Over the last half century, rapid innovation in technology has helped to transform many business practices around the world. While in the past data had to be collected and processed slowly by hand, AI can process millions of gigabytes worth of data at the speed of light. The ability of machine learning to sort through data quickly has allowed many industries to simplify tasks and reduce their overall operational costs. And with the pressure to produce and distribute products at an all-time high, having a more effective and efficient operational process is essential for any business hoping to stay afloat.
Enterprise cognitive computing (ECC), or the use of artificial intelligence to streamline business operation is slowly but steadily becoming more popular, especially in many blue-collar industries. These industries, especially the manufacturing industry, normally take a longer time to produce revenue, as they are reliant physical labor and more prone to human error. AI can help to hasten their ROI’s by automating some of their operations, freeing workers to do higher skilled, more creative tasks. An AI powered machine can be programmed to perform repetitive tasks that normally would be time consuming if normally done solely by human beings. AI allows for companies to monitor and troubleshoot in real time, saving them money and time that might have been loss due to unforeseen human error.
Predictive Analytics can also help businesses to more efficiently manufacture their products. By programming the machine to identify patterns and behaviors in the data it collects, it can more accurately predict the amount of product is necessary to generate revenue. With predictive analytics, business no longer have to worry about the financial costs accrued when they over produce and they are much less wasteful and much more mindful with the amount of product they produce, reducing their waste.
Effectively Integrating ECC
While Artificial Intelligence and machine learning present a more efficient way to process, clean, sort, and understand data, many companies are still slow to utilize its incredible advantages or lack the fundamental capabilities to utilize it properly. In a study conducted by MIT Sloan Management Review, researchers concluded that in order to successfully integrate ECC into a business and utilize its full potential in cutting costs and generating revenue, a business must be capable of data science competence, business domain proficiency, enterprise architecture expertise, an operational IT backbone, and digital inquisitiveness.
Put simply a business must:
- Data Science Competence: In order to be competent with the data they use, a business must possess the skills to extract, collect, clean, and sort data in a way that is relevant to operational outcomes. This can be achieved through the use of highly skilled highly trained data scientists who possess the skills to program a machine to collect and process relevant data and identify important patterns and probabilities in said data
- Business Domain Proficiency: How does the data collected relate to the overall goals of the business. Ai is capable of collecting a ton of data in a flash, but most of it is irrelevant and must be processed for insights in order to be of any use to a business. With a business’s value/goal clearly outlined, it is easier to curate the data towards those goals and train the AI to look for relevant information.
- Enterprise Architecture Expertise: Businesses must uproot old outdated practices in favor of more innovative practices, policies and procedures if they want to make the best use of the data they hope to collect with AI. Implementing ECC into an organization is not a guarantee for success if nothing about a company fundamentally changes to increase its value output.
- Operational IT backbone: Companies must have the IT capabilities to properly train AI to extract value for data. This means the must have the knowhow to support ECC with valuable high-quality data. A proper IT backbone will serve to streamline the function of ECC function most effectively by helping it to effectively allocate operational procedures and tasks.
- Digital Inquisitiveness: Data alone cannot be used to make decisions. Although data can provide much needed and valuable insight into potential customers, it alone can only offer a business a prediction or a probability about customer behavior. At the end of the day, a business must have the desire to be critical with their judgment using AI and its data processing capabilities as a tool.
AI has allowed companies to automate tasks that previously relied on manpower to work. Though the future where entire industries are machine powered are a way off from being a reality, artificial intelligence can still provide much needed value and innovation to many industries. With any new form of technology there will always be questions of security and privacy that must be addressed. But the future that AI has represented for many industries still looks very bright.