What can AI be used for in your business?
AI is possible to use in all businesses. It is not reserved for big tech companies and giant budgets. All companies can benefit from using AI and Machine Learning to solve small and large challenges based on data.
It doesn’t even have to be that complex. See examples of valuable applications here:
Predict crashes and prioritize your resources properly using predictive maintenance. Here, IoT and Big data are used. You can also measure changes in e.g. water flow and avoid unforeseen waste of water. Like we did with VandCenter Syd, which you can read more about here.
Most banks have large amounts of data on their customers. With a data platform that supports both AI and BI, you can get the most out of data and reuse AI models across your organization. Read how we helped Nykredit build a data platform that supports AI.
The telecom industry
Predict which customers will leave your business (churn). It is also possible to use text mining to analyze reviews on Trustpilot. You can also segment customers and on that basis offer the right telephony package to the right customers.
Use AI as an internal project if you, for example, as a restaurant chain would like to benchmark your different restaurants. Easily get a comparison basis.
Facial recognition allows you to determine the demographics of your customers like gender and age. Customers are registered as they enter and exit the store. For example, it is possible to see how long customers spend in the store, the number of customers in a day – and when they are most busy. Knowing this, you can better manage your resources in the store.
Predict insurance fraud using predictive modeling. It can even happen live – an alarm pops up when something is not as it should be. You can also use AI to categorize mails from customers and let the robot decide which department should receive the mail.
A camera is filming the production tape. It captures the color and shape of the products and raises a flag if a product is the wrong color or shape. This makes you avoid incorrect deliveries.
Use AI for analysis of social media postings where a sentiment score is drawn – that is, how positive / negative the union is mentioned on Twitter. It allows you to take any shitstorm in the riot.
Show relevant products to the customer based on similar customers or previous purchases. Chat bots are also an example of AI, which analyzes the text and uses machine learning to categorize the message from the customer.
How do you create value with artificial intelligence?
Few companies doubt that AI is the new competitive parameter. AI is gaining more and more terrain in business, both at small and large companies. – and in every industry. If you do not want to lose the battle to your competitors, you have to take artificial intelligence seriously.
The question is, how can you as a company use AI to create value? If you want to succeed with AI, it is alpha omega that you have a culture geared towards it. A culture that can handle data, set up an IT setup and attract the right skills.
There are three particular places you need to focus on when starting your AI-journey.
- Where the business case is best:
Optimizing efficiency and speed in high-profit areas is always the right place to start.
- Where the same actions are done again and again:
You have a huge potential in automation, decision support and robotics.
- Where the future needs to be predicted:
With AI comes the opportunity to make the best possible prediction of future events based on complex data contexts.
When trying to find valuable areas, where you can implement AI, work on a clear definition of what problem you want to solve. Otherwise, it is easy to get lost and seduced by very complex AI solutions./span>
Therefore, the recommendation reads; start small and quickly test the idea in the business. Then you can always improve to more complex solutions
Facts about artificial intelligence in companies
Artificial Intelligence and Machine Learning is talk of the town. And there has long been talk of AI as a golden opportunity for businesses. But if you look at the hard facts, how far have we really come in the Danish business world? Among others, Microsoft has made a report showing just that*. Here are some of the conclusions.
of all Danish companies are at the
beginning stage in the use of AI.
of all Danish companies are at an
advanced stage in the use of AI
has a strategy for how AI can be
used to create business value **
What is AI – really?
There is a big difference between artificial intelligence on film and in reality.
Artificial intelligence is not just an element of a sci-fi movie. It is not the future.
It is reality today. But what do we mean when we say AI?
As such, there is no definitive definition of AI. Quite simply, you can say that AI is about making an artificial version of the human brain
We often use the concept of AI about machines that can make decisions independently – and on their own perform tasks that normally only a human brain can perform. This applies, for example, to tasks that require abstract thinking, reflection, analysis, learning, problem solving, pattern recognition, language comprehension and decision-making.
Strong and weak artificial intelligence
Today, we often distinguish between strong and weak artificial intelligence. Strong artificial intelligence is about the notion that computers can theoretically develop a consciousness similar to that of humans.
Weak Artificial Intelligence is about computers being able to simulate every aspect of human ability – and therefore they can effortlessly lead others to believe that it truly has human intelligence and awareness (though it lacks subjectivity that defines human understanding).
AI converted into business value at Aarstiderne
In the past year, Aarstiderne has carried out a strategic customer loyalty project entitled “the best customer experience each time”. The objective was to give more structure to the customer loyalty work. What did it take to achieve this goal?
One of several initiatives was to gain insight into which customers are most likely to churn. Using a special Advanced Analytics model and Machine Learning, Aarstiderne can now identify customers at risk of churning.
”“The model now tells us with great precision which customers are at risk of churning as well as which customers are deemed loyal,” and he continues: “This will not save the world, of course, but the solution’s value potential is very important!”
– Carsten Dreyer Christensen, Head of Business Analysis