The business use of applied Artificial Intelligence (AI) has been the key technology trend of 2023. Although AI has been studied by researchers and used in industry for many years, this was the year when the general public also started understanding why it is important.
Generative AI (GenAI) has been the real game changer. It has demonstrated that if you train an AI system with a large enough language model then it can be asked more or less anything. People have been asking AI systems to summarise books, help create speeches, create custom images, and even to help speed up their coding projects.
The management consultant McKinsey said in a report on 2023 technology trends: “Models trained in machine learning can be used to solve classification, prediction, and control problems to automate activities, add or augment capabilities and offerings, and make better decisions.”
This is the important reason why tools such as Robotic Process Automation (RPA) and Machine Learning (ML) have taken off alongside the more general wave of interest in AI.
They are productivity levers.
People can answer more emails in the same amount of time. Employees can target their attention to the right place. Customer service teams can help customers guided by automated assistants. Productivity can soar using these tools.
Productivity is exciting because it affects so many outcomes. Costs can be reduced if the same number of people can do their work faster. A higher volume of work can be processed in the same amount of time. It’s also possible to imagine some new solutions that would not even be possible without these systems.
Look at the growth in the use of “next best action” systems in customer service contact centres. A customer has a problem so they call or text a company for help and an agent answers the call. The agent will be knowledgeable, but cannot possibly know everything about the product they are supporting so they will usually have some troubleshooting guidelines to follow and a Frequently-Asked-Questions database – this is filled with examples of common questions and the right answers.
With an AI system listening to the conversation it can automatically guide the agent, either to recommend the next question or to point to a document or video that will help to fix the customer problem. The customer gets an answer faster than when the agent had to search the databases manually – often placing the customer on hold listing to terrible music.
Importantly though, this example shows some other benefits of productivity. The agent can help more customers during a single shift, so they get more done in the same amount of time. The agent also feels happier and more supported because they now have a digital assistant advising them – the human can override the AI advice if their experience says it may be wrong, but in most cases the advice will be good. The agent has many of their boring admin tasks removed so they can focus on offering expert advice.
This is the holy grail for any busy executive. If you are creating a business plan and trying to get agreement from the CFO then these initiatives can demonstrate an improvement in business productivity for the company, an improvement in employee experience that should create a happier and more engaged workforce, and an improvement in service to customers. All will eventually trigger additional outcomes, such as improved customer loyalty or lower employee attrition, but the productivity boost will be immediately important in obtaining the project budget.
RPA was hyped for several years as a technology that would remove the need for any back office processes in most businesses. There was often a sense of disappointment when it was realised that someone needs to manage the automation system – so there is no simple replacement of all admin, but RPA does offer many specific benefits. From logging into systems, filling forms, extracting data from documents, to even conducting simple decision-making tasks based on predefined criteria, RPA bots can handle a broad spectrum of routine tasks.
RPA also has speed and accuracy. In contrast to human operators, these bots can process tasks at a remarkable pace, with a consistency that minimises errors. This ensures that businesses can handle large volumes of repetitive tasks without the typical inaccuracies that might arise from human fatigue or oversight.
This focus on accuracy can be seen in many real-life case studies published by experts such as IBA Group. One of the IBA case studies details how a company changed their invoice processing system to use RPA. It dramatically reduced the amount of manual cut and pasting required, but more importantly it increased accuracy – one client relationship had been ruined when they received a $35,000 invoice that should have been for $3,500.
Combine this with ML, so the AI overseeing your business is constantly watching and learning, and there is a very powerful combination of automation, analytics and insight, alongside constant improvement. Atom Bank in the UK has been managing their customer service processes with ML for the past seven years. The system learns from every encounter with a customer and problems with solutions are stored away automatically.
The message is clear for businesses across all industries – a smart combination of AI, ML, and RPA can automate processes, make life easier for your own employees, and deliver a better service to your customers. Automation not only allows for improved productivity, it creates new business opportunities.
