Apple’s Siri, Microsoft’s Cortana, Amazon’s Alexa and so many others are a testament to how fast technology evolved. A few years back, human-computer interaction was a concept that sounded exciting. But in the present, it’s nothing special and is part of our everyday lives.
Machine learning and AI have already reached the mainstream, and is being used innovatively in many different ways. Rapid technological advancements are accelerating its growth to new dimensions. Still, there are challenges for a business to leverage both these technologies.
About such challenges…
One of those challenges is associated with a business’ approach to race ahead of competition by using AI and machine learning. In today’s world and with these two technologies at hand, businesses will have to either possess the best data that efficiently solves a specific problem or play a different game from competitors by providing a unique solution that can be anything from a home-brewed data set to train machine learning models to a combination of analytics and mining to solve big technical problems.
Another challenge is about data – the most vital component for startups using machine learning technology. Data feeds and trains machine learning models. Big companies generally have huge data sets that span across various industries. In addition, the open source community are putting in a lot of effort to make complex machine learning algorithms accessible to everyone. This makes it nigh impossible for startups to come up with a competitive edge with algorithm development.
And then there are the tech giants. Corporates like Google, Microsoft, Amazon, Facebook etc. have all open sourced various components of their machine learning technologies for the sake of innovation in the space while establishing themselves as leaders. This makes it even more competitive for startups aiming to make a fortune by investing in this sector.
How and where they can find their place?
Data aggregation has prospects. For instance, Waze – a community-based GPS and navigation app. Waze began its journey by developing a proprietary data set through its product. As the company scales, the data will be utilized to strengthen its competitive advantage. The data set’s quality and the number of users also contribute to this by improving the predictive power of the algorithm, and subsequently the user experience as well. With each driver contributing to the app’s performance, the app provides better experience to other users.
Waze was eventually acquired by Google for more than $1 billion, because it was sparking a rivalry with Google Maps. The user-generated data set of Waze could’ve been one of the reasons.
Startups planning to invest in data aggregation should make sure that there aren’t others in the space with access to the data assets that they are planning to use. If there are other companies that own this data set, they may potentially become a competitor. Investors should also check whether this data set can be replicated by potential competitors. If it’s not easy to replicate it, then the coast is clear for them.
Like the name suggests, this involves establishing the business in uncharted territory, thus becoming a pioneer. Startups capable of developing new AI or machine learning models to provide better services or create more efficient applications are very likely to become successful. All they need to do is span their services across multiple sectors, and solve specific problems creatively and innovatively.
A good example is Kiva Systems. They built robots for the e-commerce industry to automate warehouses. For this they had to combine robotics, software development, and warehouse logistics after learning the problems and subsequently solving them with robots. The company established itself as a pioneer though their technology wasn’t unique. Competitors will eventually be able to catch up or surpass them. However, doing it first gave them an edge which enabled the business to evolve quickly and become the market leader.
After Amazon acquired Kiva Systems a few years ago, the company’s technology was implemented in Amazon’s warehouses.
According to Deutsche Bank, this move from Amazon cut their warehouse operations costs by 20%.
These are just a few ways how startups can establish themselves in the AI/ML world. Machine learning and AI are being leveraged for various purposes by startups and established enterprises alike, including hardware, medicine and diagnosis, self-driving automobiles, robotics, customer engagement and behavior analytics etc. Google believes that these two technologies will disrupt the industry in the near future, and that makes them worthy for investment. The tech giant has also launched the Google Developer Launchpad Studio to provide tailored support and services to AI/ML startups.
Both consumers and businesses demand these technologies. And such a demand indicates a bright future. But the time to adopt AI and machine learning is now.