By Brian Moore
4m read time
Artificial intelligence (AI) is not just using statistics and mathematical modelling to draw correlations and conclusions from large sets of data. AI promises to offer enterprises “magical” attributes to solve business problems in much shorter time frames than humans can achieve. For example, retailers are using AI to comb through databases with the goal of balancing inventory levels and making recommendations for write-offs, expediting expiring inventory, or reducing inventory buffers. Large manufacturing companies are using AI to identify issues in production lines or to scan for quality defects – which improves product availability, and fewer returned goods. And in IT, CIOs are using AI to classify trouble tickets to drive the right prioritization, identify bugs in computer programs faster, and correct technology issues before they happen.With all the benefits of AI, one might think that enterprises are adopting it en masse. However, in 2019, one analyst firm found that only 14% of global CIOs have adopted these advanced technologies! Clearly, there is a lot of business value sitting on the sidelines waiting to be achieved. These include innovative products for customers, employees and other stakeholders, lifesaving medicines, and better ways of working that can help businesses weather the storm and thrive in today’s turbulent economic environment. AI is here, now, and ready to start driving value in your enterprise.
AI terminology can be confusing. To demystify AI, it can be broken down into three pillars to help organizations automate, continually improve, identify new trends and patterns, and improve the customer experience. The pillars include supervised learning, unsupervised learning and reinforcement learning.
When considering which type of AI to implement, most organizations start with supervised learning because these types of applications mostly take advantage of structured data (e.g., names, dates, credit card information, invoices, etc.) which are more plentiful and accessible within an organization. Then, as companies expand their AI capabilities, unsupervised and reinforcement learning may provide even greater dividends and even larger benefits.
While most of today’s IT resources are dedicated to running the day-to-day business, there is an opportunity for the transformative CIO to carve out a budget for innovative AI use cases. Here are just a few examples where enterprises are deploying AI to drive value practically, and in short timeframes:
To move from the “magical” promise of AI to the “practical” reality of driving business results, enterprises need an infusion of talent, data, and new applications.
As a proof point for the value that AI can bring, CIOs have an opportunity to start piloting AI within their own IT organizations. First, start by examining current IT processes and inefficiencies. Second, identify areas where AI can help improve day-to-day activities. Then create a small task force to tackle areas for improvement in short sprint cycles where value can be quickly articulated.
Where can AI fit within the IT organization? Some examples include fixing code, automating processes, monitoring for outages, and predicting upgrade issues. With cost savings, improved productivity or innovation as proof points, it becomes much easier to talk to other business leaders about how AI can drive similar value for their organizations.
Paul Barsch, Assistant Director, Ernst & Young LLP also contributed to developing this article.
AI shouldn’t be something that will eventually benefit companies in the future. Instead, the future is now – AI can make a difference in your business today. If you want to learn more about AI, there are plenty of resources available, such as Data Camp, data science training in Udemy and several free resources on EY.com**.**
This article was originally published on ey.com. For more information about how Ernst & Young LLP can help you unlock long-term value for your stakeholders and thought-provoking content for technology professionals visit ey.com/CIO.