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Artificial intelligence (AI) uses computer algorithms to assess situations and perform a variety of activities including perception, learning, problem solving, knowledge representation and others. With AI, computers use available data to automatically make changes that take current needs and conditions into account. A high-performing AI system armed with a sufficient amount of data is capable of responding effectively.
Enterprise AI refers to the use of this algorithmic decision-making in large-scale business operations. Enterprises are typically big organizations or corporations. Though AI can help small and mid-size businesses, "enterprise-level AI" refers to artificial intelligence in corporations.
AI changes the competitive landscape for enterprises by transforming capabilities and the scale of efficiency potential. For example, a company can use automated production systems to decrease the number of employees and increase output and efficiency — reducing the costs of labor while streamlining production. AI systems can also improve decision-making by providing the necessary data to executives.
Companies that can effectively deploy AI systems will have a competitive advantage over firms that cannot do so.
Enterprise AI is an essential component of Industry 4.0, a new revolution in the way companies operate, and the type of professionals they need to employ.
Emerging Trends in Enterprise AI
AI is a rapidly growing field. There are specific trends that give insight into how these systems could influence business in the future.
Intelligence-Driven Automation
Intelligence-driven automation is a combination of AI and robotic process automation (RPA). The goal of this type of computerization of business processes is to streamline activities as much as possible.
In this "partnership," AI collects data and uses it to find ways to increase performance. Then, it uses its findings to tell the automated process systems how to adjust. In well-designed systems, this process can happen without human intervention.
This type of machine-learning can affect processes outside of a production setting. For example, service providers can learn customers' needs and provide targeted services or marketing without involving human representatives. Financial robo-advisors, on-site e-commerce marketing, and customer service chatbots are examples of this trend.
AI can also automate elements of inventory and supply chain management, which can help with limiting waste and improving operations budgets.
The challenge for businesses is that these AI systems are complex and can be difficult to tailor to their exact needs.
As intelligence-driven automation develops, companies need to rely on a network of different AI and processing systems that use diverse data sets and algorithms.
The value of people trained in AI is increasing because companies need these specialists to ensure that their intelligence-driven automation systems are operating correctly and providing the desired benefits.
AI-as-a-Service
AI-as-a-service (AIaaS) is when a third-party provider offers artificial intelligence services to a corporation or company.
Firms can take advantage of these offerings in two ways. First, they can outsource the AI components of their operation so that they do not have to worry about setup, maintenance, or improvements.
Second, they can use AIaaS to test AI setups before investing in the robotics, embedded systems, and skilled personnel needed to design and manage everything.
With AIaaS services, a company needs to rely on a third party. Depending on the AI service provider, a corporation's competitors may have the same AI tools in place.
AIaaS requires careful coordination between the service provider and the subscriber to ensure sensitive data is not compromised, and that systems are regularly updated and maintained against both internal and external threats.
Companies will train employees working with sensitive systems how to be cyber safe. It will become essential for all workers to understand basic security best practices to work collaboratively with AIaaS and take advantage of these types of remote service providers without introducing vulnerabilities or compromising the networks that power these systems.
The Need to Manage and Develop AI
AI can streamline processes and automate some tasks that workers used to perform manually. However, AI will not ultimately end the need for employees. Trained professionals will need to design, deploy, and manage the systems.
IT departments will need to grow or adjust personnel to monitor AI systems. A company may also need different sub-departments with specialists to handle development, algorithm creation, and embedded systems. For example, because AI is a data-intensive field, companies often need a qualified database engineer to build and manage databases.
These trends in enterprise AI will increase the need for professionals with specialized skills. The Bureau of Labor Statistics (BLS) expects people with IT skills to be in high demand in the future.
The overall demand for IT professionals will increase by 11% over the next decade, while the need for developers will grow by 22%. The projected growth for all occupations in the U.S. over the next decade is only 4%. In other words, the demand for IT and AI specialists will explode over the next decade.
Students seeking to meet this demand might pursue a computer science degree with a concentration in programming to ensure modern AI applications are part of their education. Students with a computer science and programming background may also ultimately pursue advanced training and certification in graduate IT degree programs to manage organizations that will make use of AI in the future. One of the greatest benefits of an MSIS graduate degree is learning how to strategically plan and manage resources for all types of technologies.
Through all of this, companies need security specialists to protect and monitor these systems because a cybersecurity breach could take down the entire system and stop production and other operations completely.
Internal Governance Policies
AI can help with internal governance policies in large corporations. For one, it can automatically perform internal audits and collect data about regulatory compliance and adherence to company policies. These real-time audits can help companies avoid problems and, in some cases, predict them with data analysis and pattern recognition before they occur.
Because they can collect vast amounts of data, AI systems can help inform policy-making within a company. Though the ability to use data to support policy decisions is an improvement, there could be issues. For example, the algorithms used to collect and analyze data could have unintentional biases that could lead to unintended difficulties for specific groups of employees.
A company will need additional analysis from different AI systems or humans to check for such bias.
Benefits of AI in the Enterprise
AI has the potential to increase efficiency, decrease labor costs, and make it possible to monitor quality and foresee issues before they become major problems. Businesses can take advantage of these traits to make improvements to their operations.
Here is a closer look at the benefits that AI provides.
Improved Customer Service
AI can, directly and indirectly, improve customer service. Chatbots and online customer support systems can give customers instant access to assistance. They can start the support interaction immediately. The AI system can then collect the necessary data to solve their issue or connect them with a human representative.
Faster Product Development
AI can help automate production, but it can also streamline product development. Companies can implement AI to forecast demand using existing market data, and they can monitor product life cycles to predict the need for new products.
Product development specialists can decrease R&D costs using computer modeling during the design and testing processes.
Improved Monitoring
Embedded AI systems can monitor machine performance and quality during production. These systems can maximize efficiency and provide alerts when equipment needs maintenance.
With improved monitoring, a company can keep production on track and avoid major repairs that could disrupt the workflow.
Monitoring can also apply to inventory and supply chain management, retail sales, marketing effectiveness, and other business processes.
Better Quality
AI can enhance the quality of products and services. Service-oriented businesses can use customer relationship management software and customer activity data to provide personalized services and allow customers to immediately engage in support services rather than wait for a human representative.
AI can also improve quality control. A computer can offer data and analysis that a person could not get through a visual inspection of a product. This extra analysis would allow a company to improve quality and catch defects before a product reaches the market.