Researchers and futurists have been talking for decades about the day when intelligent software agents will act as personal assistants, tutors, and advisers. Apple produced its famous Knowledge Navigator video in 1987. I seem to remember attending an MIT Media Lab event in the 1990s about software agents, where the moderator appeared as a butler, in a bowler hat. With the advent of generative AI, that gauzy vision of software as aide-de-camp has suddenly come into focus. WIRED's Will Knight provided an overview this week of what's available now and what's imminent.

In this paper, we investigate the use of large language models (LLMs) like ChatGPT for document-grounded response generation in the context of information-seeking dialogues. For evaluation, we use the MultiDoc2Dial corpus of task-oriented dialogues in four social service domains previously used in the DialDoc 2022 Shared Task. Information-seeking dialogue turns are grounded in multiple documents providing relevant information. We generate dialogue completion responses by prompting a ChatGPT model, using two methods: Chat-Completion and LlamaIndex. ChatCompletion uses knowledge from ChatGPT model pretraining while LlamaIndex also extracts relevant information from documents. Observing that document-grounded response generation via LLMs cannot be adequately assessed by automatic evaluation metrics as they are significantly more verbose, we perform a human evaluation where annotators rate the output of the shared task winning system, the two Chat-GPT variants outputs, and human responses. While both ChatGPT variants are more likely to include information not present in the relevant segments, possibly including a presence of hallucinations, they are rated higher than both the shared task winning system and human responses.

Conversational AI is playing an increasing role in customer service contact centres. It can greet customers, handle routine requests in a conversational manner, and more accurately route interactions to the service agents who can best assist. But when a customer reaches out to a contact centre, they are often frustrated because they have unsuccessfully tried to solve their problem online, and they expect their request to be met with empathy and urgency. Irritation can take over if the user reaches an AI bot when they need a human conversation; or if they have to wait for a human when an AI could resolve the issue more efficiently. When seeking immediate answers and information, 36% of customers choose self-service chat or a virtual agent.

Customer service holds a prime spot in a no-see, essential online world. You decided to eat out after a hard day at work. As soon as you enter the restaurant, you find the menu hard to interpret, and the support team has no knowledge. When you finally manage the order, you see the service is slow, and there is no one to help you. You vow never to revisit the restaurant.

Nearly two years on from the initial rumblings of the pandemic and Europe's already fragile economic recovery is at further risk as a series of potential restrictions are expected to put the brakes on business growth. In this environment, cost is a top priority, but so is keeping service customers satisfied. While budgets are being squeezed, businesses must still ensure service performance is optimized. Unfortunately, for companies that have a large amount of data related to service delivery, this balance is proving complex. How can businesses optimize financial data of IT and IT services, as well as make use of it for transparent business planning?

Customer behavior has experienced a whirlwind of changes due to the ongoing pandemic. And obstacles have never been lower while customers virtually connect with agents via phone, email, and texts. However, when it comes to interacting with business, points of friction are more numerous. Customers are often forced to deal with endless waits for an email response and companies often struggle to accommodate the limited availability of service desk agents during business hours. Moreover, service desk agents have soaring pressure to do more with less.

Can common business sense be programmed into AI? Many are certainly trying to do just that. But there are decisions that often require a level of empathy -- let alone common-sense -- that may be too difficult to embed into algorithms. In addition, while AI and machine learning are the hot tickets of the moment, technologists and decision-makers need to think about whether it offers a practical solution to every problem or opportunity. Machine learning, task automation and robotics are already widely used in business.

Dr Adrian Engelbrecht, Product & Development Lead, Serviceware AI, looks at how AI is taking quality and cost optimization of enterprise services to the next level. Business and service leaders are under more pressure than ever. Nearly two years on from the initial rumblings of the pandemic and Europe's already fragile economic recovery is at further risk as a series of potential restrictions are expected to put the brakes on business growth. In this environment, cost is a top priority, but so is keeping service customers satisfied. While budgets are being squeezed, businesses must still ensure service performance is optimized.

Customer service, while likened to back-office or desk jobs, has long been outsourced to third parties (call centres) for resolving customer queries. Over the years, outsourcing customer support services has hampered organizational flexibility, brand value, and privacy. With some automation, the digital revolution shifted businesses toward adopting Interactive Voice Response (IVR) technology for handling and prioritizing high volume of calls, simplifying customer service processes, and cutting overhead expenses. Although IVR allowed companies to automate their customer support and increase professionalism, the complex routing mechanism and inflexibility resulted in customers dissatisfaction towards an organization or a brand. In today's highly connected and personalized world, customers demand instant resolution of grievances and high-quality customer service at anytime, anywhere.

Contact centers have experienced overwhelming strain since the onset of the pandemic and for many organizations this chaotic trajectory has continued. In the travel industry, for example, airlines are currently facing record-breaking call volumes and their service agents are struggling to deal with a surge of inquiries. Delta reports call wait times of two to three hours and other major U.S. airlines have call wait times as long as 8 hours and 30 minutes. Extending superior customer experiences in these types of circumstances is challenging, if not impossible, and customer service agents are equally affected. The average customer service agent remains in their job for approximately one year, according to the U.S. Bureau of Labor Statistics.