AI and Machine Learning
The most significant differentiation to make is between Artificial Intelligence and Machine Learning. Although both terms can exist together they define something different.
When we talk about AI we’re referring to a broader concept of machines and systems being able to carry out tasks in a way that we as humans would consider “smart”. Whereas Machine Learning is the current application of AI where we give machines access to data and let them learn for themselves.
AI and Machine learning also include technologies such as neural networks, deep learning and natural-language processing. A neural network is a computer system designed to work by classifying information in the same way a human brain does. Deep learning is concerned with a set of algorithms, this can combine different types of algorithms, and can be showcased through multi-layered neural networks. Natural-language processing is the application of software to understand and analyse language and speech.
It’s important that we remember that AI has existed in our lives for many years, but we mustn’t confuse how it exists today with what AI will look like in another 10 years. As with all technology, it continually evolves to become more useful and more specific for our needs.
AI Is Already Here
The forms of AI that exist in our lives already, go unnoticed most of the time: number plate recognition, invoice scanning, financial forecasting and even image recognition.
Systems already exist that not only consume large amounts of data but also provide actionable insights almost immediately.
By running through numerous probabilities, an AI system can give you the most probable answer based on its findings. The more data we feed the systems, and the more we train it, the more accurate the outcome. Either flagging a number plate with no tax, or suggesting that a certain month in the coming year needs more income or realising an invoice has the wrong dates.
AI will be vital in helping businesses not only streamline their processes like above, but also in improving the customer experience. We often talk about the future as a time when the digital and the real world will become one. AI will be a key enabler in this.
The combination of data processing, speed, accuracy, understanding and integration make Artificial Intelligence technology an ideal system to bring everything together.
A perfect example of what I’m getting at can be explained through my recent visit to a BMW showroom. After arriving at BMW to upgrade my car, a showroom assistant approached me and asked a couple of questions about what I’m looking for. After spending some time showing me a number of different models, and trying to sell me something that I wasn’t interested in, I realised the process of buying a car has become incredibly disjointed.
Customers Know what they Want
When you walk into a showroom today, there’s a good chance that you know more about the car you want than the person selling to you. You’ve already visited the website and compared multiple models, you’ve saved your favourite one, emailed it to yourself and even customised your dream car on the website or app car builder. At this stage you’ve made a significant investment into the buying process. However this is completely disregarded as soon as you step into the showroom. The sales assistant has very little information, if any, on what you’re interested in. The disconnect between a traditional CRM where purchase history or showroom conversations are logged and our online browsing data is huge, and it’s significantly impacting the sales process.
Car dealerships, such as BMW, have huge opportunities to use technology to help streamline the sales experience. They could have cameras in the car park which recognise my number plate when I park and automatically upload my purchase history to a sales person’s device.
To take it one step further, beacons at the entrance of the showroom could trigger an automatic download of my app browsing history, in which An AI algorithm combines with my sales history to create a personalised profile ready for the sales assistant to sell me the car that I want.
In the end I left the showroom without purchasing a car, the experience wasn’t personal, and the user journey was disjointed. My experience could’ve been improved by using data already created. The technology required to make this happen already exists, companies just need to utilise it to combine the digital and real world experience.
Amazon Combines Digital and Real World Experience
Amazon are rumoured to be experimenting with a similar concept. Our experience with Amazon is purely digital until we physically receive our package. They utilise all of the data they have on us, our purchase history, our browsing habits and our personal details. Amazon’s rumoured physical store, manned by only 3 people, would bring this data into the physical shopping experience.
By swiping into the store using your phone, the shop assistants will be able to greet you personally by name, the digital infrastructure of the store will not only be able to make suggestions on products you want to buy, but will also see dishwasher tablets you bought two months ago and must be running low on, and therefore prepare the item ready for you to collect as you walk through the store.
Cameras placed throughout can detect the exact items you pick up and automatically charge your account when you leave. A truly seamless shopping experience, and a perfect example of bridging the physical and digital worlds.
The same web of technology could be set up in any retail space. Imagine walking into a clothes shop, facial recognition cameras remember you, and the location triggers upload your browsing profile from your app. By the time you’re two feet into the shop, AI has used your data to select clothes within that shop that you would be most likely to choose. Then cameras in interactive mirrors detect your different expressions when it augments different clothes onto your reflection, and not only establish whether you like it or not, but instantaneously learn from facial reaction, reassess the rest of its choices and lets the shop assistant know which styles to prepare for when you visit the changing rooms.
The use of AI in the real world can be applied to numerous business cases providing there is access to enough relevant data.
I’m not suggesting this is an easy, overnight change for businesses, as both resource and funding will need to be put behind it. But businesses need to start preparing for tomorrow’s customer. We don’t know exactly what tomorrow brings, but you need to be prepared. You need to start mapping your users’ journey, and instead of just collecting as much data as you can, start to look at how you can connect together the entire experience.
Paul Jarrett, Managing Director of Sonin App Development