AI opportunities for the future
One of the most impacting technologies
As mentioned in our previous blogs, Artificial Intelligence (AI) is said to be one of the most disruptive technologies impacting several industries and businesses. Researchers and practitioners compare the emergence of AI with the industrial revolution of the last century (1). AI comprises a set of technologies that will change business as we know it. More and more tasks will be automated, but the largest impact will follow from brand-new business models and the rise of intelligent services that did not exist before.
Joanne Lijbers , Carmen Wolvius & Hicham El Bouazzaoui - 2 February 2018
To conclude our series of blogs on AI, this edition will highlight 5 exemplary business opportunities enabled by use of AI. We provide these examples to have a discussion around questions such as: what does incorporating AI in your business can offer? What value can be delivered by AI? And how to measure the value of AI? We know that impact of AI can be expressed in different ways, e.g. as depicted in table below:
We will now go through some examples across various industries and examine the nature of AI implemented in these cases.
Application 1: AI in disease diagnosis and illness treatment
Cost reduction by AI seems to be an important driver to counter the ever increasing cost of healthcare. However the opportunity to improve the effectiveness of healthcare by AI driven diagnostics and treatment plans is much bigger.
Opportunities range from drug design to patient diagnosis and to create personalized treatment plans (almost on DNA level).
Just as an example, Infervision uses Deep Learning techniques on patient data derived from X-Ray, CT, MRI, text descriptions of symptoms and diagnostic reports, to construct automatic diagnostic recommendations2. Multiple top-notch hospitals in China already work with this recommendation system, identifying (lung) cancer in an early stage.
Another example is the use of Deep Learning to classify skin cancer. Researchers already developed a system that can classify skin cancer with a precision comparable to dermatologists3. Transferring this technology to mobile devices can make dermatologic care available for more people than ever before.
Application 2: AI in fashion design and customer interaction
Not the first topic that comes to mind regarding AI, but the technology is expected to influence the fashion industry as well. A long term, but disruptive, opportunity is made possible by GANs: Generative Adversarial Networks. The algorithm uses two neural networks: a generator and a discriminator. The logic of GAN is that the generator generates images of which the discriminator believes them to be real (using an iterative approach). The generated images are so alike to the training set, they would fit right in. Using this technique a GAN can act as a true fashion designer by creating brand new items, in the same style as the style of the items on which it is trained. The Amazon team Lab126 has already started to test the possibilities in this domain4. For this specific example, in terms of value, greater customer satisfaction and even competitive advantage can be achieved.
There are also other examples. Fore instance AI driven chatbots equipped with NLP and image recognition capabilities can interact with customers in an intuitive way and advise them on products. Imagine you taking a picture of your shirt and sharing it with the chatbot who then comes up with advise on newest trendy sneakers matching your outfit.
Application 3: AI in cyber crime and fraud detection
The impact of cybercrime on global business is enormous. Next to intangible damage for a company (e.g. brand damage), costs can be huge. It is estimated by Forbes that the global costs for cybercrime will be 6 trillion dollars by 2021. A large proportion of this cost is in credit card fraud and although fraud detection techniques exists for years, they are still not sufficient enough to prevent this from happening. New AI techniques, like Recurrent Neural Networks that were previously not available can be the solution to detect fraud in earlier stages8. Equipped by trained RNN’s, fraud detection systems can scan thousands of transactions instantly and predict / classify them into buckets (e.g. ranging from high to low probability of fraud). This type of system could help save time by focusing the cases where probability is high for fraud. A word of caution in this case is also applicable. Utilizing these type of algorithms requires a solid governance system to monitor the output and limit potential bias as much as possible. It’s very well known that due to pre trained character of the algorithms, bias can be serious flaw in the system.
With the recent launch of the new iPhone X, Apple brings face recognition to many of us worldwide. In the next years iPhone users will be able to unlock their iPhone by looking into the front camera. But authentication of personal content is not the only application for face recognition. Governments and security services use face recognition to identify citizens and track down criminals. Recently the police in China captured twenty-five wanted criminals at a Chinese beer festival based on photos taken at the entrance of the festival5. Not only facial recognition can help in tracking down criminals, emotion analysis can deliver extended value regarding this opportunity. Being already in use, NTchLab created software that identifies if someone is feeling stressed or angry. Put into use in e.g. a grocery store, detecting a stressed person will alert security to pay extra attention6.
Application 4: AI in personalized advertising and support
Imagine walking into a store and your favorite brand or product is at discount the same day. This can be a coincidence and your lucky day, but it’s an advantage for the store owner as you’re probably more willing to buy the product. All of this is possible with AI techniques like face-recognition. A personalized advertising application as described above is trained with many examples, knowledge of people and their shopping behavior. It results in an intelligent application that can identify the type of customers, their emotions and possible shopping preferences. Personalized advertising is a proven method to generate more revenue. Since these techniques are already available today, this opportunity is a high value/short term win.
England largest online grocery shop Ocado is a true pioneer when it comes to AI-technology in a customer’s daily shopping routine. With a cutting edge robotics system and an autonomous delivery system in store, they recently released a new Amazon feature: voice control 7. When in possession of an Amazon Echo (Alexa) one can simply call out new items and Alexa will add them to an existing shopping list. Although this feature is already on the market, its added value will be something for the future. Currently, a shopping list has to be initiated manually and Echo only listens to full and scripted sentences. So a phrase such as: ‘He Echo, we’re out of milk!’ is still a long way off.
Application 5: AI in logistics and on-time delivery
With immense growth of e-commerce and on-line shopping, one of the biggest challenges for ecommerce and many logistics companies is to reduce the costs of delivery in the “last mile” while maintaining quality and service. Some of the most innovative companies are going through a paradigm shift using AI. Some great examples are out there where driverless cars are being utilized for autonomous delivery and real-world tests are right now on-going7. There are also more practical areas where AI plays an essential role in the logistics. Think about on-time delivery, planning and time-estimation used by logistic companies through their track-and-trace systems. A good example of how AI is utilized is an algorithm developed by Deloitte Netherlands to predict timeslot of delivery. This model is able to make predictions for future deliveries based on delivery route patterns. These patterns are found using AI techniques. Proven cost reduction and significant improved customer satisfaction are the big wins here.
We have discussed some of the applications of AI and can conclude that there is a major opportunity for the business to improve, gain efficiency and define new business models by utilizing the power of AI.
This was the 5th and last edition of our series on AI. We started the series on AI by providing an overview of terms and definitions of AI9. In the 2nd blog we have discussed some of the fundamental techniques which are at the heart of AI10. In the 3rd article we went on and gave five applications of AI: Image recognition, Speech recognition, automatic translation, Q&A and Games. Then we continued and analyzed five technology trends that leap-frog AI. This was published in our 4th blog. And then finally, in this 5th release we have provided some practical applications of AI in day-to-day life.
AI is and will remain a dynamic and broad filed encompassing several areas of expertise, ranging from computer science to mathematics, neuroscience all the way up to philosophy and even biology. We hope that the topics discussed in our 5x5 on AI edition will help the reader to get an end-to-end overview of AI in a more practical sense rather than a bullet proof complete scientific exercise.
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