Artificial intelligence (AI) and machine learning are about more than just self-driving vehicles and robot assistants. AI and machine learning are the future of business operations.
The leading companies in every industry are already maximizing the use of these powerful technologies to make everything from their logistics to their recruitment and hiring more efficient. In the future, the companies at the bleeding edge of AI and machine learning will be the ones reaping all of the financial rewards and dominating the competition.
AI and machine learning can improve almost every aspect of your business operations.
Enterprise Cognitive Computing
Enterprise cognitive computing (ECC) is often confused with AI. But ECC is a complex blend of artificial intelligence, machine learning, neural networks, and human interactions. In a pure AI system, the computer is the one solving the problems. Algorithms have been programmed to solve problems. However, with ECC, the network is instead processing and presenting possibilities. It studies patterns and makes actionable suggestions for humans. The suggested actions are based upon an analysis and synthesis of all of the available data.
Another way to understand the difference between AI and ECC is that artificial intelligence relies solely on algorithms for the computer to solve a problem. With ECC, algorithms are used to help humans solve the problem.
It's a complex decision support system. Enterprise cognitive computing is problem-based instead of program-based.
ECC doesn't replace human business decision-makers. Instead, it makes the human decision-makers better at their jobs by providing real-time data analysis that humans could never accomplish on their own.
ECC is often used in business processes such as supply chain management, issuing buy/sell recommendations, and market expansion planning.
Operational Data Analysis
In the 21st century, the two key assets of any business are its intellectual property and its data.
However, the data is only useful to the extent that it is properly analyzed. Operational data analysis seeks to use the data that a company generates to improve existing operations. Humans are limited in the amount of data that they can process and synthesize. AI and machine learning, enhance operational data analysis by vastly increasing the speed of the data analysis as well as improving the quality of the data synthesis. Just as with enterprise cognitive computing, operational data analysis uses AI and machine learning to enhance human decision-makers and analysts instead of replacing them.
Operational data analysis can be used to generate more efficient inventory schemes, stronger contract management practices, and allows businesses to be nimbler. Businesses are no longer held captive to traditional seasonal patterns and intuition. Instead, they can use their AI and machine learning applications to look for ways to better time markets.
The IT operations division is in charge of the way the organization manages the software and hardware. While this may have once been a relatively simple task, the explosion of cloud computing and the plethora of task-specific business applications has made IT operations overwhelmingly complex.
Having an AI application that uses machine learning to assist your IT operations can save organizations tens or even hundreds of thousands of dollars. This savings comes from automating cyber-security and software maintenance tasks and flagging potential dangers faster than humans alone could. The right AI application makes it easier for all of the people working in IT operations to keep the organization’s systems up and running and at peak efficiency.
In many industries, the use of AI and machine learning can lead to almost zero downtime for IT infrastructure.
An AI application specifically designed for IT operations will also make suggestions for changes to the way the users of the software and hardware are interacting with the IT infrastructure. In a globally competitive market, these efficiencies are essential for the short-term and long-term success of every organization.
Recruiting and Hiring
Any organization, even a technology company, is only as good as its people. The strongest competitors have the best recruitment and hiring practices.
Like every other area of business operations, AI and machine learning can make recruitment more agile, efficient, and cost-effective. However, the best organizations still rely on the final judgment of seasoned professionals. AI and machine learning simply enhance the existing expertise of these professionals.
Some of the ways that AI can be used in recruitment include:
Analyzing demographics of the applicant pool
Sorting applications to different hiring managers
Automating communication with candidates
An AI application could screen out applicants that lack basic qualifying qualifications for the job. They can also send automated messages letting candidates know what stage of the process their application is at.
AI and machine learning help eliminate a lot of the repetitive time-intensive tasks that are required for any organization seeking to bring in new talent, while also making sure that the humans have the best data available when making their final decisions on who should join the organization.
Customers are no longer satisfied, waiting until the next business day to get an answer to their questions or concerns. Artificial intelligence and machine learning now enable every organization, no matter how small, to immediately give a response to its customers.
Automatic call centers can easily be created using typical AI applications. These applications can also learn when a concern needs to be escalated for a human to take action on it. The best examples of customer service allow AI to handle routine questions about hours and policies while still making sure more pressing concerns that need more nuanced answers are immediately forwarded to humans who can deal with the customer promptly.
The idea that robots are going to replace humans in the business world is a myth. The real power of AI and machine learning isn't in replacing humans. The real power is in the ability of these technologies to make humans better at their jobs by making them more efficient and delivering faster access to the best data so they can make the best decisions.