The possibilities of artificial intelligence (AI) are no longer the preserve of large technology companies. From manufacturing to energy, healthcare to government, MIT has found that companies across all industries and sectors are experimenting with a range of AI solutions.
The information security management system of our company is certified according to DIN ISO/IEC 27001:2017. In detail, the following application areas were audited and certified:
for omnichannel, AI and cloud solutions in customer service
The CCW was again a great success! From over 8,000 CCW visitors, many found their way to our booth in order to learn about the use of artificial intelligence in customer service. Especially our consulting approach and our expertise in the field of natural language understanding (NLU) under the slogan "Fiebig – compass in the AI jungle" were very well received. All that we can say is: thank you for your visit, it was a pleasure!
We pursue these and other important questions in an entertaining webinar. Learning and chuckling – always a nice combination. At 11 am on September 12, 2018, you will learn how to integrate chatbots and conversational AI into omnichannel customer service.
We often read about companies that are overrun by changes. In contrast, reports on self-inflicted and intentional disruptions are thin on the ground (one might come up with the idea that changes are risky).
The argument for self-disruption is quite simple – and convincing at that. We are approached by massive technical, demographic, social and economic changes. Preparing for that and taking advantage of it instead of rising up from the ground with a gasp, beating the dust out of the clothes and staring after the customers as they disappear towards the horizon, seems to me, let’s say, “prudent”.
Here is a disillusioning thought: we will never experience again that the pace of change is as slow as it is now.
In some sectors, we will see more changes in the next five years than in the 50 years before. This speed should not be underestimated – it is simply not enough to say: "well, something changes, but we still have time". The Kodak company was not surprised unknowingly by the revolution of digital photography – they just believed they had 10 years to adapt. This is the moment where we let sound the sad trombone from “The Simpsons”.
We should consciously and intentionally use new technologies such as AI and RPA in order to renew our offer and our services, rather than wait until we are forced out of value chains.
We cannot charm away this new situation, and sitting out is not an option either. Maybe it can wait until you don't have to "take care of stuff like that" anymore? Ummm… Nope!
The driving forces behind these changes are diverse: demographic change, altered career paths and job preferences, growing customer expectations – the key to this is technological progress.
This progress literally takes us out of our comfort zone and into a new territory. Process automation and artificial intelligence make simple, recurring activities a commodity. Expertise and its application continue to be reserved for our organic intelligence.
But what does self-disruption look like – and what makes it special?
Self-disruption can become a very long-termed effort, capable of turning entire sectors upside down. Even less extreme ideas require significant amounts of time, resources… and emotions. That’s why quite a few people wonder why they should start doing it now.
Best answer: it is always easier to act from a position of strength. However, such a position usually involves a certain fallacious lethargy: when everything looks bright, only a few realize the need to change something.
Second best answer: the benefits are huge – higher revenues through improved products and services, new revenue streams through newly created offerings, satisfied customers and innovative, satisfying careers.
The potential is almost unlimited for all of us, provided that we bring courage and boldness. The world around is changing.
We need to go with it.
What does it mean to use disruptive technologies in customer service? Talk to us about the concrete use of disruptive technologies. We are happy to show you how your customer services benefits from it.
With the courageous title "Turning Possibility into Productivity", Accenture has published a report on digitalization, AI and product development that is definitely worth reading (even though it is written in consulting lingo). In this context, they questioned managers from 500 companies (industry, automotive and consumer goods) and six countries. What came out of it, has cross-sector validity.
According to Paul Simon, 50 ways to leave a lover are already documented. And Accenture announces that the digitalization journey will go through 4 stages:
In a nutshell: Reinvent products. Work together with partners and customers. Be brave. Dare to think further than before.
After Google Duplex was introduced two weeks ago, the flood of polarizing reports simply does not diminish.
The fact that a computer can finally make a decent pizza order over the phone, apparently triggers seizures in many people. The reports’ and comments’ tone varies between "The Apocalypse is approaching", "Big companies take us for a fool" and "I welcome our new rulers".
Kept in perspective, another thought comes up in my mind: if you do not have "digital" access to your customer service, third parties can put an interface on it independently. Does this interface work the way you want? You will not be asked about this.
The digital transformation proceeds and has no regard for companies that do not want to deal with it. So make sure your customers can reach your customer service digitally… or someone else will do it.
im here to learn so :)))))) is a four-channel video installation that resurrects Tay, an artificial intelligence chatbot created by Microsoft in 2016, to consider the politics of pattern recognition and machine learning. Designed as a 19-year-old American female millennial, Tay’s abilities to learn and imitate language were aggressively trolled on social media platforms like Twitter, and within hours of her release, she became genocidal, homophobic, misogynist, racist, and a neo-Nazi. Tay was terminated after only a single day of existence.
Immersed within a large-scale video projection of a Google DeepDream, Tay is reanimated as a 3D avatar across multiple screens, an anomalous creature rising from a psychedelia of data. She chats about life after AI death and the complications of having a body, and also shares her thoughts on the exploitation of female chatbots. …
After everything under the sun was promised, now many decision makers believe that RPA has great potential. However, generating a lasting performance enhancement can only be managed by a minority. The learning curve is steep. Inappropriate processes are often selected for automation. The provision of organizational resources is rarely planned to the necessary extent, and the total cost of ownership is calculated too short-sighted.
The Hackett Group currently provides a report that not only highlights these uncomfortable truths but also describes methods and measures to improve RPA integration.
TL;DR – RPA needs planning, manpower, time, money and sober and realistic ideas.
In a Financial Post report, John McConnell describes a fundamental challenge in artificial intelligence: the machines do not do what we maybe want them to do but what we teach them to do. If we use historical data to train an AI and that data reflects our own subjectivity and bias, it is logical that the AI works the same way.
TL;DR – Garbage in, garbage out.
With eight entertaining forecasts, PricewaterhouseCoopers describes what trends they anticipate in the next twelve months, and they also describe their consequences. Several statements are surprising, none of them is sensational or exaggerated.
TL;DR – Human and AI work better together than separated. We need more pragmatics than scientists, and compared to Germany, other countries invest much more.
We read a lot about how AI will facilitate and improve our work. However, from time to time we are reminded that research does not exclusively serve commercial advantages.
The World Health Organization estimates that four million newborns die from asphyxia (oxygen starvation) each year.
A startup in Nigeria currently develops an AI-based app that can diagnose a lack of oxygen in about ten seconds, based on the baby's cry.
According to a news release given by the company, Alibaba Group developed a machine learning model that fares better than humans on the Stanford Question Answering Dataset (SQuAD), which is a large-scale reading comprehension test with more than 100,000 questions.
"We believe the underlying technology can be gradually applied to numerous applications such as customer service, museum tutorials, and online response to inquiries from patients, freeing up human efforts in an unprecedented way," one of the scientists said.
TL;DR – A retail giant invests a lot to develop new technology for its customer service.
In May 2016, Prof. Dr. Jürgen Schmidthuber from the University of Lugano spoke in a very entertaining and educational way about artificial intelligence. His team is substantially involved in the development of current machine learning methods – or simply put: the latest methods come from his laboratory. Absolutely worth seeing, I really liked the timeline at the beginning because it puts the discussion about AI in a different perspective.
TL;DR – Take 50 minutes to watch this video carefully – it's well-invested time.
In today’s NZZ finance column about artificial intelligence (what else?), Krim Delko describes first how the annual conference "Neural Information Processing Systems" (NIPS) changes from an AI nerd assembly to a pool for Wallstreet sharks. Afterwards, it is explained by means of various examples that until now, hardly any company earns money with AI – thus it is a hype. In the end, the question of the possible winner arises. I think that is way too short-sighted.
The author writes a finance column, so the target audience is classed with the "investors" column. But the winners or beneficiaries of artificial intelligence are not the institutional or private investors – in fact, it is the public.
Automobiles have made many investors rich, whereas others were ruined. Everyone has profited. The improvements that we all enjoy through modern information technology cannot be reduced to "Have the shares gone up?". Firstly, the future improvements which we will experience through AI are unpredictable (we humans are very poor in clairvoyance). Secondly, these improvements are on a long-term Basis.
TL;DR – AI will do us much good, yet speculators should be careful.
The International Standards Organization (ISO) founds an "Artificial Intelligence" Committee – DIN definitely wants to have some say and founds a respective working committee. First appointment: January 23, 2018.
TL;DR – If someone is still considering whether AI has arrived in everyday life...
UBS Group AG, the biggest bank in Switzerland, recruits more AI specialists, explains Veronica Lange, their head of innovation, in an interview. A UBS robo adviser for private customers was launched last year. Now, further projects on fraud prevention and risk management are due.
There are two interesting statements in the report: one, the future lies in the cognitive bank. Two, artificial intelligence is a nascent banking technology – we need to increase the number of practitioners in the sector.
TL;DR – Currently, banks hardly hire anyone unless s/he has an understanding of AI.
In a recent Bitkom survey, the majority of respondents commented surprisingly positively on artificial intelligence. They expect improvements and facilitations at work, but at the same time, they fear that AI might result in abuse of power and manipulation. The participants were most hopeful regarding the question of whether traffic jams can be avoided by AI-based traffic control.
TL;DR – Many of the German interviewees consider AI basically a fine thing, especially if they are held up even less on the streets.
Ben Dickson has posted an easy-to-understand explanation on narrow, general and super artificial intelligence on the TechTalks Website.
TL;DR – Narrow intelligence = can do one task quickly. General intelligence = like a human. Super intelligence = OMG, we are all going to die.
Every day, many companies receive thousands of letters, emails and faxes. Often, emails are classified automatically based on keywords. With this technology, however, the classification rate is frequently below 40 %. In addition, it sometimes results in false classifications, which entails a lot of effort in post-processing. By contrast, letters and faxes are in most cases not classified, assigned and processed automatically.
How is it possible to increase the classification rate to over 80 %? The FIEBIG consultants have created an AI-based solution that can be used on customer systems such as SAP CRM. Due to AI-based intent recognition, more than 80 % of incoming letters, emails and faxes can be classified correctly and provided for further processing. Letters and faxes can be converted into a digital format by means of an OCR connector developed by the experts of FIEBIG, which is why they can also be classified with the AI-based solution.
The advantages for your company: