With artificial intelligence gaining traction in the IT industry, it was only a matter of time before the attention shifted to machine learning, the subset of AI that trains machines and technologies to learn. The concept came about from the idea that computers have the ability to learn without prior programming and complete specific tasks through pattern recognition. Thanks to low-cost yet powerful computational processing, accessible storage, and increasing data volumes, deep learning has now transcended the barriers of test laboratories and research papers to real-life applications.
But, the hype and media involving AI have made it quite hard to distinguish futuristic potential predictions from real-world enterprise use cases. Thus, to avoid getting caught up in all the hoopla surrounding technical implementation, the decision makers in tech companies have started developing a conceptual lens — one that allows them to check out the different aspects of their organization and how they can be improved through the implementation of machine learning. Take a look at five practical use cases of machine learning below.
The combination of automation and artificial intelligence has led to the introduction of IPA. or intelligent process automation. IPA encompasses a wide range of machine learning uses, from automating risk assessments to automating manual data entry. Machine learning can accommodate both simple and complex use cases and fits seamlessly into any scenario where the need for human decision is required.
However, it is bound by a particular set of boundaries, patterns, or constraints. Due to cognitive technologies such as machine vision, natural language processing, and deep learning, machines have become more adept at augmenting standard automation procedures based on rules and over time learn to execute them better while adapting to change. The majority of IPA solutions have already started using the capabilities of machine learning beyond basic rule-based automation.
The business benefits of machine learning far exceed the cost-saving aspects and involve extensive use of highly qualified employees or costly equipment, rapid decision-making and actions, product and service innovations, and rewarding outcomes. The moment machine learning takes over the rote tasks in the enterprise, it is in a position to free human workers so they can focus on service improvement and product innovation, thereby enabling the company to overcome traditional performance trade-offs and obtain great levels of efficiency and quality.
In an enterprise, sales is usually responsible for generating tons of unstructured data which is necessary for training ML algorithms. This is a good thing for organizations that save consumer data over time as it is also the area with the greatest potential for instant financial benefit from implementing ML in the company. Companies that want to secure a competitive edge are currently applying machine learning to both sales and marketing challenges so they can fulfill strategic targets.
A few of the most commonly used marketing techniques that depend on ML models include predictive lead scoring or ad placement and intelligent content. When the enterprise fully embraces machine learning, it sets the stage for rapid evolution and personalization of content in a way that meets the ever-evolving requirements of the prospective customers. Machine learning models are now being implemented in the field of customer sentiment analysis, customer churn predictions, and sales forecasting analysis. Thanks to these solutions, sales managers get alerted in advance when it comes to at-risk customers or certain deals.
Virtual digital assistants like chatbots are slowly spreading all across the customer service sector, and with good reason. Owing to the huge volume of customer interactions, the amount of data being captured and analyzed is overwhelming, and this serves as the most effective teaching material when ML algorithms need to be fine-tuned.
The agents of artificial intelligence can now recognize customer queries and suggest suitable articles for fast resolution. This frees up a lot of the human agents’ time and helps them focus more on improving the speed and efficiency of the decisions. The adoption of ML in the organization cloud has a surefire impact on customer service-related routine work.
Virtual personal assistants will improve their business considerably, compared to other AI solutions. Executives save lots of time by using virtual assistants and they can use this time to concentrate on creativity and deep thinking.
ML gives enterprises the opportunity to enhance their threat analysis as well as the way they respond to security incidents and attacks. Machine learning in the field of data security allows for increased spending in big data, analytics, and AI. Predictive analysis is great for detecting threats and infections early while behavioral analytics ensures that no anomaly in the system goes unnoticed.
ML is also ideal for improving the company’s monitoring capabilities when it comes to data logs from mobile and other devices capable of IoT. It also helps them generate a profile for differing behavioral patterns within the IoT ecosystem. In this manner, security teams that had earlier been stretched earlier now have the opportunity to detect the smallest irregularities easily.
Companies that embrace this kind of threat-aware mindset are better equipped to get a leading position in the company, disrupt the sector through innovation, and better navigate regulatory needs.
To get the maximum benefit from ML, companies must tap into the capabilities of both human intelligence and machine learning. Collaboration tools enhanced by ML can improve efficiency, hasten the discovery of new concepts, and result in better outcomes for teams that collaborate from separate areas. A few use cases in collaboration are:
- Audio intelligence, image intelligence, and video intelligence add context to the shared content, allowing customers to find necessary files easily.
- Translation of languages in real-time facilitates collaboration and communication between worldwide workgroups in the native language.
- Integration of chatbots into the team applications allows for native language features, such as polling team members for status updates and alerting them.
Machine learning: Building blocks for success
The five levels of machine learning mentioned above help both organizations and clients across a wide range of applications and projects, from predicting malfunctions to labeling images. These form the core building blocks with regards to ML use cases and are quite valuable.
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