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Improving the Effectiveness of IoT Deployments with AI and Machine Learning

January 30, 2018      

With many manufacturing organizations trying to better understand their customers’ needs — and often their customers’ customers’ needs — there is an increasingly need to collect and mine big data and convert it to useful and actionable information. And at no time has this been more important than now, considering the need for actionable information from AI when beginning IoT deployments.

As part of this process, these organizations are seeking new and innovative ways to better understand their customers’ — and their customers’ customers — wants, needs, and behaviors. Increasingly, this means the use of digital tools and digital transformation initiatives, as well as the use of customer and predictive analytics, artificial intelligence (AI), and machine learning, to better know and understand their prospects’ and customers’ needs in order to provide more effective IoT deployments, and ultimately an outstanding customer experience.

For some, the initial exposure to AI has been with such consumer products as Apple’s Siri and Amazon’s personal assistant, Alexa.

For others, this journey began with exposure to progressive retailers who have been leaders in defining and executing on key elements of omnichannel retailing and omnicommerce. These firms, which include such names as Apple, Best Buy, and Nordstrom, and others, are relentless in their efforts to better understand their customers, and have been among the leaders in developing customer-centric systems.

Analytics and digital transformation efforts are often at the center of discussions about better understanding customer intentions, and ultimately product demand.

Organizations are finding that such transformation is often less about changes in technology, and more about anticipating changes in customer expectations and responding with enhanced business models and differentiated customer service.

Understanding customers through data analysis

Understanding and anticipating customer expectations and product demand is more difficult than ever, with some manufacturing organizations and robotics providers having to store massive amounts of big data, which includes both structured and unstructured data. This data often resides in databases and data warehouses throughout these companies, sometimes in siloed, disparate systems.

IoT deployments require a strong grasp of networking and the cloud.

Analyzing this data often requires the use of AI, machine learning, and predictive analytics techniques to sift through the data to identify trends and predict customer wants, needs, and behaviors.

These tools are becoming increasingly necessary, considering McKinsey estimates that the volume of all data continues to double every three years as information from digital platforms, wireless sensors, and mobile devices are shared across systems.

Many manufacturers are finding that in order to deliver on their customers’ heightened expectations, faster and more accurate ways to predict end-user customer wants and behaviors are critical to successfully interact and engage with both prospects and customers.

Analytics, AI, and machine learning can be instrumental in meeting these goals, particularly when used as the foundation of IoT projects. These solutions can offer timely and relevant alerts and accurately predict next-best-product suggestions, which can augment customer outreach efforts and improve the overall customer experience across industries.

IoT deployments a new necessity

The use of AI and machine learning is increasing, with AI being a key component of machine learning solutions, including the use of chatbots and similar tools in call and contact centers. The algorithms used in these solutions are programmed to learn in various ways based on the data they are exposed to, and interact with. And, as more data is processed and more insights learned from the data, the machine learning process becomes more intelligent and adept at discovering patterns, enabling improved prediction capabilities.

Similar benefits can be seen throughout the retail and manufacturing sectors as well. For example, McKinsey found that over the past five years, U.S. retailer supply chain operations who have adopted data and analytics solutions have seen up to a 19% increase in operating margins.

Unfulfilled value still abounds

While there has been much value realized from data management and analytics to-date, there are ample opportunities for improvement. The estimated potential value captured from the use of data and analytics has been uneven, with the retail industry capturing approximately 30-40% of potential value from such systems, and manufacturing only capturing about 20-30% of potential value, per McKinsey.

Also, PwC estimates that almost half of all manufacturing activities might be automated through robotic process automation (RPA), which could translate into a $2 trillion reduction in global workforce costs.

And RPA is not used solely in manufacturing, as it is already used to resolve credit card disputes, process insurance claims, and reconcile financial statements, to name just a few tasks where AI and machine learning are being used.

Looking ahead, there are tremendous opportunities to increase the value derived from analytics, AI, and machine learning solutions. And the benefits of such systems can be realized across a wide variety of manufacturing organizations and robotics manufacturers and their end users, with the potential for improvements in customer satisfaction and the delivery of an outstanding customer experience being paramount.

Companies that ignore the potential of AI and machine learning capabilities to improve the efficiency and effectiveness of their IoT deployments do so at their own peril.