The impact of AI in every company division. Part 5 of the series ‘Perspectives on Artificial Intelligence’.

Futures Studies

Of all the current technological developments, artificial intelligence is both the most profound and the least understood. We are witnessing impressive new applications, but can hardly foresee their impact on people, organisations and society. In this series of blogs – Perspectives on Artificial Intelligence – we investigate not only the opportunities, but also the intended and unintended consequences.

No escape

In previous blogs from this series, we discussed the impact of artificial intelligence (AI) within Customer Service and Human Resource Management departments. This blog will show that AI affects all departments. Anyone setting up a strategic business plan for the next 3 to 5 years can no longer ignore AI.

Demand forecasting

What if you had a better projection of demand for your product? Your company would experience less waste, reduce stock levels and avoid lost sales. In sectors with high turnover rates and low margins, such as retail, this makes a significant difference to company profitability. Sales increase, while production, storage and logistics costs go down.

Thanks to AI and big data, demand forecasting algorithms are becoming increasingly complex and sophisticated. In addition to historical demand data, you can now include local weather forecasts, changes in customer attitudes on social media, new pricing by competitors, and recent media attention. It is not only the accuracy of these algorithms, but also their frequency which is on the increase. Demand forecasting can be continuously updated, with real-time a real-life possibility.

Market and product development

Demand forecasting is also possible for new products and new markets. If you understand what drives demand for your existing products and know which attributes your consumers will respond to, you can also predict what reactions new product variants will bring. This saves on market testing costs and also reduces the time to market (TTM).

The same goes for the effects of the expansion of your distribution network. You can estimate how successful sales will be in a new region or country. This is conditional upon the availability of data; for completely new products, for which customer demand still has to be created, this is not viable. Algorithms are only as good as the data they can access.

Production and logistics

Thanks to AI, robots are becoming increasingly intelligent and can be applied in an ever increasing number of ways. The first factory robots were relatively stupid; they were stationary and carried out the exact same actions time and time again. Modern robots are equipped with sensors and can move throughout a workspace. Recognition technology has become so advanced that robots are conscious of objects (and faces) around them. They can avoid all types of collision.

The video below shows the Alibaba warehouse in China. Amazon’s in the USA looks much the same.

The control of these robots is dependent upon AI. The collaboration between robots, as well as route amendments in case of malfunction, is fully computer-controlled.

There are also robots that can collect the correct product from a shelf. These are programmed by a robot instructor (new profession), who takes hold of the robot arm and carries out the correct range of movements. Thanks to machine learning, the robot is then not only capable of repeating, but also capable of improving this set of actions.

AI and Robots can accelerate processes, reduce costs, and increase output within a production environment. Due to their accuracy, the number of errors decreases while quality improves. Machine downtime is reduced through the use of sensors which indicate when preventive maintenance is required.

Marketing and sales

We have already become familiar with dynamic pricing used in airline ticket and hotel room sales; this is now being applied to an increasing number of product categories. Algorithms determine the price based upon an estimate of what the consumer is willing to pay. Many factors play a role in this decision, such as the day of the week, seasonality and weather conditions. Prices may vary according to the sales channel (online, offline) and can even be changed during the day, for example in response to price changes made by competitors. Algorithms can also take your location and the brand and type of the device you are using into account, along with your historical buying behaviour. For these reasons, not every consumer will pay the same price.

Advertising is becoming more personal thanks to AI. Not everyone is shown the same banners when online, and not everyone receives the same offers from stores. Albert Heijn knows exactly what you usually buy thanks to their bonus card. Customers who prefer top brands will not receive offers for own brands. Customers who don’t buy nappies are not interested in purchasing baby food. Data makes clear which message you should use at which time to convince a particular consumer to buy a particular product at a particular price.

Burberry also uses AI to promote sales, both online and in-store. Through loyalty programmes, the company collects as much customer-related information as possible and makes suggestions for follow-on purchases. Shop employees can then see which products are likely to arouse customer enthusiasm via an in-store tablet.

New customer experiences

The best examples of new client experiences built upon AI often come from start-up companies. Uber has eliminated all the obstacles associated with traditional taxi transport, such as customer insecurity regarding waiting times, prices and driver quality. Meal delivery services such as, Foodora and Deliveroo use AI to plan routes. And Trivago searches a few hundred websites in order to find the lowest possible price for a hotel room.

Get set

AI affects all company divisions and can make a major difference to the positioning and profitability of a business. Organizations in all sectors, both profit and non-profit, should take the impact of artificial intelligence very seriously.

A good start is to develop your strategic options. What opportunities does AI offer for upgrading and improving your existing products and processes? What can you do to increase sales and enhance client loyalty? Which new services might you develop for entering new markets?

Various methods and tools are available for setting up systematic trend research, developing scenarios and creating strategic roadmaps. Writing a business case may be the logical progression, but if you are undertaking something entirely new and historical data has little to offer, you may just as well start with an experimental business right away.