The impact of AI on 4 key brands: CXO panel
- 17 May, 2018 06:53
Artificial intelligence (AI) is here to stay. And while brands are in varying stages of adoption and strategy maturity, there’s one common principle: The nascent technology needs to meet customer expectations in a simple and useful manner.
This was the consensus across brand leaders during a customer experience themed panel at this week’s CeBIT on AI’s impact on the enterprise.
“Banks will no longer be disrupted by fintechs, but by customer expectations,” NAB general manager business support customer journey, Craig Swinburne, said. The real promise of AI comes in delivering the customer value proposition and helping the group to meet expectations with simplicity and utility.
“The bank or the industry that cracks this will crack it real-time. Make it simple and make it easy. Personalise it. Use the data you have, analyse it and give me a solution to a problem I didn’t even know I had. Personalise it for me. And be there when I need you,” Swinburne said.
Fellow panellists included UBank head of digital and technology, Jeremy Hubbard; BPay Group CIO, Angela Donohoe; and Volkswagen Group Australia chief customer officer, Jason Bradshaw, who all discussed the power of AI and projects on their radar.
At NAB, Swinburne is responsible for determining how to make customer experience first rate. And he’s looking at AI more and more, particularly in the area of chatbots, to achieve it.
NAB is looking at it on several fronts. “We’re looking at proof of concepts around how you team up with a third party, maybe a software provider, and use some AI to do some deep analysis on customer’s transactional behaviour on our side, and maybe their software on the other side,” he explained. “Then you can start to build a solution to a problem the customer may not even know they have.”
The bank’s biggest challenges revolve around resources and determining how much money gets spent on AI, versus legacy systems versus other digital projects.
“What do you do first? Is it a chatbot? Is it fulfilment? Do you leap ahead with speech?” Swinburne asked.
For BPay, the pursuit of AI is at the beginning stages, Donohoe said. The overarching mission is finding practical and valuable ways of offering services to the customers it serves. BPay is also trying to understand the end-to-end payments experience as it relates to AI and what the company can do in order to help reduce frictions in commerce and use of its products and services.
“We’re working on a venture where we’re exploring the opportunity to lift data from unstructured documents and turn those into the opportunity to use AI to help with bill payments and other types of use cases,” Donohue said. “That’s quite exciting for us because it’s stepping outside the realm of what we would normally do. It will help us service our customers in a different way.”
BPay is also exploring data science and using all sorts of techniques and learning. She recognised a top challenge in AI is getting access to adequate data sources in order for the algorithms to learn.
“One of the things we’ve had to overcome is how you get those sources of data while also making sure we’re respecting privacy and the use of that data,” she continued. “It’s then learning from that and feeding that back into the undertaking.”
For BPay, the desire is how to be different from the competition. “There are lots of people investing in similar areas. And so understanding what everybody else is doing, and seeing if you can leverage those solutions, is something to be thinking about,” Donohue said.
Another big challenge is talent. “We need to have people to conduct the experiments and actually turn them into production-grade solutions,” she said. “Looking at the emerging data pool for data scientists and analysts, I think we’re going to see a shortage. There are light years between a good and poor data scientist.”
The Australian Volkswagen group, meanwhile, is looking at using AI in ways that will help the company make better decisions tapping into its abundance of data, and in a timely manner. Bradshaw pointed out the car maker has more than 300 systems just in Australia.
“How do we use AI to bring all of that data together so we can make timely decisions that impact in a positive ways the lives of our customers and our team members?” he asked.
While business units across the world are using AI in different ways, domestically Volkswagen is leveraging AI via two platforms: Salesforce Einstein and Qualtrics. The Qualtrics research platform, for example, is used to understand key drivers of behaviour.
“This then forms some of our programmatic work from an educational point of view with our team members,” Bradshaw said.
While insights are aplenty, he acknowledged challenges associated with data privacy and the need to be vigilant with customer safety.
“It comes down to the fundamental question of trust and transparency. If we were to look at what the future looks like, I think unfortunately there will be some case studies that will be talked about for decades around organisations with great intent, but breaking trust,” Bradshaw said.
“That will force organisations to be a lot more transparent around how they are using, collecting or not collecting data, and how they use that to leverage people. And it may not be the person that is interacting with the device. This is really going to test an organisation’s desire to be transparent.”
Long term, the company is looking to invest in AI tools for policies and processes in order to get out of the way of employees doing great work, and make it easier for people to get on with their lives.
“The AI technologies that are going to absolutely stick with us are those that make our lives easier and in a way that allow us to be successful,” Bradshaw says. “Every one of us wake up every morning wanting to achieve something and it’s great when we can achieve it with a little bit less effort than what we expected.”
Bradshaw didn’t, however, see a world where AI replaces humans. “I see AI actually letting us start to build human connections again,” he added.
“Get away from the processes and systems, and ‘this is the way we’ve always done it’, to actually connect with each other and having some relationships again.”
Hubbard said UBank, a challenger, digital-only brand, is focused on changing banking for its customers. The company commenced its foray into AI about 18 months ago and has two experiments in production now: One customer facing, and one internal.
One launching in coming months will use big data to do deep analytics and provide unique insights for customers, Hubbard said.
“The lesson we’ve had over that last 18 months is the more we understand what’s possible, the more use cases that we spot, and the more opportunities that we see,” he said. “So it’s been a good start, but we’ve got many more to do.”
In delivering the two use cases, Hubbard said he was amazed just how quickly the team was able to move from idea to production. The first use case was a chatbot, dubbed RoboChat.
“One of the interesting things about our chatbot is its focus and its scope. It is very much targeted on helping customers as they apply for a home loan in a digital-only channel. We’re able to go from idea from finishing developing in six weeks, and were in production within eight weeks,” he said.
The tool has involved quickly since going into production and now answers about 80 per cent of customers questions correctly on the first attempt.
“It’s had a dramatic improvement on our home loan conversion rate, particularly the part of the experience which is starting a digital application through to getting to the end of that application,” Hubbard said. “Across the customers engaged with the chatbot, we’ve seen a 15 per cent increase in conversion rate. It has been a great chance for our team to get their first taste of developing using some AI toolsets.”
This sparked the next phase of its journey, which flipped the concept on its head and sees teams taking insights from the chatbots to be used by customer service agents.
“We’ve invested over 1000 internal documents from our various knowledge databases and information sources and using natural language processing, and some machine learning models, teaching it about UBank, and then enabling our customer service agents, through a single search query, to get real-time help as they service our customers,” Hubbard said.
For UBank, a short-term problem is data and access to data. “We’re only a 10-year old company, yet somehow we still have legacy systems and we still have challenges getting our data, particularly our global data, our customer data, from legacy data stores into a place where we can use it for AI,” Hubbard added.