Research Technology

6 AI Technologies Your Enterprise Needs

Many companies do not recognize the scope for AI solutions in their organizations. However, AI can be leveraged for a number of advantages in enterprises such as increased operational efficiency, prediction of customer needs, and data-driven insights that power strategic decision making. While AI is not a magic wand that can solve all problems, it is extremely powerful technology that can drive value and support strategic decision-making when leveraged correctly.

Familiarizing yourself with the AI technologies available will help you select the option that best suits your requirements. These are the AI technologies that can be harnessed by enterprises.

1. Natural Language Processing (NLP)

Natural Language Processing as its name suggests is concerned with understanding written or spoken human words and speech. It is useful in response systems and customer service to understand and respond to customer queries. It can significantly improve response time and customer engagement. Although it is primarily used in service and support transactions, it also has huge potential for internal use to improve company processes and efficiency.

2. Natural Language Generation (NLG)

Natural Language Generation aims at taking what a system knows and transforming it into natural language that is easy for humans to understand. Siri uses this technology for limited responses. It looks for the insights hidden in data and coveys them in natural language. This enables us to have conversations with systems that have access to a great deal of data. This technology turns all sorts of data into text that is easy for humans to read and understand. This is done extremely fast at a speed of multiple pages per second.

3. Virtual Agents and Bots

Chatbots, virtual agents and virtual assistants are being implemented to help customers and function as online personal assistants. The predictive abilities of AI allow pre-emptive action to be taken as customer requirements can be anticipated based on the individual’s history and preferences. They can have an intelligent conversation with a customer instead of the customer having to navigate an unwieldy system online or via the phone. This results in enhanced customer experience and reduced turnaround time. Another advantage is that unlike a call center, chatbots and virtual agents can function 24/7 so customers do not have to wait to resolve their queries. There is also potential for them to be used in the medical and industrial fields as well as internal training and support.

4. Machine Learning

Machine learning involves building AI-powered applications that can function without human intervention after an initial training period with large data sets. Siri, spam filters and self-driving cars are just a few examples of machine learning at work. Many businesses are looking to leverage big data to drive smarter strategic decision-making and predictions, and this is exactly what machine learning can do. It is also useful for customer segmentation and recognizing text and objects in images and video. Machine learning can help customers’ businesses to scale which is a challenge for growing companies.

5. Deep Learning Platforms

Deep learning is a branch of machine learning that makes use of neural networks with several layers that learn in iterative steps. It learns from examples like a human being. It learns how to perform classification tasks straight from text, sounds, or images, and training requires large data sets. Deep learning applications achieve very high levels of accuracy surpassing humans. Deep learning works well with massive data sets, so big data and deep learning are often intertwined in AI applications. A few applications of deep learning include automatic speech recognition, image recognition, and NLP. Self-driving cars are the most high-profile example of deep learning applications.

6. Workflow Automation

Workflow automation which includes Robotic Process Automation (RPA) can help employees increase their productivity. RPA in particular can be of use for fields of work that are data driven, rule-based, high volume and repetitive. It is mainly being adopted by medium and large enterprises due to the critical mass and scale of benefits advantages that it provides. The productivity payoff from workflow automation is a significant competitive advantage for enterprises and allows employees to focus on more challenging tasks instead of being tied up with mundane repetitive work. It also improves operational efficiency by streamlining processes.

AI solutions are being implemented across a wide range of industries such as banking, financial services, insurance, mutual funds and manufacturing, to name just a few. Now that you have some familiarity with the AI technologies that are available, you can examine what value AI can deliver for your enterprise – from unlocking new opportunities to gaining a competitive edge, and take steps to implement an AI solution.

Our next-gen, AI-powered content intelligence platform RAPFlow in tandem with our RPA solution RAPBot, provides end-to-end workflow automation capabilities that can be deployed in just a week. Please book a demo to explore how RAPFlow and RAPBot can transform your business.

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