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AI IN CUSTOMER COMMUNICATION – 5 PRELIMINARY QUESTIONS

 

AI is set to be a game-changer for businesses across every industry. Artificial intelligence is undoubtedly changing the way companies address and interact with their customers.  Pluq, the increasing adoption of digital language assistants such as Alexa and Co., Siri and Amazon Echo, in private households is leading-edge.

 

A new study by Bitkom and Deloitte on the future of consumer technology showed that, in addition to 13%, who already had an intelligent virtual assistantin 2018, 4% of those surveyed are planning a purchase a voice assistant in 2019 and 27% can imagine controlling devices by voice in the future. The fact that, according to Gartner, 30% of companies will use AI for at least one key sales process in 2020, which encourages AI adaptation. Companies are faced with the huge task of adapting to the increasingly complex communication needs of their customers. Therefore, language assistants are being integrated into more and more devices.

 

Study highlighted the rapid rise of intelligent language assistants in 2018 and in the coming years we will control more and more devices with our voice. Which opens gates to a new billion-dollar market.

 

Despite all the forward-looking tips and statistics, many companies are still wondering how they can ideally use AI for themselves. Also, the costs that result, the impact on employees and customer satisfaction is not really measurable for many.

To get an overview here you should ask the following basic questions:

 

  1. What do customers really want?

Often, when answering this question, it helps to have a closer look at the customer database. Age structure, nature and complexity of incoming requests provide a clear direction. For example, an airline can quickly and efficiently handle the query of travel times with the help of artificial intelligence. But customers still preferer a human contact when questions about insurance details or specific health problems rise.

 

  1. How is automation currently being used?

Automation is not just a topic for companies since the introduction of artificial intelligence. Many have already integrated automatic systems such as IVR (Interactive Voice Response) for telephone inquiries and automated e-mails or SMS into customer communication – systems that have proven themselves so far. Implementing artificial intelligence here is not necessarily the way to go. Rather, one should analyze how existing systems can be improved to meet evolving customer needs. For example, an automated language solution with machine learning in the background could complement an existing solution and offer the customer an improved contact experience.

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  1. How will the employees react?

The biggest fear among employees is that artificial intelligence makes them redundant in the foreseeable future, for example through chatbots, and as a result they lose their jobs. The fear since the beginning of the industrial age, that machines will take over humans and jobs, is the biggest communications challenge. This is further aggravated by; a lack of understanding of AI compounded by confusing communication by various players during the current hype cycle. There is a need for constant communication and increasing awareness to improve the understanding and applications of AI. One just needs to look at history to conclude that every technological change created an explosion of new jobs and services and, overall, generated more wealth for all.

 

  1. How to find the right AI solution and how should an implementation work?

There are already a variety of AI solutions for various functions, including, for example, Natural Language Processing (NPL). You should basically get an overview of the solution providers – especially those who have a platform with interfaces to different AI solutions in their portfolio. It is essential, however, that there is a precise idea of ​​the existing communication infrastructure and the improvements to be achieved in customer communication. For example, cloud-enabled contact center vendors and specialized integration offerings with an end-to-end AI package can bridge the gap between existing functionality and the AI ​​skills needed to meet existing needs.

 

  1. How is one prepared for the future?

AI will inevitably play a major role in the future of customer contact. But there are many details to consider when planning implementation – even though the customer base is not yet fully receptive to this technology, the rapid development of AI and the ability to address more complex issues can lead to that acceptance which will increase significantly in just a few years. Also, the increasing adaptation of consumers to these types of interfaces will increase their acceptance to, and expectation of, this technology. Long-term planning should therefore always leave room to introduce new innovations as soon as they can offer defined added value.

 

This is precisely why Cloud-based contact center and integration technologies are available that are inherently capable of adapting flexibly to new developments and introducing new third-party connectors. This open technology has the advantage of reducing the risks for future AI and contact center planning and provides the ability to introduce functionality as needed. This avoids being late for a new innovation and losing valuable competitive advantages.

 

Impact of Artificial Intelligence on the Future of Labor Market

Impact of Artificial Intelligence on the Future of Labor Market

Disruptive changes to business models are having a profound impact on the employment landscape and will continue to transform the workforce for over the coming years. Many of the major drivers of transformation currently affecting global industries are expected to have a significant impact on jobs, ranging from significant job creation to job displacement, and from heightened labour productivity to widening skills gaps. In many industries and countries, the most in-demand occupations or specialties did not exist 10 or even five years ago, and the pace of change is set to accelerate.

Artificial Intelligence (AI) is changing the way companies used to work and how they today. Cognitive computing, advanced analytics, machine learning, etc. enable companies to gain unique experience and groundbreaking insights.

 

AI is becoming ever more dominant, from physical robots in manufacturing to the automation of intelligent banking, financial services, and insurance processes – there is not a single industry untouched by this trend.

Through the advances in AI, people and businesses are experiencing a paradigm shift. It’s crucial that companies meet these expectations. As a result, artificial intelligence (AI) is becoming increasingly important to simplifying complex processes and empowering businesses like never before.

In such a rapidly evolving employment landscape, the ability to anticipate and prepare for future skills requirements, job content and the aggregate effect on employment is increasingly critical for businesses, governments and individuals in order to fully seize the opportunities presented by these trends—and to mitigate undesirable outcomes.

 

AI: Impact on the labor market

 

Whenever we discuss AI, opinions usually vary widely. The issue always separates those who believe that AI will make our lives better, and those who believe that it will accelerate human irrelevance, resulting in the loss of jobs. It is important to understand that the introduction of AI is not about replacing people but expanding human capabilities. AI technologies enable business transformation by doing the work that people are not doing so well – such as quickly, efficiently and accurately processing large amounts of data.

 

The relationship between humans and AI reinforces each other. Although one of the analyst studies suggests that around 30% of global working hours could be automated by 2030, AI can help by taking on the monotonous and repetitive aspects of current workers’ work. Meanwhile, these employees will focus on the types of work that are more strategic or require a more analytical approach. However, this also requires the retraining of the existing workforce at a certain level.

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This new way of working has begun to affect the job market: in fact, it is expected that the development and deployment of new technologies such as AI will create millions of jobs worldwide. In the future, millions of people will either change jobs or acquire new skills to support the use of AI.

 

AI skills: The Gap

 

While the AI ​​will be responsible for a significant transformation of the labor market, there is currently a gap between this opportunity and the skills available to the current workforce. When companies experiment with AI, many realize that they do not have the proper internal skills to successfully implement it. For the workforce, new education and skills are needed to adapt jobs to the new opportunities of AI. In return, new trainers are needed. AI technologies require the development and maintenance of new advanced systems. People with knowledge and experience in these new areas are in demand.

 

There is currently no agreement on who will take the responsibility to qualify current and future workers. Companies, governments, academic institutions and individuals could all be held responsible for the management of this retraining. To meet the current and future demand for AI, companies should create opportunities for their current employees to continue extra education-training so that they become the group of workers who will monitor and manage the implementation and use of AI with human and machine interaction. Only when all these different groups take responsibility, the workforce will be able to effectively develop the necessary AI skills and take the companies to the next level.

 

In the change of time

 

In summary, one can safely say that sooner or later, AI will lead to a redesign of workplaces. We assume that innovative options can be harnessed in more and more industries.

Above all, AI is a transformative force that needs to be channeled to ensure that it benefits larger organizations and the social cause. We should all be overwhelmingly involved and elaborate in making the most of it.

GDPR: Artificial Intelligences’ Major Blockage

The data protection and privacy law, which came into effect across the EU on 25thmay have a great impact on companies building machine learning systems. We know that in order to build these systems, companies’ needs large amount of data, but Big data is completely opposed to the basis of data protection.

 

According to the EU Data Protection Regulation, companies must meet three specified transparency requirements (along with other suitable safeguards) in order to better inform data subjects about the Article 22 (1) type of processing and the consequences:

 

  • inform the data subject purpose of data storage;
  • provide meaningful information about the logic involved; and
  • explain the significance and envisaged consequences of the processing.

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The logic behind is to be aware of how far this transparency provision is interpreted and whether companies have to fear or not.

 

AI is omnipresent: From the analysis of large and complex data sets such as genome data in medical research, Predictive Policing in the police and security sector to digital language assistants such as Apple’s Siri or Alexa of Amazon. Even fitness apps are increasingly relying on the use of AI and machine learning in order to be able to offer each user a tailor-made training plan optimized for them.

 

This trend has not gone unnoticed by politicians. After the European Commission presented a European concept in the field of artificial intelligence at the end of April, the parliamentary groups also took part on 26 June 2018 , to discuss over recommendations for action in the handling of artificial intelligence – especially in legal and ethical terms – by the summer break in 2020. The AI ​​concept of the European Commission also provides extensive research and development measures with the aim of promoting AI innovation in Europe.

 

Even if the use of AI does not necessarily have to be associated with the evaluation of personal data in every case of application, for example in banking and insurance, it also stays suitable for the comprehensive evaluation of personality traits (so-called “profiling” / “scoring”). According to European data protection authorities, an example of profiling and classifying is the following:

 

“a business may wish to classify its customers according to their age or gender for statistical purposes and to acquire an aggregated overview of its clients without making any predictions or drawing any conclusion about an individual. In this case, the purpose is not assessing individual characteristics and is therefore not profiling.”

 

It is therefore astonishing that the concepts of the EU Commission with regard to the data protection measurement of AI use have so far remained rather vague for companies.

 

Regardless of the admissibility of a particular procedure, these transparency obligations are often seen as extremely critical in the light of the protection of trade and business secrets. The reason for this is that the person concerned must also be provided with “meaningful information about the logic involved” and it is still unclear to what extent and to what amount this information is to be given. The key question is whether the person in charge, ie the company using the AI, is only required to explain the principles and essential elements underlying an automated decision-making process descriptively, or whether the disclosure of calculation formulas, parameters and algorithms can actually be demanded from this.

 

In any case, with the view expressed here, there is no obligation to disclose formulas and algorithms from the GDPR. The transparency provisions of the GDPR therefore only require “meaningful information about the logic involved” of automated decision-making, but not the actual publication of these logics. According to this, the responsible party owes only a description of the principles underlying an automated decision-making process, that is to say about the fundamental laws by which an algorithm makes decisions. The purpose of the GDPR obligations is therefore not (as often represented) to enable the concerned person to recalculate the results of an automated decision-making process, for example the “score” of the concerned person. This would require, for example, the specific calculation formula and the calculation parameters. Rather, in the context of the transparency provisions, for example in the context of a privacy policy, the data subject should only be given the opportunity to obtain advance information on the extent to which his data is processed by a particular service provider and, if appropriate, to look for alternatives.

 

This view is not contradicted by the requirement of “meaningfulness” of the required information. On the other side, for the average user, a comprehensible description of the underlying processes may represent a greater added value than the disclosure of the mathematical-technical logics themselves. Only by then a generally understandable description can meet the requirements of the GDPR. This requires that all information to be provided must be provided in an intelligible form and in a “clear and simple language”.

 

In summary, the GDPR lurks no real danger for the protection of know-how. Rather, their admissibility requirements and transparency obligations in the use of automated decision-making are consistent and appropriate: Human individuals should not become the ordinary “ball” of machines. If machines make automated decisions without being checked by professionals for precision, it can lead to insignificant results as well.

Human Machine Partnership – Is 2018 the year of #MachineLearning?

Human Machine Partnerships2018 is all about the further rapprochement of man and machine. Dell Technologies predicts the key IT trends for 2018. Driven by technologies such as Artificial Intelligence, Virtual and Augmented Reality and the Internet of Things, the deepening of cooperation between man and machine will drive positively the digitization of companies. The following trends will and are shaping 2018:

 

Companies let AI to do data-driven thinking

 

In the next few years, companies will increasingly use the opportunity to let artificial intelligence (AI) think for themselves. In the AI systems, they set the parameters for classifying desired business outcomes, define the rules for their business activities, and set the framework for what constitutes an appropriate reward for their actions. Once these sets of rules are in place, the AI systems powered by data can show new business opportunities in near real time.

 

The “IQ” of objects is increasing exorbitantly

 

Computing and networking items over the Internet of Things are becoming increasingly cost effective. The embedding of intelligence into objects will therefore make gigantic progress in 2018. Networked device data, combined with the high levels of computing power and artificial intelligence, will enable organizations to orchestrate physical and human resources automatically. Employees are becoming “conductors” of their digital environments and smart objects act as their extension.

 

IQ of Things

 

AR headsets ultimate comeback in 2018

 

Its economic benefits have already been proven by augmented reality (AR). Many teams of designers, engineers or architects are already using AR headsets. Whether to visualize new buildings, to coordinate their activities on the basis of a uniform view of their developments or to instruct new employees “on the job” even if the responsible instructor cannot be physically present at the moment. In the future, AR will be the standard way to maximize employee efficiency and leverage the “swarm intelligence” of the workforce.

 

AR headsets

 

Strong bond of customer relationship

 

Next year, companies will be able to better understand their customers through predictive analytics, machine learning (ML), and artificial intelligence (AI) and use these technologies to improve their customer first strategies. Customer service will perfectly maintain the connection between man and machine. It will not be first-generation chatbots and pre-made messages that address customer concerns in the service, but teams of people and intelligent virtual agents.

 

Deeper Relationship with Customers

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The “Bias Check” will be the new spell checker

 

Over the next decade, technologies such as AI and Virtual Reality (VR) will enable those responsible to evaluate information without prejudgment and make decisions in an entirely balanced way. In the short term, AI will be used in application and promotion procedures to bring out conscious or unconscious prejudices. VR is increasingly being used as an interviewing tool to cover the identity of applicants with the help of avatars. “Bias checks” – “prejudice checks” – could become the standard procedure in decision-making processes in the future, just as spell-checking is today when it comes to writing texts.

 

Bias check

 

The mega-cloud is coming up

In 2018, an overwhelming majority of companies will adopt a multi-cloud approach and combine the different cloud models. To overcome the associated cloud silos, the next step will be the mega-cloud. It will interweave the different public and private clouds of companies in such a way that they behave as a single holistic system. With the help of AI and ML, this IT environment will be fully automated and consistently evaluated.

 

mega-cloud

 

IT security is becoming more important than ever

 

In today’s increasingly connected world, IT security companies need more than ever to rely on third parties. They are no longer individual instances, but parts of a bigger whole. Even the smallest errors in any of the connected subsystems can potentiate to fatal failures in the entire ecosystem. In particular, for multinational corporations, it’s a must in 2018 to prioritize the implementation of security technologies. This development is further fueled by new regulations, such as the GDPR regulation of the EU.

 

 

E-sports gaming industry ready for mainstream

 

Not least driven by virtual reality, the phenomenon of e-sports for companies in the media and entertainment industry 2018 finally become a fixture. Millions of other players and viewers are jumping on the bandwagon and making continuity e-sports mainstream for 2018. This phenomenon is representative of a bigger trend: even original physical activities such as sports are digitized. In the future, every business will be a technological business, and people’s free time will be shaped by networked experiences.

 

“People have been living and working with machines for centuries,” says Dinko Eror, Senior Vice President and Managing Director, Dell EMC Germany. “In 2018, however, this relationship is reaching a whole new level: man and machine will be more intertwined than ever, and that will change everything – from the way we do business to the design of leisure and entertainment.”

Data Analytics Trends for 2018

Using data profitably and creating added value is a key factor for companies in 2018. The world is becoming increasingly networked and ever larger amounts of data are accumulating. BI and analytics solutions and the right strategies can be used to generate real competitive advantage. Here below are listed the top tends concerning Data Analytics of 2018.

 

How new technologies support analysis

Learning (ML) technology is getting improved day by day and becoming the ultimate tool in creating in-depth analysis and accurate predictions. ML is part of the AI that uses algorithms to derive modules from structured and unstructured data. The technology supports the analysts with automation and thus increases their efficiency. The data analyst no longer has to spend time on labor-intensive tasks such as basic calculation, but can deal with the business and strategic implications of analysis to develop appropriate steps. ML and AI will therefore not replace the analyst, but make its work more efficient, effective and precise.

 

Natural Language Processing (NLP)

According to Gartner, every second analytical query on search, natural language processing (NLP) or language should be generated by 2020. NLP will allow more sophisticated questions to be asked about data and relevant answers that will lead to better insights and decisions. At the same time, research is making progress by exploring ways in which people ask questions. Results of this research will benefit data analysis – as well as results in the areas of application of NLP. Because the new technology does not make sense in every situation. Their benefit is rather to support the appropriate work processes in a natural way.

 

Crowdsourcing for modern governance

With self-service analytics, users from a wide range of areas gain valuable insights that also inspire them to adopt innovative governance models. The decisive factor here is that the data is only available to the respective authorized users. The impact of BI and analytics strategies on modern governance models will continue in the coming year: IT departments and data engineers will only provide data from trusted data sources. With the synchronized trend towards self-service analytics, more and more end users have the freedom to explore their data without security risk.

 

More flexibility in multi-cloud environments

According to a recent Gartner study, around 70%of businesses will implement a multi-cloud strategy by 2019 in order to stop being dependent on a single legacy solution. With a multi-cloud environment, they can also quickly define which provider offers the best performance and support for a given scenario. However, the added flexibility of having a multi-cloud environment also adds to the cost of allocating workloads across vendors, as well as incorporating internal development teams into a variety of platforms. In the multi-cloud strategy, cost estimates – for deployment, internal usage, workload, and implementation – should, therefore, be listed separately for each cloud platform.

 
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Increasing importance of the Chief Data Officer

With data and analytics now playing a key role for companies, a growing gap is emerging between responsibilities for insight and data security. To close them, more and more organizations are moving to analytics at the board level. In many places, there is now a so-called Chief Data Officer (CDO) or Chief Analytics Officer (CAO), who has the task to establish a data-driven corporate culture – that is to drive the change in business processes, overcome cultural barriers and the value of analytics to communicate at all levels of the organization. Due to the results orientation of the CDO / CAO, the development of analytical strategies is increasingly becoming a top priority.

 

The IoT innovation

The so-called Location of Things, a subcategory of the Internet of Things (IoT), refers to IoT devices that can calculate and communicate their geographical position. On the basis of the collected data, the user can also take into account the location of the respective device as well as the context that may be involved in the evaluation of activities and usage patterns. In addition to tracking objects and people, the technology can also interact with mobile devices such as smartwatches, badges, or tags, enabling personalized experiences. Such data makes it easier to predict which event will occur where and with what probability.

 

The role of the data engineer is gaining importance

Data engineers make a significant contribution to companies using their data for better business decisions. No wonder that demand continues to rise: from 2013 to 2015, the number of data engineers has more than doubled. In October 2017, LinkedIn held more than 3,500 vacancies under this title. Data engineers are responsible for extracting data from the company’s foundational systems so those insights can serve as decision-making basics. The data engineer does not just have to understand what information is hidden in the data and what it does for the business. He also has to develop the technical solutions to make the data usable.

 

Analytics brings science and art together

The use of technology is getting easier. Everyone can “play” with data today without having to have deep technical knowledge. Researchers who understand the art of storytelling are pursued for data analysis. More and more companies see data analysis as a business priority. And they recognize that employees with analytical thinking and storytelling skills can gain competitive advantage. Thus, the data analysis brings together aspects of art and science. The focus shifts – from simple data delivery to data-driven stories that lead to concrete decisions.

 

Universities are intensifying data science programs

For the second time in a year, the Data Scientist ranked first in America’s annual Glassdoor ranking of the best jobs in America. The current report by PwC and the Business-Higher Education Forum shows how high applicants with data knowledge and analytical skills are in the favor of employers: 69% of the companies surveyed indicated that they would prefer suitably qualified candidates over the next four years instead of candidates without appropriate competencies. In the face of growing demand from employers, it is becoming more and more urgent to train competent data experts. In the United States, universities are expanding their data science and analytics programs or establishing new institutes for these subjects. In Germany too, some universities have begun to increase their supply.

Artificial Intelligence vs Human Intelligence

Whatever the encouraging results and the progress of #ArtificialIntelligence (#AI) the world can see, we are far from the development of an intelligence such as human intelligence. More and more studies show the major importance of our sensory relation to our environment.

In his book, Descartes’ Error, neuroscientist Antonio Damasio writes that “Nature appears to have built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it “. In other words, the human thinks with all his body, not just with his brain.

This need of physical survival in an uncertain world can be at the root of the suppleness and power of human intelligence. But few AI researchers have really embraced the implications of these ideas.

Artificial Intelligence vs Human Intelligence

The motivation of most artificial intelligence algorithms is to conclude patterns from vast data sets – so it could require millions or even billions of individual cat’s photos to gain a high degree precision in the recognition of cats in general, for example.

But when a human confronts a new problem, most of the hardest work has already been done. In a way that we are just beginning to understand, our body and brain have already built a model of the world that we can apply almost instantly to a wide range of challenges. But for an AI algorithm, the process starts at zero each time. There is an active and important line of research, known as “inductive transfer”, based on the use of knowledge previously learned by machines to illuminate new solutions. However, as is, it is doubtful that this approach is capable of imitate something like the richness of our own body models.

 

If Stephen Hawking’s caveat that smart machines could put an end to humanity is relevant, technology is still far from proposing something that is more or less approaching human intelligence. And it will be impossible to achieve this goal if experts won’t think carefully about how to give the algorithms a kind of long-term relationship and embodied with their environment.

 

Currently #AI has beaten humans in poker, but not to forget that a computer can’t win at poker if great set of algorithms aren’t behind it. The victory of AI proves the ability of a computer program to learn from human to surpass him but doesn’t mean that human intelligence has been left behind by the artificial intelligence.

 

At the same time that the American Gafa (Google, Amazon, Facebook, Apple), the Chinese BATX (Baidu, Alibaba, Tencent and Xiaomi), car manufacturers and all industries and services make the bet of artificial intelligence, Would it be necessary to conclude that future sounds the death of human intelligence?

 

Will we be, in near future, led by robots that have become, thanks to humans, more intelligent and more effective in managing our lives? Or, conversely, is not the victory of a computer program ultimately only the success of computing and calculating power … and nothing else than that. This question will be asked repetitively and it’s up to each individual to decide the answer we want to give.

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We must not forget that #ArtificialIntelligence only helps us in our daily lives, by assisting us in various activities that we are moreover willing to entrust to more efficient and technical than we do to realize them. For example, to order a lunch/dinner, to chat with an after-sales service or to drive a car is in no way the demonstration of talent, intelligence or the human mind, nor of the domination of artificial intelligence. Is it not, moreover, the characteristic of human intelligence to know how to invent machines to replace it and make it better in such activities?

 

The recent success of “chatbots”, these small programs able to make the machine interact with the man thanks to the artificial intelligence, testifies in the same way. Thanks to them, it is now easier to manage your gas bill or know the terms of voting at an election. And tomorrow, they will allow us to dialogue with our car, our house and all connected objects of our world …

 

For it is ultimately the primary meaning that must be restored to artificial intelligence: to be at the service of human. Whether it deals with the recognition of voice, images, movements or their interpretation, artificial intelligence is only a tool, designed by humans, to render a service more efficient and to free man from the constraint of Activities entrusted to him.

 

Emotion, the shielded hunt of the human

True, technological breakthroughs frighten us as much as they fascinate us, but we have the certainty that human intelligence will remain dominant as long as it knows the only thing a machine can never transmit: emotion. This means that artificial intelligence can only assist us with functions and tasks that do not involve emotion and feelings. The man will then take over the artificial intelligence.

 

It is also in this case that technology may well make us much more human by reminding us of our irrevocable comparative advantage: our ability to experience feelings, to understand others, to anticipate their expectations, to guess their Fears … No machine is capable of feeling emotions with the finesse, precision, delicacy but also the weakness and sometimes the charm of the emotional intelligence of man.

 

Faced with the increasing irruption of artificial intelligence into everyday life, the man-machine relationship “remains to be defined”, but leave impression that the “upheaval will be profound”.

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