If you've ever started a sentence with "Alexa…" or "Siri…", you'll know that we humans are now well used to communicating with machines through natural human language. Chatbots are underpinned by the same technology as voice interface systems like Siri, but instead of responding to spoken commands, chatbots interact with users via a written chat interface, such as Facebook Messenger or a web-based application. Like many other AI-driven technologies, chatbots have become a key technology trend. Today, businesses big and small are using chatbots to interact with their customers, drive sales, solve user problems, and more. In fact, chatbots are being used in a wide range of business functions – customer service, sales, marketing, tech support, HR – across a surprisingly diverse range of industries.

A chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Chatbots are often designed to convincingly simulate how a human would behave as a conversational partner and are used for various practical enterprise use cases including customer service, IT helpdesk, HR or information acquisition (Business Intelligence). ABC Corp uses BotCore's AI chatbot which enables organizations to build and deploy customized AI chatbots. So, let us see how Nathan's life at ABC Corp. has been impacted by chatbots. Nathan has joined as a Marketing Manager.

Sometimes HR managers and recruiters have a tough time finding the right candidate for a job. Can conversational AI save the day? Recruiting the right talent is one of the most challenging tasks for recruiters. On top of these responsibilities, recruiters have to develop a recruitment strategy that meets business goals, takes into consideration competitor analysis and employee satisfaction rates. In reality, managing this workload effectively is a hard task to do, that's why many companies turned to Artificial Intelligence for extra help. In one of their recent reports, Deloitte covered a use case of a multinational bank struggling to optimize the workload of hundreds of service desk agents, who support HR management processes.

Berlin-based recruitment chatbot startup Jobpal has closed a €2.5 million ( $2.7M) seed round of funding from InReach Ventures and Acadian Ventures. The company, which was founded back in 2016, has built a cross-platform chatbot to automate candidate support and increase efficiency around hiring by applying machine learning and natural language processing for what it dubs "talent interaction". The target customers are large enterprises with Jobpal offering the product as a managed service. For these employers the pitch is increased efficiency by being able to rapidly respond to and engage potential job applicants whenever they're reaching out for more info via an always-on channel (i.e. the chatbot) which is primed to respond to common questions. Candidates can also apply for vacancies via the Jobpal chatbot by answering a series of questions in the familiar messaging thread format.

John Sumser visited a company's website recently and was greeted by a chatbot asking if it could help him. Sumser, an analyst and editor-in-chief of HR Examiner, answered by asking for the company's address. The chatbot didn't have that information, transferred him to customer service, and he ended up leaving a voicemail that went unanswered for 10 days. For organizations looking to use recruitment chatbots, Sumser's story offers important lessons about the limitations of this emerging technology, in particular the discord that results when expectations and results don't match up. "When you install a chatbot, you deliver the expectation that you're going to have the responses to these questions in machine time, not human time," Sumser said. "If you don't set the expectations properly, what you get is damaged relationships with people."

To design tools and data pipelines for explainable B2B machine learning (ML) systems, we need to recognize not only the immediate audience of such tools and data, but also (1) their organizational context and (2) secondary audiences. Our learnings are based on building custom ML-based chatbots for recruitment. We believe that in the B2B context, "explainable" ML means not only a system that can "explain itself" through tools and data pipelines, but also enables its domain-expert users to explain it to other stakeholders.

We report on experiences with implementing conversational agents in the recruitment domain based on a machine learning (ML) system. Recruitment chatbots mediate communication between job-seekers and recruiters by exposing ML data to recruiter teams. Errors are difficult to understand, communicate, and resolve because they may span and combine UX, ML, and software issues. In an effort to improve organizational and technical transparency, we came to rely on a key contact role. Though effective for design and development, the centralization of this role poses challenges for transparency in sustained maintenance of this kind of ML-based mediating system.