The term “Artificial Intelligence (AI)” is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem-solving”. Every system can be considered Artificial intelligence if it involves a program doing something that would normally only rely on the intelligence of a human being.
With AI, a computer system is able to correctly interpret external data, learn from such data, and use those learnings to achieve specific goals and tasks through flexible adaptation.
How Artificial intelligence works
Artificial Intelligence is a problem-solving technology that works by following a systematic search through a range of possible actions in order to reach some predefined goal or solution. AI systems perform intelligent searches, interpret both text and images to discover patterns in complex data, and then act on those learnings. AI’s Problem-solving methods divide into special purpose and general purpose. A special-purpose method is tailor-made for a particular problem and often exploits very specific features of the situation in which the problem is embedded. On the other hand, a general-purpose method is applicable to a wide variety of problems. One general-purpose technique used in AI is means-end analysis, a step-by-step, or incremental, reduction of the difference between the current state and the final goal.
AI for Business
It is useful for companies to look at AI through the lens of business capabilities rather than technologies. Broadly speaking, AI can support three important business needs:
- Automating business processes
- Gaining insight through data analysis
- Engaging with customers and employees.
This involves automation of digital and physical tasks, typically back-office administrative and financial activities, using robotic process automation technologies (RPA). RPA is more advanced than earlier business-process automation tools because the “robots” (that is, code on a server) act like a human inputting and consuming information from multiple IT systems. Tasks include:
- Transferring data from e-mail and call center systems into systems of record; for example, updating customer files with address changes or service additions.
- Replacing lost credit or ATM cards, reaching into multiple systems to update records, and handle customer communications.
- Reconciling failures to charge for services across billing systems by extracting information from multiple document types.
- Reading legal and contractual documents to extract provisions using natural language processing.
Most AI systems use algorithms to detect patterns in vast volumes of data and interpret their meaning. These machine-learning applications are being used to:
- Predict what a particular customer is likely to buy.
- Identify credit fraud in real-time and detect insurance claims fraud.
- Analyze warranty data to identify safety or quality problems in automobiles and other manufactured products.
- Automate personalized targeting of digital ads.
- Provide insurers with more accurate and detailed actuarial modeling.
Cognitive insights provided by machine learning differ from those available from traditional analytics in three ways: They are usually much more data-intensive and detailed, the models typically are trained on some part of the data set, and the models get better—that is, their ability to use new data to make predictions or put things into categories improves over time.
Cognitive insight applications are typically used to improve performance on jobs only machines can do—tasks such as programmatic ad buying that involve such high-speed data crunching and automation that they’ve long been beyond human ability—so they’re not generally a threat to human jobs.
This aspect of AI involves engaging employees and customers using natural language processing chatbots, intelligent agents, and machine learning. Some of the ways in which this function is expressed include;
- Intelligent agents that offer 24/7 customer service addressing a broad and growing array of issues from password requests to technical support questions—all in the customer’s natural language.
- Internal sites for answering employee questions on topics including IT, employee benefits, and HR policy.
- Product and service recommendation systems for retailers that increase personalization, engagement, and sales—typically including rich language or images.
- Health treatment recommendation systems help providers create customized care plans that take into account individual patients’ health status and previous treatments.
The Benefits AI brings to the business
Many businesses take up artificial intelligence (AI) technology to try to reduce operational costs, increase efficiency, grow revenue, and improve customer experience.
By deploying the right AI technology, your business may gain the ability to:
- Save time and money by automating and optimizing routine processes and tasks
- Increase productivity and operational efficiencies
- Make faster business decisions based on outputs from cognitive technologies
- Avoid mistakes and ‘human error’, provided that AI systems are set up properly
- Use insight to predict customer preferences and offer them a better, personalized experience
- Mine vast amount of data to generate quality leads and grow your customer base
- Increase revenue by identifying and maximizing sales opportunities
- Grow expertise by enabling analysis and offering intelligent advice and support