Although it may not resemble the future promised by decades of science fiction showcasing a robotic workforce, the age of automation has unquestionably arrived. New developments in artificial intelligence and machine learning are driving IT automation trends and influencing change management strategies across a wide range of industries. As these tools continue to transform business processes in the future, it’s worth examining the current impact of those trends and exploring how they are likely to develop in the years to come.
One of the most visible applications of artificial intelligence in business processes, robotic process automation (RPA) delivers tremendous boosts to efficiency and productivity by automating vital, but time-intensive and repetitive, tasks with automated software tools. Finance and accounting were early adopters of the technology since AI-driven RPA systems could be put to work scanning data and entering it into the appropriate forms and records. This automation not only frees up employees to focus on higher-value tasks, but it also ensures higher degrees of process consistency and data accuracy. According to ISG research, RPA is helping companies to reach new productivity heights, executing processes “five to ten times faster with an average of 37 percent fewer resources.” Those efficiency gains from automation are coming without a corresponding loss of jobs, largely because RPA allows companies to utilize their human resources far more effectively.
The amount of data generated each year is expected to keep increasing over time, which poses significant challenges for the data scientists who manage and analyze big data workloads. Improvements in AI will see more of these tasks give way to automation in the future. Gartner expects that more than 40 percent of data scientist tasks will be handled by AI by 2020. These “augmented analytics” will empower businesses to utilize data sets more effectively and cheaply, allowing them to react to changing market circumstances quicker and deliver products and services that are more closely aligned with the needs of consumers. The automation of big data analysis is also facilitating the democratization of data, allowing smaller companies to access the same powerful analytics resources that were once only available to large-scale enterprises.
Data centers continue to play an important role in IT strategy, whether in the form of private facilities, colocation data centers, or the massive hyperscale operations of cloud computing providers. Managing the power demands of these facilities is a major challenge, especially considering that they consume about three percent of the world’s available electricity. Automation programs powered by machine learning have already played a key role in driving better data center energy efficiency practices. The most notable example of this comes from Google, which handed control of the cooling systems in many of its data centers to a machine learning-driven artificial intelligence program developed by DeepMind. The results exceeded even the designers’ high expectations, reducing the cooling infrastructure’s overall energy consumption by 40 percent. Many other data centers are already experimenting with similar concepts to automate key aspects of their infrastructure to improve efficiency, reduce energy consumption, and improve reliability.
Between the expansion of digital services and Internet of Things (IoT) devices, companies are increasingly inundated with customer requests and data. According to Forrester Research, somewhere between 60 to 73 percent of gathered data is never even used, which means that decision-makers are often forced to make difficult choices about business strategy without a complete picture of what’s happening on the ground. Automated systems driven by machine learning and artificial intelligence could help to close the gap by handling routine aspects of customer service and data management. Data-driven decisions such as issuing loan approvals or determining whether refunds should be issued can easily be automated to provide a faster, more user-friendly experience. At the same time, AI can be used to support decision-making throughout an organization by analyzing and gathering insights from previously unused data.
Many IT and business process automation trends to date have proven to be rather ad hoc and sporadic, implemented in a few test cases, but not rolled out across an organization in a holistic fashion that truly leverages the potential of automation technology. Despite the potential cost savings and productivity gains, only 39 percent of companies are deploying these technologies at scale, and even then with only a few narrow use cases. That will likely change over the next few years as companies combine the potential of big data with automated customer experiences and process automation tools that maximize employee potential. Rather than implementing a few machine learning or artificial intelligence systems as part of IT automation, organizations must start considering how these systems interact to create a more coherent user experience that is appealing to customers and employees alike.
As IT automation becomes a larger part of change management, companies will become more creative in the way they apply machine learning tools and artificial intelligence to their infrastructure and services. By adapting to the latest business process automation trends and identifying where automation can benefit IT operations, they can set themselves on a path to be more competitive in the future by enhancing the value of their existing resources.