A glimpse into the future

The year is 2030. It is a sunny summer morning and you are just finishing a relaxing breakfast together with your family. The doorbell rings for a delivery of several essential household items on which you are running low, plus your food for the week. But you never placed this order or booked this delivery slot. Instead, a loyal, autonomous, context-aware smart agent placed it on your behalf. Using data from IoT devices in your home combined with knowledge of your past habits, the agent identified the products you needed. Using knowledge of your budget, spending habits and loyalty programmes enabled identification of the best supplier on your behalf (taking into account availability and delivery costs). Using knowledge of your diary and habits the agent  chose a convenient delivery slot. Once all of this was done, you were quickly consulted to check you were happy with the proposals. Then, after your agreement, the agent autonomously completed payment for the order on your behalf using a digital currency known as the ecoDigiCoin.

As you unpack your delivery, you think about the amount of effort that your smart agent saves you, giving you more quality time with your family and friends, and to enjoy your hobbies. Of course, many people are still wary of the data sharing that underpins this service. This is why regulations insist that such agents must be proven to be loyal to their user (not the company who built them or any other third party) and that privacy-preserving techniques must be used to ensure that data is only shared and used in a trusted and controlled manner.

After unpacking your delivery, you decide to go shopping for some new clothes. You step outside and an electric vehicle is waiting for you. It is self-driving and arrived at your front door whilst you were eating breakfast. Rather than owning a car, you pay only for the kilometres you travel in shared autonomous vehicles. Predictive analytics powered by artificial intelligence make sure that one is available when you need it, and this sharing economy model is also reducing the environmental impact of travel. The payment for your journey is automatic. After you step out to go shopping, the car drives itself to a charging hub (where it also autonomously pays for the recharge).

Once on the high street, you receive an alert on your phone. Some shirts you were viewing online the night before are available in the shop you are just walking past, with a promotional offer available today. You visit the store to look at them. Before trying them on, you enter their immersive virtual fitting rooms, where you can quickly see how the clothes will look on yourself without having to change into them. After this, you try on physically the ones that you like the most and ask one of the shop’s fashion advisors for some suggestions for trousers that might be a good match. Finally, you choose three shirts and two pairs of trousers, thank them for their helpful advice, and walk out of the shop without passing through a checkout, with payment made automatically based on biometric identification from your palm scan. In this case your eWallet has determined that a cash-back offer associated with your credit card account would make this the preferred method of payment. The high degree of automation at the checkout is what gives the staff plenty of time to provide you the highly personalised advice and attention that you have enjoyed.

As you continue along the high street, you walk past a musical instrument shop. Suddenly you make an impulsive decision to buy a guitar – you have always wanted to learn to play one! Inside the store you check that the guitar is genuine by scanning a QR code which validates the full traceability of its production – including which parts were recycled from discarded instruments, giving you full visibility of the environmental impact of your purchase.  After you buy it, the digital ID contained in your eWallet is used to add your ownership onto the traceability blockchain. Many years ago, this type of high-value, atypical transaction might have been blocked as suspected fraud. However, the permissioned and secure sharing of data means that your earlier journey, your recent purchases, and the location of your phone and exactly how you hold and move it can all be used to validate that this transaction is legitimate. Of course, this has only been possible because of the advanced fraud detection machine learning algorithms that are now commonly in use.

Next you meet some friends for a coffee. Here again you enjoy the personalised service of the barista who recommends a new coffee blend that they think you might enjoy, as well as talking you through their patisserie menu. After this you can relax and share stories with your friends as the drinks and cakes are brought to your table, with payment (including an ecoDigiCoin tip paid directly to the barista) quickly taken care of by your eWallet, with no need to split the bill or store a physical receipt.

After this social time with friends, you decide to take the tram home. Just as with your car, you only pay for the kilometres travelled. And you do not buy a ticket or make a payment. Secure facial recognition is used to register your journey, which is then paid for using your preferred method (direct debit in this case, as it entitles you to a 10% discount with this transport operator).

You arrive home eager to show your family your purchases and share the news from your friends, as you enjoy your evening meal together.