The Crux of Artificial Intelligence’s Enterprise Utility: Timely Analytics

The assortment of use cases Artificial Intelligence supports for enterprise users is constantly burgeoning. Applications of neural networks, symbolic reasoning, and prescriptive analytics are appearing in everything from internal systems for data management to customer-facing ones that generate revenues.

These cognitive computing techniques are instrumental for increasing the speed and scale at which organizations accomplish mission critical objectives, such as devising new treatments to help patients in healthcare settings, for example. When properly applied, they become a form of competitive advantage that gives organizations superior production capabilities than others in the marketplace have.

The centerpiece of nearly all these varying deployments cognitive computing supports is analytics, which underpins the full spectrum of organizational needs, such as “a use case around a customer experience, or a use case around inventory optimization, or use cases around adoption of mobile banking,” reflected Tapan Patel, SAS Senior Manager of AI and Cloud. “In all those use cases…that’s where analytics, and data, and correctly identifying the business problems make a difference.”

The real-time predictive capabilities of machine learning excel in identifying business problems in areas of the Internet of Things (IoT) for preventive maintenance. In other deployments involving Natural Language Processing, statistical and non-statistical AI techniques can prevent problems by correctly addressing customer needs via chatbots. 

A detailed look at these two examples indicates AI is well on its way to becoming inextricably bound to some of the most universal, and lucrative, enterprise processes for quite some time to come.

The Chatbot Phenomenon

Although chatbots have been deployed in various business settings for some time, recent advancements in this technology have made them much more effectual than they previously were. The best of these virtual agents are endowed with NLP capabilities that let them transcend the relatively simple template-based approach that characterized many of these deployments before. Consequently, their conversational responses are more diverse than ever and applicable to a wider range of use cases. Chatbots are an excellent example of AI’s impact in business settings because they’re horizontally applicable—for internal and external purposes, the latter of which affects customer service in general.

Many of these gains are attributed to advancements in machine learning model-driven NLP. Techniques like BERT and transformers are helpful for accelerating the training period for the underlying machine learning models at the base of this statistical variety of NLP. “One of the core strengths of AI is: how can an algorithm respond to a customer need in real-time using Natural Language Processing, and start a conversation, a human-like chat, for example?” Patel said. NLP driven by machine learning models is doing just that by refining areas of Natural Language Understanding and conversational AI. To that end, “it represents the strength AI brings to the table,” Patel remarked. “Customer engagements have improved significantly over the last two to three years. That’s an example of how customer experience in kind of a digital transformation project has gone to the next level with Natural Language Processing and AI-based technologies.”

Anomaly Detection

Another convincing example of AI’s utility for organizations across industries is its propensity for anomaly detection—which oftentimes utilizes machine learning’s advanced pattern recognition at enormous scale. Applications ranging from cyber security and network security to preventative maintenance in the IoT rely on advanced machine learning techniques to proactively identify aberrational behavior and issue alerts about them. Some of the IoT use cases in manufacturing—which includes the automotive sector, too—epitomize this capacity of statistical AI. Whether applied to sensors in cars or trucks, or to equipment assets like planes and trains, machine learning algorithms can analyze this data in real-time and effectively predict when maintenance (or repairs) are needed to keep equipment properly functioning.

Real-Time Analytics

According to Patel, there are both short term and long term benefits of this approach. For the former, “if a vehicle is showing anomalous behavior in the field, this will help [organizations] take targeted intervention,” Patel revealed. “They’ll want to address those anomalies, investigate why a certain part is behaving differently than others, so they can prevent downtime.” Moreover, the ability to aggregate this real-time data and analyze it for historic trends in relation to low latent ones creates long term benefits related to the manufacturing process itself. “If a part is breaking consistently and frequently, at frequent intervals, [companies] can take those insights and share it with the parts manufacturer, so they can proactively modify the part and avoid problems in the future,” Patel divulged. In either case, the elimination of downtime translates into thousands, if not millions, of dollars in savings.

Here To Stay

The common theme in both of these use cases—chatbots fortified by NLP and anomaly detection in the IoT—is the enterprise worth of real-time analytics. The ongoing applicability of this aspect of AI will continue to expand, creating even more advantages for astute firms choosing to avail themselves of it.       

Featured Image: NeedPix


Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.

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About Jelani Harper

Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.

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