AI and Machine Learning for Businesses
The future we were promised may not have arrived in the form of jet packs, hover cars, and replicators (well, not yet, anyway), but in more subtle ways, the future we were promised has actually already been here for some time. After all, artificial intelligence (AI) and machine learning have been put to work by tech industry-leading companies and other forward-thinking organizations for more than a decade. Perhaps more importantly, AI and machine learning can be put to many more wide-ranging uses these days.
In fact, machine learning for businesses is more than just a contemporary gadget. Instead, it is rapidly becoming the standard among organizations that are looking to streamline and enhance processes and strategically position their businesses within their respective industries. In this post, we’ll take a look at some of the ways that machine learning has emerged as a fundamental component of contemporary digitization and automation efforts across multiple businesses and industries, as well as how businesses can turn to algorithms and other AI resources to enhance their success and security.
Enhanced Decision Making in Real-Time With AI Machine Learning
Getting the right information at the right time is extremely important for making the right decisions to strategize, execute, and manage your business on numerous levels. Unfortunately, there is only so much data a team of humans can crunch in a given period of time. Managing and extracting the information necessary to operate at a high organizational level requires intelligent technology – technology dependent on AI and machine learning.
The information that can be gleaned from big data by artificial intelligence has value beyond enhancing the real-time decision-making capabilities of your organization. This information can be used to enhance your everyday business practices and processes, as well as activity across your entire organization. Having this information can allow you to strategize and respond to fluctuations in your market, your supply chain, and a great many other business situations and circumstances—including even your IT service desk.
Machine Learning for Businesses and The Reduction of Manual Tasks
In the early days of machine learning, applications of AI were somewhat limited to
industrial automation. These days, though, predictive models and machine learning applications have a much wider range of uses. Machine learning and AI can support the automation of a variety of simple tasks. As a result, the time and resources deployed on managing routine workplace events can be leveraged to complete larger, more strategic projects.
Predictive models that can be applied to data points can help with real-time responses to shifting markets and targets, as well as other evolving and developing situations that impact your business. Machine learning and
AI can also enhance automation that frees up a business’s limited resources for tasks like research and development, as well as greater advancements in business strategy and execution at the human level.
Using AI Machine Learning to Enhance Security and the Function of the Service Desk
Finally, AI and machine learning algorithms can also be put to very effective use in the service desk and across the IT and organizational infrastructure in order to improve security and create service efficiencies. A smarter service desk can utilize AI to identify related incidents that may be indicative of a potential problem that could impact the broader organization, spot anomalies with IT assets, and generally help detect outages, problems, and critical incidents.
Furthermore, AI-powered technologies like
chatbots and suggested knowledge base solutions can enhance a business’s service management efforts, both at the help desk and across consumer- or user-facing systems. Identifying customer problems and guiding them to the right information or solutions can be performed automatically and at scale, helping provide a high level of accuracy in service — all without making customers wait longer than they have to for the resources they need.
