annotated images or categorised content, ensure that they clear all crucial training data requirements. Source from proven teams and allow no room for compromise.

6. Research before executing

So, you have a great idea. But bummer! Amazon and Facebook are already 90 percent through the development process for what you have in mind. While that can be disappointing, what will truly break you as an entrepreneur is investing large resources and time into an already executed idea. As a startup, it's close to impossible to make a product that competes with the big boys of tech, for they have virtually unlimited access to the best engineers and data scientists.

While I do not intend to discourage you from working on a product you hold a strong passion for, I've noticed a lot of budding entrepreneurs and product developers mentally suffer from feeling helpless while competing with big tech. Steer clear of this problem by doing relevant market research.

7. Hire only the required talent and hire smart

Remember, as an aspiring entrepreneur, you're limited on resources. Only spend on what your AI model genuinely requires entering the market. Invest heavily in qualified data scientists and machine learning engineers. Also, you will require an effective sales and business development team that understands the AI space thoroughly, so ensure resources are allocated to set that in place.

So yes, your primary requirement concerning manpower will be a team to develop your product/service and a team that identifies potential clients and markets your offerings.

8. Understand areas for possible expansion

Businesses grow when they expand their offerings. Expansion is key for businesses to stay relevant, and with ever-increasing AI use cases, it makes complete sense for an AI business to explore their options. For example, if your business successfully sells customer service chatbots, you could consider creating something like a gymming coach chatbot. Explore your avenues and seize that bread!

9. Use tools to speed up your processes

There's no need to waste time and money in building in-house tools for development processes. Your AI business can make the most of readily available tools in the market to aid your AI model development. Be it training data or libraries for differential programming across tasks, a tool exists in the market, for an affordable price. Employing such tools will allow you to focus more on the core model development. Simply put, if the tool’s in the market, make use of it!

10. Build responsible AI

As guardians of our planet, we should always remember that the primary purpose of AI development is to improve our lives significantly. Consumers will love you for introducing an AI solution that can address a lifelong pain point (Eg: customer care chatbots, autonomous vehicles, etc).

On the contrary, the world will despise you for creating AI-inspired tech that considerably reduces their quality of life (Eg: systems that threaten user privacy, fake news generators, systems with gender bias, etc.). Use your hard-earned skills to make the world a better place not just for you, but for generations to come.

(Edited by Evelyn Ratnakumar)