Artificial intelligence (AI) and robotics have become the technology industry’s hottest topics with major companies and start-ups cashing-in on their bright promises. On the crest of the AI wave is the advent of conversational computing, or chatbots, as well as more complex virtual assistants that respond to voice commands on smartphones or tablets, such as Amazon’s Alexa or Apple’s Siri. However, many companies experimenting in this area have yet to get it right – a stark reminder of the risks of overhyping technologies.
What many don’t realise is that AI has already been around for some time, albeit in different guises. To understand how, it’s important to define this technology. Of course, Siri and Alexa both come under this umbrella, as do self-learning robots, but AI is so much broader than you might imagine. Put simply, it refers to the process of making computers and machines capable of human-like, intelligent behaviour. By this definition, AI has been widely used for more than 35 years – evident in a number of technologies, including advanced business rules engines, data driven adaptive algorithms and machine learning analytics.
All around us, every day
The science fiction nature of what’s portrayed in the media often obscures the real pragmatic usage that organisations are getting from AI. Despite the enormous value AI has already been able to deliver, AI remains often misunderstood and underused.
Today, AI is at the heart of some fundamental processes that affect profit and loss in meaningful ways. Royal Bank of Scotland (RBS) has been using Pega’s AI in the form of predictive analytics, Machine Learning and Decision Management to figure out the ‘Next Best Conversation’ to have with any customer in real time and at any interaction point.
And it’s not just banks. Many businesses use technology in a way you might not have recognised as AI. By deploying AI software, which includes case management, industry service cases and Next Best Action capabilities, customer service has transformed. Businesses can recognise that a customer is coming back to them via another channel, continue the dialogue and provide relevant suggestions to both staff and customers. Customers really appreciate this as they jump from a call, onto the web and back into the store to sort out their phone contract. This means fewer complaints, shorter calls, lower customer churn and increased savings, with AI helping the organisation to make better, more informed decisions when speaking to customers.
While perhaps not as exciting as ‘human androids’ of the sci-fi genre, these are real examples of AI and software robotics in action today. We’re there already. The next step is thinking about how you get AI to grow with the business and not become an assortment of technologies that lack integration and scalability. Ultimately, we should be focusing not on the technology itself, but on the business value that this technology provides.
Elevating customer experience
According to a study by Pegasystems on banking customers’ attitudes towards customer service, 68% of banks think they understand their customers extremely well, but less than half of banking consumers feel the same way. This highlights a major disconnect between banks and their customers. And it’s something which AI and machine learning is able to address.
To understand this disconnect, we should remember that organisations were traditionally built from the inside-out, with customers and customer-facing employees on the ‘receiving-end’ of technology. This meant customer service agents were toggling between as many as 30 different screens, while telling the customer “the system’s a bit slow this morning”. The best-performing agents have overcome this inside-out technology that’s supposed to support them.
The need to operationalise insight has never been greater. An ‘always-on’ central brain functions across all different lines of business, channels, systems and data, ensuring that customers get the best treatment, no matter how they interact with the organisation. This “Customer Decision Hub” sits between customers and customer-facing employees, determining Next Best Actions. This system merges data, generates insights from predictive analytics and machine learning, with governance and judgement easily injected by business experts.
This need for a connected customer experience is also being addressed by AI-powered bots which provide customers and employees with smart digital assistants from any conversational interface, using natural language in voice and text. SMS, text and social media channels can be transformed into intelligent assistants that anticipate needs, make helpful suggestions, complete complex tasks, and provide exceptional experiences.
Deep learning is a great example here: a fascinating opportunity but not yet a game-changer for a bank or insurer. These commercial organisations need AI to do the hard work of growing revenue and driving down costs. The challenge is all about transforming interactions with customers – improving loyalty by adding value, whilst increasing the number of products and services they buy. This is where Decision Management offers huge potential. Pulling together several AI technologies – predictive analytics, machine learning, and traditional business rules – decision management helps companies make decisions that are driven by data analytics, something a computer is uniquely able to provide.
The benefits of AI extend far beyond improved customer service. It can also help businesses respond to changing circumstances in consumer demand, and manage resources intelligently. When, for example, an unexpected cold snap in a specific area suddenly creates increased demand for snow chains, an intelligent AI process can ensure the right action is taken by prioritising and diverting resources to where they need to be.
AI, deep learning and whatever else may come afterwards are all simply tools, and we need to work out how to best use those tools to improve our lives. For enterprise software, machine learning will always have to be governed by human judgement. Whether due to ethics or regulation, the need to govern and judge will keep humans in a position of control, essentially ‘steering’ the AI to address our business needs. Many organisations have already begun to benefit from AI in real and measurable ways. AI should be seen as something that you steer and optimise to deliver the business goals that have been set. And it doesn’t need to have passed the Turing test to add business value.
Don Schuerman, Chief Technology Officer, Pegasystems
Image Credit: PHOTOCREO Michal Bednarek / Shutterstock