The automation of systems has brought about change in commerce and industry and made a difference to global economies. AI (artificial intelligence) has touched our lives, both in our jobs and in our personal capacity. From automation in the workplace to the supermarket down the street, the cars we drive, our entertainment, and communications, life is certainly not dull.
AI is vast. Although it’s been around since the 1940s, it is still relatively new in commerce and industry, where we see companies implementing AI in various forms:
- Virtual assistants in the form of chatbots.
- Computer vision, such as facial recognition.
- Robot process automation in manufacturing environments.
- Machine learning (ML) that is used across businesses to analyze current and historical data and forecast future scenarios and requirements.
AI is technology that simulates the workings of the human brain. Machine learning and deep learning are components of AI that enable it to utilize data and learn from it. AI is constantly learning, building upon its knowledge, and improving the algorithms that it learns from. A big breakthrough has been the synthesis of human language, and AI’s ability to communicate with us. It also learns from music, videos, data, and software.
Artificial intelligence is not just about robots and system automation, however. It goes deeper than that. AI and machine learning work in conjunction to achieve a constantly improving learning curve.
Table of Contents
Machine learning as a component of AI
Machine learning is based on the statistical analysis of data using algorithms. In the process of learning, ML is in a continuous cycle of learning and improving upon existing algorithms or creating new ones.
Machine learning is most effective when accessing vast volumes of data. Efficient data management is essential for forecasting and informed decision making.
Enhanced decision making
AI and ML analyze data on a much larger and faster scale than is humanly possible, giving us unprecedented insights into commercial and industrial processes. Managers make informed, data-driven decisions and implement enhanced strategies using sophisticated predictive analysis tools.
From manufacturing and the supply chain to marketing, customer experience, human resources, and cyber security, a bright future awaits businesses that are adapting their operations to include the latest AI and ML technology.
We give an overview of some AI-related enhancements that are making a difference in various business sectors.
Manufacturing
Manufacturing and sales/marketing teams collaborate to predict future needs based on consumer statistics and trends. AI software analyzes current usage patterns and customer preferences to predict future usage. It suggests design improvements and sometimes comes up with new product ideas. It identifies bottlenecks in production processes and makes suggestions for improvements.
This predictive technology affects inventory management too. It introduces higher levels of accuracy in the stock ordering and replenishment process and reduces redundancy levels. Warehouse space is costly to maintain from a personnel, equipment, and insurance/risk point of view. AI and ML reduce the need for excessive stockkeeping, thus reducing costs.
Sophisticated technology combines machine learning and sensors to predict when breakdowns are likely to happen. It preempts maintenance of machinery, resulting in cost savings and improved productivity.
Supply chain
In the supply chain, transportation of supplies is optimized through AI- and GPS-enhanced route-planning and vehicle tracking technology. Optimization of delivery routes means savings in fuel and time by planning routes around bad weather, road conditions, peak-hour traffic delays, and poor visibility. This enables accurate ETAs and a seamless delivery process.
In the fleet management arena, tracking technology includes AI-based predictive analysis that identifies potential vehicle breakdowns and produces reminders for vehicle maintenance.
Optimization of raw material procurement, planning, manufacturing, and distribution means a more efficient supply chain overall and improved customer satisfaction.
Consumer-driven decisions
Interrogation of social media data has escalated the ability to analyze consumer trends and buying patterns. The advertisements that pop up on your screen when browsing are based on predictive analysis. Customer audience segmentation is based on historical purchasing data, as well as social media posts, current trends, age groups, and geographical location.
AI and employment
In a study done by the International Monetary Fund (IMF), AI affects almost 40% of global employment, reaching a figure of 60% in the advanced economies.
AI has the ability to recognize patterns in human performance, helping HR departments manage recruitment.
On the subject of AI and employment, innovator Martin van Blerk is the founder of Caliber, an intuitive AI and web-based platform that connects job seekers with suitable positions that match their skills. The applicant tracking system (ATS) is easy for jobseekers to use and builds up an HR database of prospective employees that can be tapped into whenever the need arises.
Financial institutions and fraud detection
In the world of finance, ML algorithms are used to identify and raise alarms on irregular patterns in big data. This enables faster detection of fraud. In financial planning, ML and AI identify risks and opportunities, taking the pressure off planners and investors.
Here to stay
The costs associated with implementing AI and ML technology may be prohibitive for many companies. However, adaptation of AI tools is likely to raise the level of competitiveness in business, reduce risks, and optimize processes. There’s no doubt that AI is here to stay — the sooner companies adapt, the better.
