How Startups can Utilize the potential of Artificial Intelligence and Machine Learning

Machine Learning and Artificial Intelligence have changed the way businesses operate. But thinking it easy way down the road is not quite the right approach. Some of the problems are the shortage of required talent and high costs.  

Yet these emerging technologies have a lot to offer to startups for fast, better, and smarter working. Moreover, the advent of cloud technology has opened up new horizons for the companies. Leveraging AI and machine learning are now more affordable and easy for startups.

AI to improve customer experience

Thanks to Cloud computing, Artificial Intelligence, and Machine Learning are now available for startups. Ever since the global pandemic started, this is becoming more and more evident. 

The lead technologist at Amazon Web Services, Dean Samuels, maintains that these technologies can be leveraged by the employees of different startups who are working from home due to covid-19. He says that companies achieved great success in building these intelligent contact centers. For them, it was not even a matter of months, but days.  To support more and more remote working, companies are looking towards cloud technology. The best plus of technology is the facility of building virtual contact centers that are so easily scalable. 

Samuels further says, “With the help of AI and machine learning, things like intelligent voice recognition systems can service end customers,”. 

So tackling and addressing repetitive customer requests is not a big deal for cloud solutions. That leads to increased efficiency and productivity and better utilization of human potential. The human workers get more time to handle more complicated customer service issues.   

PepsiCo used a robot for HR operations. The robot named Robot Vera called and interviewed candidates for sales positions. AI can significantly smooth human resources departments. For example, data-based decision making, candidate screening, and recruitment process.

Businesses can use Machine Learning and Artificial Intelligence to refine and enhance their customer experience to unimaginable extents. Technologies process their customer data more quickly and provide valuable insights into consumer behaviors. Ultimately, they can come up with better product ideas, build better products, and drastically boost revenue.

Thai Insurtech startup is one of those companies with smarter policies. They are leveraging machine learning for providing customers with a wide range of insurance products and customizable premiums.

The algorithms process millions of records of historical data to estimate the individual’s eligibility for insurance more accurately. Thus, companies can offer those from lower-risk groups premiums that are 10% to 20% lower than those from traditional insurers.  The algorithms are built on AWS. These algorithms also allow Sunday to analyze a broader range of events and measure their financial risks more accurately. It has resulted in the deployment of new products. A relevant example is its car insurance scheme when drivers pay only for the days when they drive.

To strengthen sales and customer relationships, the famous Luxury fashion brand Burberry utilizes big data and AI. They collect consumers’ data through different loyalty and reward programs. The organization uses it to provide tailored recommendations on whether customers are shopping online or in brick-and-mortar stores. Another renowned way to produce a stellar customer experience is by using innovative chatbots.

Artificial Intelligence Services for Finance 

The finance industry enjoys technologies when it is time to analyze massive amounts of data at faster rates than ever before. After the advent of artificial intelligence and machine learning, it is no more fiction but promising reality. Data filtering and sorting have become convenient and simple tasks.  It helps a lot with fraud detection in the financial industry.

Let’s take the example of Coinbase. It is a digital wallet and exchange platform. Founded in 2012, it has gathered more than 20 million users who deal with cryptocurrencies. Coinbase also enjoys transactions that exceed 150 billion US dollars.  

This shocking magnitude is downright formidable. But for the company to stay watchful, it is imperative. 

Soups Ranjan, former director of Data Science at Coinbase says, “One of the biggest risk factors that a cryptocurrency exchange must get right is fraud, and machine learning forms the lynchpin of our anti-fraud system.”

Amazon SageMaker

It is a tool that helps engineers build, train, and deploy machine learning models. Moreover, it can identify the mismatches and anomalies in user identification sources. Engineers at Coinbase use Amazon SageMaker because it lets them take action quickly against potential instances of fraud. Amazon SageMaker has also been used to develop machine learning algorithms that are capable of defeating scammers. Usually, this is how scammers work. 

They create multiple accounts and use the same face in all the fake IDs. How Coinbase manages to detect them is more interesting. It first extracts the faces from uploaded IDs. Then it compares those extracted faces across multiple IDs to catch IDs with similar faces. Finally, the forgery comes to the light. 

Integration of Artificial Intelligence and Machine Learning into Business

The question haunts a lot of startups today. Most of them are well-aware of how smooth business operations can go with the help of technologies like Machine Learning and Artificial Intelligence. They want to harness their power to optimize their business further. Numerous options are available for that. But they have no idea where to start. That is the most confusing moment for a lot of modern startups. 

Well, depending on the niche, industry, company size, and many other things, different companies can opt for different starting points. However, it is sure that starting from basics is what experts always recommend to most of the founders. 

Here is how Samuels expresses his opinion. 

“First and foremost, startups really need to identify the business case.”

It will further help to figure out if Artificial Intelligence or Machine learning services are the right solution for the business. After successful identification of the business case, companies should work backward to pick out the right technology that meets their business needs and helps them fulfill the objective. 

Hereafter, companies should avoid abrupt changes. They should try to make changes as gradually as possible, instead. The whole process should be done in stages. Samuels says, do not boil the ocean. “You want to start very small and build your minimum viable product,” he says.

Hence, the business can test and ascertain if a product or service is useful and whether the target consumer base can adopt it. At the later stages, customer feedback is always there to help the companies improve while maintaining the flexibility to stretch towards any of the possible pivots from their original business idea. 

To deviate from the original business idea is usually seen as a startup failure. AWS, however, has a unique angle. Samuels says, “At AWS, we really do encourage doing a lot of experimentation.” As the path helps create better services and products, AWS encourages the customers to take it.


Startups have to be keen and willing to explore new solutions to address business needs. That is indispensable to leverage artificial intelligence services and machine learning services

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