Artificial Intelligence, Technology

How Startups Can Leverage Artificial Intelligence and Machine Learning


Startups today are in an exciting phase. Amazon, Facebook, Google, Netflix, Tesla, and many of the world’s biggest technological household names were small fledgling startups not long ago. 

Even now, every few days, we hear of another startup like Spotify, Airbnb, or something or the other becoming a unicorn, going public, and, before we know it, becoming an integral part of our everyday life. It replaced companies and brands that had been a part of our lives for as long as we can remember. If not all, most of them can do so because of the tremendous advances that we have seen in technology in the past few years. 

AI & ML today offer solutions to many problems that we considered insurmountable in the past. They improve existing solutions in cheaper, quicker, and more efficient ways. While it can be challenging to manage expectations from Artificial Intelligence and Machine Learning efforts even today, the results can be worthwhile if done right.

7 ways to leverage AI and ML

Here are some ways in which startups are uniquely positioned to leverage their flexibility to make AI and ML efforts successful.

1. Access a lot of data

It is the era of Big Data, and you can’t do AI or ML today without data. Depending on your product’s scale, you need to ensure that you have access to enough data on the problems you are trying to solve. By being small, tech-savvy, and operating at scale, startups use technologies to digitize whatever material they can, automate whatever tasks they can, and often capture large amounts of data for training models and building AI products.

Machine Learning projects often run into many cold-start problems, not having data resources and whatnot. Manual effort and one-time solutions can get you out of quick pickles. Still, to eventually come up with a scalable and long-term solution, you will need access to large amounts of data. But on not only what works but also what doesn’t by tracking large datasets over long periods. Because startups that realize this consider data as an asset that they actively try to build.

Results of a recent survey of ML Practitioners on their current data requirements (Source)

2. Find the Right Fit

According to VentureBeat, 87% of AI projects don’t make it into production.

Companies often need time to figure out their product/service concretely, what features they can realistically offer, how it distinguishes themselves in the market, and many things that can only happen through trial and error. Large corporations come under a lot of scrutiny by both the public and investors. 

At the same time, startups generally can spend a lot more time exploring multiple options and finding the right fit for themselves. One of the best examples that come to mind is Slack Technologies. 

Currently one of the world’s most popular chat and productivity tools – used by 77% of the Fortune 100, Slack was initially a gaming company called TinySpeck. After shutting down their MMORPG Game “Glitch” in 2012, they went on to port the game’s “familiar” pet rock into Slackbot, a friendly bot that helps you find things around gives you notifications, and keeps you informed about the workspace. Slack was launched in 2014, got listed on NYSE in 2019, and was acquired by Salesforce for $27.7 billion in cash and stock.

Related reading: What Does AIOps Mean for Startups?

3. Minimal Bureaucracy

Compared to large enterprises, startups are more flexible and dynamic in setting up the business and cultural procedures, ensuring that they set processes right with a long-term vision right from the get-go. People wear many hats at startups, but there is also a lot more active direct communication and understanding between the business teams, product teams, data science teams, and domain experts.

Communication in startups is more comfortable, and analyzing the problem from multiple angles and understanding a lot of different perspectives is simpler. Because of the closer working relationship among team members, it is easier for them to get access to any data point that they may need, and get resources to test out different technologies and operation processes until the right fit for the team(s) has been discovered. This system is also present in more prominent companies, but in startups, the system is cheaper, quicker, and easier to manage.

Related reading: What is Hybrid Intelligence and Why is it the Future of AI

4. Building a Knowledge Base

While large corporations often handle an entire end-to-end solution, startups generally spend a lot more time carving out a niche and pursuing excellence first in that small niche. 

Startups have smaller, more connected teams that are all focused on solving that particular niche problem quickly. Storing information about the difficulties faced, and sharing lessons learned is more straightforward. 

Startups can afford to be flexible, make mistakes, fail fast, and work out solutions to what works or will eventually. This builds a domain-specific knowledge base about the problem that serves a critical role in building AI-based products.

KiwiTech helps resolve AI challenges for startups

By assisting startups to build loyal relationships. Communicating with customers in their familiar environment allows your company to feel that they are heard, supported, and appreciated. Such clients will, in turn, understand the flexibility of the system and its ability to help them to achieve the desired result, whether it is a conversation with a chatbot or a virtual agent who joined in the conversation right in time.

We are constantly improving interactive applications and solutions. We are working on the perfection of their use cases and paradigms, which ultimately will lead to increased productivity for startups and a more personalized and accessible experience for end customers.

At KiwiTech, we have helped several startups get the right AI implementation in place for their business. Speak to one of our AI consultants today.

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