How leveraging AI and machine learning can give startups a competitive edge

While AI and machine learning are changing the way businesses operate, startups may still struggle to leverage them effectively, with challenges such as cost and a lack of talent among the chief concerns.

But despite these barriers, using these emerging technologies effectively can help startups work faster, better, and smarter. Additionally, the use of cloud computing has opened up new avenues for companies to tap into AI and machine learning more easily and affordably.

Using AI for better customer experience

Cloud computing has made AI and machine learning accessible to many startups – something that has become even more apparent during the Covid-19 pandemic.

Startups, whose employees have to work from home, could tap on these new technologies to keep operations running without a hitch, according to Dean Samuels, lead technologist at Amazon Web Services (AWS). Companies have had success in building intelligent contact centers in a matter of days instead of months, he says.

To support remote work, more companies have turned to the cloud as a solution. Cloud computing has created an opportunity for businesses to build virtual contact centers that can be scaled easily.

“With the help of AI and machine learning, things like intelligent voice recognition systems can service end customers,” says Samuels. This means repetitive customer requests can be addressed by technology, increasing productivity and enabling human staff to focus on more complicated customer service issues.

Dean Samuels, Lead Technologist at AWS / Photo credit: AWS

AI and machine learning can play a role in improving the customer experience as well. Businesses can use these technologies to process data quickly in order to generate insights and create better products for customers at a faster rate.

Thai insurtech startup Sunday is a company that leverages machine learning algorithms to provide customers with a wide range of insurance products and customizable premiums.

Built on AWS, its algorithms process millions of records of historical data in order to evaluate an individual’s eligibility for insurance more accurately. This enables the company to offer those from lower-risk groups premiums that are 10% to 20% lower than those from traditional insurers.

These algorithms also allow Sunday to analyze a broader range of events and measure their financial risks more accurately, which have led to the deployment of new products. An example is its car insurance scheme where drivers pay only for the days when they drive.

AI for financial services

AI and machine learning can also help sieve through and analyze massive amounts of data at much faster rates than ever before – a trait useful for fraud detection in the financial industry.

Take digital wallet and exchange platform Coinbase, for example: Since it was founded in 2012, it has amassed over 20 million users who trade in cryptocurrencies, with transactions amounting to more than US$150 billion. The sheer magnitude can be overwhelming, but that makes it all the more important for the company to remain vigilant.

“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,” said Soups Ranjan, former director of data science at Coinbase, in a statement to AWS.

Coinbase engineers use Amazon SageMaker, a tool for building, training, and deploying machine learning models, to recognize mismatches and anomalies in user identification sources, allowing them to quickly take action against potential instances of fraud.

In addition, SageMaker was also used to develop machine learning algorithms to combat scammers. Scammers often use the same face in fake IDs when creating multiple accounts, so the solution developed by Coinbase extracts faces from uploaded IDs and compares them across other IDs to identify similar faces and detect forgery.

Integrating AI and machine learning into a business

With numerous solutions available to them, startups that are looking to use machine learning to further optimize their operations may find it daunting to determine where to start.

Founders should begin from the basics.

Photo credit: Kreatikar / Pixabay

“First and foremost, startups really need to identify the business case,” says Samuels. From there, businesses can then “work backwards” to pick out the right technology that matches their business objectives and figure out if AI or machine learning is the right solution for them.

Next, startups should make changes gradually and in stages. As Samuels puts it, “Don’t boil the ocean.”

“You want to start very small and build your minimum viable product,” he suggests. This will allow a business to test and ascertain if a product or service is useful and can be adopted by the target consumer base. Companies can then make use of customer feedback to make improvements while remaining flexible to possible pivots from their original business idea.

Moving away from an original idea is often seen as a failure among startups, but AWS has a different take on it. “At AWS, we really do encourage doing a lot of experimentation,” says Samuels. Since it helps create better products and services, it is a path that AWS customers are encouraged to take.

At the end of the day, in order to leverage AI and machine learning effectively, startups need to keep an open mind and be willing to explore new solutions to address business needs.

Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform, offering over 175 fully featured services from data centers globally.

Learn more about leveraging AI and machine learning on AWS’ platform with the AWS Learning on Demand webinar series for startups, which will also explore topics such as blockchain and scaling for growth.

This content was produced by Tech in Asia Studios, which connects brands with Asia’s tech community. Learn more about partnering with Tech in Asia Studios.