How to Think of AI vs. Machine Learning
AI tools are best used for process automation and can be used by agencies to routinize processes to eliminate human error. Agencies are increasingly having to deal with more data and larger workloads, often on a flat or tighter budget. AI can help tremendously by automating processes within applications.
Often, when federal IT leaders and workers hear about automation, it can be perceived as a threat. However, AI tools are designed not to replace humans but instead augment their capabilities and allow human workers to focus on higher-value tasks.
Mission leaders have been tasked with dealing with ever-growing workloads. Unless they want their employees working 24/7, they need to embrace automation at some level.
Automation comes in different flavors and can be used in a variety of settings. For example, AI tools can be used in data centers to automate IT processes across computing, network and storage layers in physical and virtual environments, as Cisco notes on its website. Automation can also be used in productivity tools, as Microsoft has done with its Office suite and as Google has done with its productivity apps. AI tools can also be used to automate cybersecurity alerts and processes.
Federal IT leaders should think of AI technologies not merely as tools that solve problems but tools that can used to automate how their apps and the workloads within those apps are run.
Turning to machine learning, agencies should think of this as a tool that can think in ways a human cannot. ML is designed to read data and sift through trends to catch things humans cannot think of.
Machine learning plays a major role in predictive analytics. The biggest ways ML can add value is in the cybersecurity context. ML-based tools can analyze an IT environment and raise flags that would not otherwise get raised. This means not just detecting and alerting analysts about anomalies, but also predicting threats analysts do not see coming. For example, ML tools can pull from multiple data sources and threat intelligence databases to warn of impending threats.
Additionally, machine learning can be used in predictive maintenance, something that is especially valuable to the military, but that can be used in other agencies that have large amounts of equipment in the field. Based on maintenance records, real-time observations and historical rates of failures, ML tools can let an agency know when its equipment is likely to break down before it does so.
Predictive analytics can also be used in financial analytics to predict the financial health of the enterprise based on current spending patterns and future spending projections.
LEARN: Find out how to bring federal workers into the conversation around emerging technologies.
How to Go About Implementing AI and ML
There are clearly many use cases for both AI and ML tools. After determining whether it is appropriate to deploy AI or ML for a particular application or process, agencies can turn to CDW as a trusted partner to advise them on the right tool based on the agency’s mission needs.
Agencies cannot simply procure an AI tool, plug it in and expect it to work wonders right away. There is an entire process they should follow to get the most value out of an AI or ML investment.
In partnership with agency IT leaders, CDW can help assess what an agency’s strategy is, then look at its IT environment from a people, process and technology perspective to determine the appropriate tools. Those tools need to be tailored to both the IT environment and the agency’s priorities.
AI and ML toolsets may need be modified to fit within an agency’s environment. Agencies should know before they purchase an AI or ML tool what it will be used for and if it can be used successfully by IT and other staff.
Federal agencies can benefit a great deal from AI and ML. First, however, it’s useful to know the difference between the two and how they can be most effectively used.
This article is part of FedTech’s CapITal blog series. Please join the discussion on Twitter by using the #FedIT hashtag.