Find out what AI and machine learning can do for your business and how chatbots can save you time and money on the customer services side.

AI and Machine Learning

What do self-driving cars, Amazon Alexa, and Netflix’s movie recommendations all have in common? They each use a form of artificial intelligence (AI) to enhance their decision-making and provide a superior customer experience. As the fourth industrial revolution becomes more and more central to 21st-century living, companies are finding innovative ways to use AI and machine learning to improve the quality of our everyday lives.

AI comes in many different forms; from simple chatbots that can relieve the pressure on overworked customer support staff to global big data projects with life-and-death consequences.

What Is Machine Learning?

Machine learning is a subset of artificial intelligence. At its core, machine learning is automated data analysis where the systems that you have put in place use the data that is being gathered and processed to identify patterns and trends and make improvements without the need for human intervention.

A good example of machine learning is natural language processing, where chatbots learn about tone, context, and meaning through interaction with the human voice.

Chatbots

An AI chatbot is a computer program that simulates a regular human conversation online, but in fact, is between a human and a program. For example, Google’s Siri and Amazon’s Alexa are sophisticated chatbots. They’re easy to build, useful, and helpful tools for superior customer support. Most people know they’re not talking to a human, but they’re willing to interact with a chatbot if it’s efficient, friendly, and charming.

How Chatbots Help the Customer Support Effort

Chatbots allow companies to interact with their customers in a casual, personalized manner without the expense of hiring extra staff to fulfill those roles. By handling most of the simple, everyday queries that customers may have, a chatbot saves time and money for a company, freeing up staff members to tackle more complex issues that require human intervention.

AI Data Applications

Most AI is deployed in those areas of a business that customers never see. AI data applications operate in the background, gathering reams of data in real time and offering solutions that feel seamless and natural, but are actually driven by extremely complex, sophisticated rule engines. For example, Walmart uses SAP’s HANA to reduce costs and increase efficiency across its entire supply chain, while General Electric uses Predix to monitor and predict when critical equipment might fail.

Companies can use the technology in many different ways, big and small; however, the only constraint is the knowledge and imagination of business owners who may be unaware of what AI and machine learning could do for their businesses.

Predicting Trends and Identify Patterns

One of AI’s greatest strengths is its ability to identify patterns in large amounts of data. Something that would take humans years to do, AI can do in seconds. 

 This is particularly useful in a number of ways:

• Detecting fraud by spotting transactions that don’t fit a pattern and can be flagged for further investigation
• Identifying buying patterns in different branches of a store so that management can respond appropriately with a proper stock allocation
• Recommending further purchases for consumers, based on their previous spending behavior

Businesses around the globe are at a threshold moment in their adoption of this kind of intelligent software and AI learning. The proper use of AI and machine learning can provide an overwhelming advantage to your business if you understand what they can offer. But no organization can be expected to understand it all; that’s why it’s strongly recommended to partner with technology specialists, like Laminar, who can pinpoint areas of need in your business and recommend effective AI solutions.

Learn how AWS continues to innovate by leveraging process automation, AI, ML, and IoT to build additional functionality to help your business.

“When you have a period of discontinuity like a pandemic, companies take a step back and they rethink what they’re doing,” said incoming AWS CEO Andy Jassy at the AWS re:Invent 2020 in December 2020.

Amazon Web Services (AWS) continues to innovate and reinvent itself by leveraging process automation, AI, ML, and IoT to build additional functionality. Here are just a few of the ways that businesses are using AWS robotic process automation for better data analysis and business insights.

• Amazon Connect Services
  AI can transcribe a phone call in real time and perform sentiment analysis, then describe mitigation steps or automatically escalate calls.
• Amazon ComprehendUsing Natural Language Processing (NLP) and machine learning, Amazon Comprehend finds relationships in text. Its data sentiment analysis tool analyzes text for sentiment, language, syntax, and key phrases.
• Amazon Outposts Edge
  Amazon Outposts is shifting workloads to the edge with an on-premises box. Amazon Outposts architecture extends the AWS infrastructure, services, APIs, and tools to virtually any data center, colocation space, or on-premises facility for a consistent hybrid experience.
• AWS CloudFormation Modules
  With AWS CloudFormation, you can define your infrastructure and apps using modules. These modules are reusable building blocks that encapsulate resources and configurations that can be applied across an organization. Many of the modules are being built and available through the open-source developer community.
• AWS Lambda
  AWS Lambda allows organizations to run code without provisioning or managing servers. You can run code on nearly any type of backend service or application without administration. You upload the code and AWS takes care of everything else to run (and scale) your code with high availability. Only pay for computer time. When the code isn’t running, there’s no charge.
• AWS Batch
  AWS Batch allows fully managed batch processing at any scale. You can easily run hundreds of thousands of batch computing jobs simultaneously. AWS process automation dynamically provisions the resources needed based on volume and requirements of the batch jobs without human intervention.
• AWS Elastic Load Balancing
  AWS Elastic Load Balancing automatically distributes incoming traffic across multiple Amazon instances. This improves your fault tolerance in applications and automatically reroutes traffic to healthy instances while restoring unhealthy instances. You can balance loads across single or multiple availability zones to improve app performance.
• Amazon IoT Events
  As a fully managed service, Amazon IoT Events can connect a nearly unlimited number of Internet of Things (IoT) devices. AWS process automation can detect and respond to sensor and app events in real time. It avoids having to build expensive custom apps to trigger additional events.