We’ve all heard the trope that machines are taking our jobs. In reality, the digital age reduces the need for humans to do mundane, repetitive tasks; replacing many old roles with new challenges and new career opportunities. We see this played out through the rise of a new role in many digitally driven organizations – the AI trainer.
Modern enterprise software is becoming so user-friendly, that many vendors now aspire to let anyone use and build on their technology; without the need for them to have coding skills or in-depth technical knowledge. This sea-change towards accessibility and ease of use has been rightly welcomed, with advances in the democratization of data and, as a result, successful implementation within many sectors.
From experience, this trend is important because when an organization’s own (non-technical) employees believe in automation and understand how it enhances service/support/sales, they tend to get results, a lower cost of ownership and better organizational buy-in. So, if we accept that technology belongs in the hands of the domain experts and team leaders making the critical decisions, shouldn’t the same apply to implementation of any technology?
Should your customer service team, for example, have to rely on pre-built or predefined virtual agents or chatbots to do their jobs? Would a bot that was created by someone that doesn’t necessarily understand the intricacies and nuances of the business, campaign, or initiative, perform as well as one that is managed by your existing team? Would any old bot not only recognize what your customer is asking for and know when it should forward a customer to human support seamlessly?
The rise of the AI Trainer
AI trainers are the people responsible for building and maintaining conversational AI within an organization. Implementing the pre-built AI models that have been built by an external agency or solution provider, and tailoring them to a business’s particular needs. While a conversational AI solution, for example, may have been trained to correctly identify user intents and provide the right kind of responses, internal, customer service experts are required to monitor the conversation logs and make any necessary adjustments to ensure the AI is performing as expected.
The reality is that in-house AI ‘trainers’ maintain many premade industry AI modules across a range of functions and help to implement and build on automation projects across the enterprise. While vendors manage the technology, for example, a virtual agent’s language capabilities. Internal teams are required to work with external tech teams to provide new functionalities to any technology. Keeping up with customer demands and keeping your customers happy.
Many technology providers offer comprehensive training and most clients choose to develop talent from within their company to fill these roles. Selecting the best people for these jobs can be the deciding factor between good and fantastic conversational AI. So, what are the core skills your workforce needs to design their perfect digital coworker in the emerging age of artificial intelligence? Here are a few examples of roles that your team will need to successfully get your virtual agent online and performing long term:
The AI Conversationalist – is responsible for giving your new digital employee its voice and personality. They really understand your customers and can produce the right kinds of responses to accurately and empathetically address their needs. The wordsmith of the team. Crafting the virtual agent’s responses using their customer service skills and expertise and truly understand your company’s brand and values. Anticipating your customer’s needs by drawing on their own experience. Get them onboard early and they’ll relish the opportunity to build the dialog; crafting the on-point responses that customers expect from your company. Keep them on the project and they’ll scrutinize the conversation logs for ways to improve the customer experience through the virtual agent’s responses.
The AI Analyst – is the person who makes your virtual agent smarter and keeps it ticking over. They are responsible for the nuts and bolts of your virtual agent and spotting patterns in the data. They can take a mass of test and training data and decode the relationships the AI finds. Improving your model and reducing the number of wrong answers it gives. Knowing the ins and outs of the model and being comfortable with the more technical interactions – such as text-processing and synonyms – makes these AI Trainers crucial for the long term success of your project.
The AI Pioneer – bridges the gap between your virtual agent and your business. These are the added value trainers, the out-of-the-box thinkers. They are always bringing something extra to the table, with a new use case to solve and novel approaches to a problem. They’ll be the first to push for a new integration: transforming an FAQ bot into an advanced virtual agent, that’s capable of managing your customers’ transactions. As your model matures in both the volume of information it can provide, and the way it delivers it, these guys are key.
In the customer service/support space, the notion that machines, and especially AI, will replace human jobs is a misconception. Instead, technology solutions, like conversational AI, open up myriad avenues for support and service representatives. Working alongside ‘robots’ and artificial intelligence in new and exciting roles to deliver a more complete customer experience than ever before.
