Click to learn more about author David Drai.

A chaotic global economy and the rush to digital transformation has forced many organizations to reduce operational expenses and cut costs during this unprecedented time.

All major industries – especially retail, manufacturing, telecommunications, and financial services – are looking for better and faster ways to drive greater operational efficiencies. As more organizations sell goods and services online and rely on apps for external and internal communications in this age of remote work, they are leveraging analytics and AI to reduce costs without sacrificing their competitive stance or key investments in innovation and talent. 

According to a new study by McKinsey Global Research, AI offers multidimensional ways to reduce operating costs by as much as 15 to 20 percent in 12 to 18 months. This tech-enabled cost reduction approach uses automation and AI to find new efficiency opportunities in areas such as capacity reallocation, spending effectiveness, and accounts receivable. 

The McKinsey study also found that the use of automation and AI can unlock additional cost savings opportunities in other areas such as reducing manual tasks, automating knowledge workers across business functions, creating higher-quality just-in-time transparency and analyses, and hastening decision making and issue resolution. 

To make the most intelligent and beneficial decisions around cost reductions during this chaotic time, many forward-thinking organizations are using AI-based monitoring and analytics tools to process very large quantities of data in real time.

This helps organizations become more efficient and reduce costs in two main ways. First, it enables them to discover many more insights – whether it is incidents or opportunities – at a much finer granularity than what is available with existing and mostly manual business intelligence (BI) solutions and traditional monitoring tools. This allows them to take much quicker actions to either mitigate incidents or capitalize on opportunities. In both scenarios, the effect saves or makes money for the organization, thus boosting efficiencies.

Second, the use of AI-based monitoring and analytics reduces the need for manual and tedious data analysis by market and monitoring teams, freeing them to tackle higher-level tasks such as interpretation of insights that lead to better and faster decision making. This increases operational efficiency while reducing costs for organizations.

Best Practices for Leveraging AI and Analytics for Operational Efficiency

There are two key best practices to follow when implementing AI-based monitoring and analytics to improve operational efficiencies. First, organizations must select scalable technologies that support their enterprises and specifically choose AI systems that can easily scale to analyze very large amounts of data.

Second, it is important to break the data silos and let AI systems analyze data from multiple sources in order to generate the most impactful insights that lead to better business and cost reduction decisions. AI can discover joint patterns of data that come from multiple facets of the organization and lead to more accurate and meaningful insights that can make all the difference in business outcomes. For example, AI can correlate the reduction in ad conversions to lower sales of a product and find other patterns that may not be immediately visible to business analysts. Human beings have a hard time performing such joint patterns, but AI-based systems are designed to do this task and, as a result, offer increasingly accurate insights as more data from multiple sources are given to them to analyze. In this capacity, AI serves as the “single source of truth,” by avoiding inefficiencies because of multiple systems showing contradicting insights.

Following these best practices will help organizations reap immediate rewards from AI-based monitoring. And, another added benefit is that different parts of the organization need to learn just one tool, rather than multiple BI and manual monitoring options, thereby increasing efficiency and faster, more effective communications.

AI Benefits Go Beyond Cost Cutting

There are other reasons to invest in AI and analytics beyond driving greater operational efficiencies – especially now that organizations are re-energized and coming back even stronger in 2021.

Digital commerce and digitization of all aspects of an organization’s operations have increased significantly since early 2020. More data is being collected in real time and the need to discover all insights from it immediately is growing significantly as well. This dependence on real-time data-driven insights was already growing rapidly and it has now accelerated dramatically in the age of digital transformation.

Forward-thinking organizations planning a more robust 2021 have already replaced some traditional BI and visualization technologies with AI-driven monitoring because AI can process all of the data, at any granularity, constantly and in real time.

They are already relying on AI to find relationships between data originating from various sources that an organization collects such as marketing, sales, application performance, IT, networks, and external sources. This gives them higher-quality insights that lead to better decision-making by the business operations and management teams – which will ultimately pave the way for a much brighter future in 2022 and well beyond.