Across the globe, brands are leveraging Machine Learning (ML) to drive innovation and better customer experience. Nike, for example, uses ML for personalized product recommendations. Dominos ensures 10 minutes or less pizza delivery time using ML technologies. Another famous example is how BMW Group uses ML to read data from vehicle subsystems, predict vehicle parts’ performance, and proactively recommend maintenance.
Machine Learning (ML) – An overview
Machine learning is one of the most disruptive technologies that we have encountered in our generation. It has the great potential to transform businesses for the better. From being a niche technology, ML now sees increased adoption among organizations in various sectors. ML emerged as a critical priority area for technology leaders in 2020 as they aim to achieve revenue growth while reducing costs. In 2021, enterprises are exploring more matured use cases of the technology as they navigate an environment of flux. Disruptive organizations have been at the forefront of adopting this technology for process automation, customer experience, security, etc. In 2021, here are the top five industries that will adopt ML to change how they work forever.
Role of Machine Learning (ML) in Healthcare
The global pandemic has underscored the importance of investing in and optimizing our healthcare systems. ML is the most promising technology that allows healthcare providers to churn massive volumes of data and derive valuable clinical insights. ML offers tremendous progress in drug discovery, cutting down the long discovery & development pipeline and reducing costs. It can also significantly improve healthcare delivery systems and, in turn, lift the overall quality of healthcare while keeping costs under control. In the coming days, ML is also predicted to have critical applications in clinical trials.
Experts emphasize that ML will significantly impact almost all branches of healthcare, including pharma and biotech.
Role of Machine Learning (ML) in Banking & Finance
The banking sector has already seen many matured use cases of ML, especially in fraud detection and automating processes. Machine Learning use cases will be actively explored across areas such as trading, investment modelling, risk prevention and customer sentiment analysis. As digital transactions continue to grow, ML combined with predictive analytics will play a big role in helping financial institutions to improve transaction efficiencies throughout the transaction lifecycle. Banks and financial institutions will also use this technology to customize their products and offerings to stay more relevant in a competitive environment.
Role of Machine Learning (ML) in Media & Entertainment
Companies like Amazon and Netflix have recently popularized data-driven content consumption models. As the global pandemic further drives up the demand for new consumption models, firms will effectively leverage AI and ML to create value for customers and present the most relevant content to them in real time. Whether developing better recommendation engines or delivering hyper-targeted services, ML will be critical for the media and entertainment industry to address the drastically changing customer expectations. Predictive modelling will be vital in responding to customers in real time, anticipating future demand and making investments wisely.
Role of Machine Learning (ML) in Retail & E-Commerce
No other industry has better understood the need to be prepared for the unexpected. The global pandemic has disrupted the retail sector in several ways, and ML has been considered a critical enabler for the industry to address change effectively. Whether it is the traditional brick-and-mortar stores or the ecommerce companies, the sector is on a path to reinvention with technologies such as ML. From supply chain and inventory management to personalized product recommendations through chatbots, the retail and ecommerce sector looks at several ML use cases. It is also being used extensively for predicting user behaviour and analyzing the trend effectively to be better prepared. Dynamic pricing is emerging as a critical ML use case to help retailers thrive in a competitive market landscape.
Role of Machine Learning (ML) in Manufacturing & Industry 4.0
With the massive adoption of IoT devices set to further increase in the manufacturing sector, ML will be the most critical technology bridge that analyses the vast volumes of data generated. ML is the powerful building block of Industry 4.0, along with automation and data connectivity. While predictive maintenance is the most explored use case so far, manufacturers will look at more matured use cases of ML, such as real-time error detection, supply chain visibility, warehousing efficiency & cost reduction, and asset tracking. As traditional factories transform into smart factories, ML will fuel more incredible innovation and efficiency in the future.
