AI Data Annotation The Next Frontier in Revolutionizing Core Business Processes
Without trying to sound too alarmist, technology adoption will be one of the deciding factors for the survival of businesses in the coming times. Covid-19 and the accompanying disruption have highlighted the importance of future-proofing businesses with the adoption of technology and digital strategies. According to a Gartner poll, AI came to the rescue of businesses during the pandemic. It states that “despite the global impact of COVID-19, 47% of artificial intelligence (AI) investments were unchanged since the start of the pandemic and 30% of organizations actually planned to increase such investments.”
Machine learning applications powered by AI data labeling guided businesses to adapt to the continuous market disruptions. While some companies could achieve resilience by transforming according to the given speed of technological evolution, those who couldn’t adapt to the ‘new normal’ had to put down the shutters permanently. The adoption of next-gen technologies such as robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), etc., thus, kept the lights ON for the organizations.
AI and ML have the potential to redefine business modules across industries. But a question worth contemplating here is what empowers these machines to make decisions? The answer is the data labeling process. Just like humans act and learn from experience, machines learn from data to make decisions, identify patterns, and act in favor. Supervised training is required for machines to evolve and grow, thus the need for data labeling for machine learning is worth understanding.
Once a subject of imagination and the main plot of science fiction movies for ages, AI and ML is now a marvelous reality. From our window, we see AI and ML making their way into the organizations and gradually becoming the next frontier in revolutionizing core business processes including customer experience. These new-age technologies have immense potential in enhancing the bottom-line efficiency of the company and generating greater revenue.
Higher intelligence demonstrated by the machines has led to applications across a range of industries and verticals including banking, finance, insurance, eCommerce, retail, healthcare, life sciences, agriculture, and so on. The strategic combination of AI and ML in the form of computer vision models, conversational interfaces, etc., is impacting fields as diverse as medical diagnosis, human communications, autonomous vehicles, fraud detection, and much more. Let’s take a closer look at some of the use cases highlighting how ML drives value to businesses:
Businesses can save significant amounts of time and costs spent in assessing heaps of incoming paper documents by automating their data extraction process. They can augment traditional processes including manual data entry, data scanning via optical character recognition technology, data collection, etc., that are tedious and error-prone. Investing in computer vision-based models that are customized to automate the data extraction process is, thus, a smart way out!
Customers are embracing the convenient and quick in-store cashier checkout experiences. It also reduces transaction times and operational costs for retail businesses.
