By Michael Lock
Can you ever truly future-proof your business? Probably not, but more and more companies today are leveraging Artificial Intelligence (AI) to help them find and manage the “unknown unknowns” in our rapidly-changing business environment. In fact, findings from Aberdeen’s recent 2017 Big Data survey demonstrate that Best-in-Class companies are more likely to explore investment in AI technologies, including predictive analytics, data lakes, natural language processing (NLP), and real-time / streaming analytics (Figure 1).
Figure 1: Top Companies Prioritize Cutting-Edge, AI Technologies
AI in Action
Despite its relative nascence as an enterprise technology, the application of AI in a business setting is seemingly limitless. As the technologies continue to refine and evolve, it’s fair to wager that the theoretical will transform into the practical, and more companies will adopt AI in more areas of their business. The following are just a few ways in which the technologies can be (or are being) used:
Supply chain planning. With the ubiquity and expediency of data in today’s global economy, supply chain networks have become more flexible in their ability to accommodate more geographically dispersed elements, forming a broader supplier network. However, that trend only exacerbates the complexity of managing the flow of parts and goods through the supply chain in an efficient manner. By using machine learning (ML) algorithms, AI can help to better anticipate changes in lead times, predict price fluctuations, and recommend certain suppliers. Ultimately, this is making organizations more proactive than reactive in managing their supply chains.
Contact center optimization. The contact center is the nerve center of businesses. It has become a customer engagement hub where companies must seamlessly manage multiple channels to deliver consistent and personalized interactions. Contact centers using AI can automate analysis of historical customer traffic across each channel (e.g., phone, live chat and email), and optimize agent forecasting activities. This would mean minimizing understaffing and the associated customer churn, and overstaffing and the associated unnecessary labor costs. Another use case is analyzing previous phone conversations with the help of ML and speech analytics to determine keywords associated with customer churn. Using this capability, AI can help contact centers notify agents when a customer uses one of these keywords and determine customer churn risk. This, in turn, alerts the agent to take the next-best action to minimize the chances of losing the customer.
Fraud detection. Efforts to thwart fraud and cybercrime, particularly within credit card companies and other major financial institutions, have been around for decades. However, with the staggering volume of transactions taking place every minute of every day, and the array of vulnerable endpoints tied to the typical individual, traditional methods of fraud detection are being augmented with AI and deep learning (DL) algorithms. At its essence, fraud detection is about identifying anomalies and unusual behaviors hidden within a mountain of routine transactions. With the use of DL algorithms, the enormity of available data can actually work in favor of organizations looking to detect fraud. (The volume of data provides a rich foundation of transactions that Go to the full article.
Source:: Business 2 Community