Technology has made the world a better place. One such revolution is artificial intelligence. It can be used by all b2b companies like eWorldTrade to increase their efficiency in their field. Let's take a look at how it is used to improve your business. Today, content marketing is used to make a business more trustworthy and credible.

Each week I read many customer service and customer experience articles from various resources. Here are my top five picks from last week. I have added my comment about each article and would like to hear what you think too. Or maybe you had a private word with the owner or manager. My Comment: I'm often asked about how to handle negative online reviews.

Companies are increasingly using algorithms to manage and control individuals not by force, but rather by nudging them into desirable behavior -- in other words, learning from their personalized data and altering their choices in some subtle way. Since the Cambridge Analytica Scandal in 2017, for example, it is widely known that the flood of targeted advertising and highly personalized content on Facebook may not only nudge users into buying more products, but also to coax and manipulate them into voting for particular political parties. University of Chicago economist Richard Thaler and Harvard Law School professor Cass Sunstein popularized the term "nudge" in 2008, but due to recent advances in AI and machine learning, algorithmic nudging is much more powerful than its non-algorithmic counterpart. With so much data about workers' behavioral patterns at their fingertips, companies can now develop personalized strategies for changing individuals' decisions and behaviors at large scale. These algorithms can be adjusted in real-time, making the approach even more effective.

Recently researchers developed some interest in the knowledge stored in the large pre-trained models. Petroni et al. (2019) investigated BERT (Devlin et al., 2018) and other architectures with respect to their ability of storing commonsense factual knowledge. As the stored knowledge depends heavily on the pre-training corpus, we are curious about whether one can "teach" these kinds of models further knowledge by exposing them to texts from specific domains, like customer complaints in the automotive industry. Especially for product-driven organizations as car manufacturers, customer feedback provides a precious source of information for product improvements, e.g. in terms of potential security risks identified and mentioned by customers. However, the structured use of this data is an open problem in industry, despite numerous investigations with advanced NLP methods (Choe et al., 2013; Lee et al., 2015; Akella et al., 2017; Liang et al., 2017; Joung et al., 2019). Handling this fuzzy data and satisfying the demand for detailed information extraction in an intelligent manner remains challenging. The recent developments in NLP lead us to the idea of evaluating the ability of pre-trained language models to act as a domain-specific knowledge base. We investigate if a language model, further pre-trained on customer feedback, is able to store customer opinions about products, features, and services as knowledge in model parameters.

Artificial intelligence serves a number of purposes in contact centers. It can automate routine processes, provide live chat in the form of virtual agents or chatbots to address customer queries, help deliver personalized experiences and provide predictive analytics, among other things. Each of these AI functions helps enhance both the employee and customer experience, and many CX leaders are taking a closer look at how it can help in their companies. The driving force behind this movement is the increase in contact center interaction volume as COVID-19 shows no end in sight, moving customers from physical to virtual buying patterns and requiring help from live customer service agents. CX leaders have reported many changes in their contact centers, including the need for more agents to support customer demand over both the phone and digital channels, and replacing in-person experiences with virtual ones -- driving even more traffic to the contact center.

The use of artificial intelligence (AI) in inventory management can help remove the inefficiencies in the current processes by enabling a predictive approach and reducing errors by automating operations. The current inventory management practices are laced with challenges and inefficiencies. Inventory management is mostly done manually, and thus takes a lot of time. Similarly, there is a high chance of error that can impact business operations. This can lead to customer complaints due to the gap between demand and supply which is usually caused by incorrect information input. As a result, deliveries to customers are delayed, which can lead to businesses losing out on customers and facing reputational losses.

The AIOps Catalyst team's work has resulted in a new collaborative workstream focused around the topic within TM Forum. Artificial intelligence (AI) offers huge opportunities for communications service providers (CSPs) to do things better, faster and cheaper. In fact, they have no choice but to introduce AI into operations and business processes due to growing complexity and the sheer volume of data and transactions. However, as well as delivering huge benefits, the introduction of AI also creates new challenges relating to the management of services and processes. A TM Forum Catalyst team is taking a two-pronged approach, tackling both these areas simultaneously to ensure CSPs – and their customers – reap the rewards of AI.

In my previous post, I suggested that it was possible to provide a road map that would help with the introduction of artificial intelligence, advanced analytics and machine learning into insurance companies. This post outlines the process. The first area to address is applications where the adoption of analytics will have an immediate impact on cost reduction and efficiency. The obvious point is process automation. In many insurance companies, the first projects involving advanced analytics and machine learning models are the digitisation and optimisation of processes.

Google may soon tell you which restaurants could give you food poisoning. The tech giant is working with Harvard University to develop an algorithm that analyzes Google searches to spot which restaurants might have food safety issues. Researchers say it's capable of flagging possible offenders in'near real time.' They created a machine-learning based algorithm to identify unsafe restaurants, training it to look for specific search terms and location data. The model is called FINDER, or Foodborne Illness Detector in Real Time.

These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.