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The widespread use of cloud computing, the increase of compute power, and the invention of machine learning (ML) solutions that are easy to develop and adopt are giving businesses the tools to grow faster. Machine learning can contribute to many areas of the business from research to forecasting and software development. Gartner predicts that the AI-derived business values will reach $3.9 trillion by 2022.
Machine learning is seen as an undeniable instrument for growth that can be deployed in many aspects of a business from enhancing customer experience to boosting innovation, reducing fraud, and optimizing business operations. This is an overview of how major companies have implemented machine learning to reach their goals, and how you can do the same.
Machine learning is giving organizations the tools to shift their relationship with customers to a better state. By incorporating machine learning solutions in your organization you can form a clearer understanding of the customers' behavior and suggest appropriate solutions based on their historical data or reaction to previously shown options.
Machine Learning surpasses manual responses and gives quicker and more efficient customer support. If you reach out to companies that rely on the human workforce to offer customer service, you have to wait in line for several minutes until an agent is free. Using machine learning chatbots each customer will get an answer to their query in seconds and reduce the waiting time.
Aramex, a company in the global logistics and transportation industry spanned across 604 locations in the world, could digitize and enhance its customer experience through machine learning solutions. Inawisdom, another AWS partner (Blue Orange Digital is a certified AWS partner too), helped Aramex eliminate nearly 40% of inbound calls by lowering their transit time application from 2.5 seconds to 200 milliseconds and increasing the shipment accuracy by 74%. This was done by using Amazon SageMaker and other AWS services to inspect the shipment process in real-time through data ingestion and predictive analysis.
The hardest part about optimizing business operations stands in processing large amounts of data before making decisions. Machine learning can achieve this in record time and automate processes as well as predict business outcomes. By implementing machine learning businesses can automate manual operations such as finding customers, recruiting, research, and even improving communications.
Machine learning can be used to automate several IT operations which means that unexpected problems can be solved in minimal time. This leaves time for staff to do meaningful work that can’t be automated easily (yet). In one of our case studies, we explain how we were able to help a talent analytics company to improve and automate its services.
Uiba, is a company dedicated to helping mid and large-sized organizations to complete their workforce with new staff and build high-performing teams in minimal time and with maximal productivity. We designed and built their platform with all the features that allowed companies to filter through employees based on team, role, and employee, as well as optimize talent distribution for more productivity.
Sometimes valuable team members can’t contribute to certain processes due to their lack of expertise but machine learning solutions are offering more and more ways for different team members to access machine learning solutions and improve apps, services, and products. Machine Learning produces optimal solutions for clients and saves time in the process.
Cloud-computing tools and modern data stack technologies have made it easier for companies to gather, manage, transform and interpret data which means that almost any problem can be solved much faster than before as long as there is historical data where machine learning models can be based on.
Amazon Robotics, which is used in Amazon fulfillment centers to speed up the fulfillment processes, has deployed machine learning and artificial intelligence solutions to maximize the automatization process with complete efficiency. By using Amazon SageMaker they were able to build their Intent Detection System fueled by deep learning to reduce costs, increase productivity, and scale the process further.
Machine Learning can be incorporated in multiple sectors including retail, healthcare, IoT, agriculture, finance, real estate, talents analytics, construction, energy, and e-commerce, and bring a total transformation in business operations.
At Blue Orange Digital we have applied machine learning to all these sectors with realistic results and have helped organizations scale faster. You can view a full list of our case studies and schedule a short and free 15-minute call to discover how we can help you apply machine learning or any of our other solutions to your business.