Enterprise cognitive computing (ECC), or artificial intelligence to streamline business operations, is slowly but steadily becoming more popular, especially in many blue-collar industries. These industries, especially manufacturing, usually take longer to produce revenue, as they rely on physical labor and are more prone to human error. AI can help to hasten their ROIs by automating some of their operations, freeing workers to do higher-skilled, more creative tasks. For example, an AI-powered machine can be programmed to perform repetitive tasks that would typically be time-consuming if done solely by human beings. In addition, AI allows companies to monitor and troubleshoot in real-time, saving them money and time that might have been lost due to unforeseen human error.
Predictive Analytics can also help businesses to manufacture their products more efficiently. Programming the machine to identify patterns and behaviors in the data it collects can more accurately predict the amount of product necessary to generate revenue. With predictive analytics, businesses no longer worry about the financial costs accrued when they overproduce. As a result, they are much less wasteful and much more mindful of 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 use it properly. In a study conducted by MIT Sloan Management Review, researchers concluded that to successfully integrate ECC into a business and utilize its full potential in cutting costs and generating revenue, a company 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 acquire:
- Data Science Competence: A company 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 meaningful patterns and probabilities in said data
- Business Domain Proficiency: How does the data collected relate to the overall goals of the business. AI can collect a ton of data in a flash, but most of it is irrelevant and must be processed for insights 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 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 train AI to extract value for data properly. This means they must have the know-how to support ECC with valuable, high-quality data. In addition, a proper IT backbone will serve to streamline the function of ECC function most effectively by helping it 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. Therefore, a company must have the desire to be critical with its 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 is a way off from being a reality, artificial intelligence can still provide much-needed value and innovation to many sectors. Of course, 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.