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Artificial Intelligence (AI), what exactly is this?

You hear and read more and more about Data science and Artificial Intelligence (AI) in the media. I often get asked to explain this simply. This is not easy to do in a short article like this. In fact, Data science and AI is also a collective name for many methods and techniques. I would therefore like to limit myself to explaining in easy language what Data science and AI can mean for an organization so that everyone can easily interpret this and project it on their own organization.

New entrants use data-driven business models in many markets. Prominent examples are AirBnB, Uber, Netflix and Booking.com, which all bring together supply and demand through a data-driven service / platform.

Data science and AI can be used for specific applications in specific business situations. Examples are: chatbots, knowledge and decision systems, speech, party and image recognition. The apps on your mobile phone probably have more artificial intelligence (AI) than in all of your business applications.

The big difference between “Business Intelligence” and “Artificial Intelligence” (AI) lies in the predictive and self-learning ability of AI. AI is able to predict events and results with very high probability based on prediction models and available internal and external data, which can then be reacted with a service. Consider, for example, predicting malfunctions in production machines, a data-driven customer contact center or regulating energy consumption by predicting peak moments. Chatbots can play a major role in supporting employees and customers in the service processes and through AI, a company can further automate the production and trade in products. A chatbot can make a lot of jokes, but not yet. Technology is available to make the bot understand the customer’s emotion, but that does not mean that a bot can respond to the customer in the correct, empathic way. This combination of man and machine remains crucial.

Introducing new processes is a “blue” approach. But you can inspire employees and look for people who are most motivated to grow a movement or organizational change. Although the Data science & AI field is not new, there are not many people who understand the full benefits of the benefits, which sometimes makes distribution difficult. Data science & AI in an organization is and remains a great journey of discovery. It is a complex field that you have to explain in easy language in order to reach a larger audience. In addition, the art of change management is required.

It is important to have a diverse workforce with a mix of ambition and experience that you each want to stimulate in their own way with new insights and activities. That way you will discover quickly enough what motivates everyone and you will discover common denominators. Pushing people to use or create a certain solution is of course less effective than letting them discover and create a new solution from a shared vision.

For a Data Scientist who likes to innovate with data, characteristics such as curiosity, creativity and openness to the outside world are of course important, as well as domain knowledge and a feeling for the business. It is therefore important not only to know everything about a certain AI method and technology, but also to talk to customers and your own organization so that you know which solutions you can contribute that actually create value.

 

To what extent can technology replace humans?

It is important for Data scientists in the innovation domain to remain curious and to look for new possibilities. Mastering the AI ​​methods and techniques is one aspect, but understanding what goes on in the minds of customers is also a major influence on the final solution. That is exactly what makes the profession so much fun: when you start discovering it, you come across something that might be something completely different when you dig through.

AI solutions are often very logical, but the illogic ones make innovation interesting. The successful, new solutions often do not arise by building on what is already there.

Just like in a flock of birds, you just want to have a bird flying up the other side so that you discover something you didn’t know yet. We only see this movement as something special because a bird deviates from the pattern. Therefore, it doesn’t hurt to do something illogical every now and then because it can take you on new paths. The question is whether there will ever be technology that has such a human factor, such as illogical reasoning, but especially the empathic component. If the emotion is not real, we often don’t believe it.

My conviction is that data takes on an even more central role in the business operations of every organization. That we succeed even better in the here and now because we do more with data. And that we put data at the center of innovation, because what fact is better than data in knowing that ultimately people will continue to make a difference if the facts do not point you to the solution.

I believe that Data Science & AI is in every heart of a successful business model, ergo is the basis of a successful business model. Data science & AI are going to make the “competitive” difference. The D of Digitization of Data is standard in the marketing mix of Porter and is the 5th P!

 

Are you also looking for the right competences to help your organization with data and to develop data driven insights and services? Then look at IXT, People multiplying your Business; www.ixt.nl or mail to hans@ixt.nl

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