In the future, many companies will also rely on artificial intelligence when designing their business processes. This will be accompanied by a great need for change management in companies, but the consulting industry will also have to adapt to this.
The director Spike Jonze tells us about artificial intelligence in his 2013 film "Her", which seems a bit crazy. Theodore Twombly, the main character of the film, falls in love with Samantha, an operating system on his computer with which he communicates via headset and video camera. Samantha is artificial intelligence. She learns about the social interaction with Theodore and her behavior becomes more and more human. In the end, Theodore falls in love with artificial intelligence and even starts an intimate relationship with Samantha.
Of course, we are talking here about a movie that does not claim to correspond to reality in all respects. But how far are we from such a scenario in 2019, half a year after a man in Japan married a hologram? How intelligent is artificial intelligence today and how well can it communicate with us?
What is artificial intelligence anyway?
In order to answer these questions, there is another question that must be answered first; what is artificial intelligence? This is not easy, because the term artificial intelligence, AI for short, is not uniformly defined - not least because AI research, since its beginnings in the 1950s, has been interdisciplinary. For practical application, however, the following definition has proven to be useful; "Artificial intelligence is the characteristic of an IT system to show human-like, intelligent behaviors.”
Many systems that we consider intelligent do not rely at all on artificial intelligence. Dictation functions and voice control are helpful for us, but not an expression of intelligent speech comprehension. So far, they have been working with preset keywords that control certain functions, such as "set alarm clock to 7 o'clock" or "lamp on".
In more detail, the term artificial intelligence describes computer science applications aimed at showing intelligent behavior. This requires the following four core skills:
- act and
They extend the basic principle "input, processing, output" of all EDP systems. Artificial intelligence should support people in reaching their goals - not make them superfluous. In order to be able to support people optimally, however, the core skills mentioned are necessary. The really new thing about today's AI systems is learning and understanding.
Learning machines and systems - for humans?
Modern systems can be trained in the processing component to achieve better results - usually better than with conventional methods. The latter are essentially based on rigid, clearly defined and firmly programmed sets of rules (if... then...). Examples of this are image and speech recognition. AI systems, on the other hand, not only recognize letters in an image, they also know what the word "complaint" in a scanned letter means and can initiate a complaint process. And they are often much more powerful than humans - for example, when it comes to searching millions of websites and providing all the images that show a dog. That would be impossible for humans.
Modern AI systems are currently used to supplement a clear system control. The special thing about them is that they learn during the test phase and during operation on the basis of their errors or feedback.
Machines learn similarly to humans
In principle, machines learn similarly to humans. For example, a computer program can learn to recognize certain objects. To do this, it is first fed with data and then trained. It is told, for example, which object is a horse and which is not. The program then receives regular feedback from the programmer as to whether it made the right distinction between "horse" and "no horse". This feedback is used by the algorithm to improve itself until it finally makes a sure distinction between "horse" and "no horse".
Machine learning systems usually consist of three components:
1. A model that predicts and identifies
2. Parameters, i.e. signals or factors used by the system to make decisions
3. The learning system.
The learning system adjusts the parameters and thus the model by looking at the differences between the prediction and the actual result.
At the beginning of the model, a forecast is often made that applies to a particular situation. In the beginning, the results often deviate from the forecast. The system must therefore learn. To do this, it continuously checks the data fed in and learns from them. Mathematical algorithms are used to adjust the original assumptions and thus further optimize the model.
AI in (service) companies
Service companies, such as banks and insurance companies, are already investing a lot of time and money in artificial intelligence. For example, they rely on AI disciplines such as Robotic Process Automation (RPA), knowledge management software, digital assistants and predictive analytics. They see the future benefits of artificial intelligence above all in contact with customers. With the help of AI, products and customer approach are to be designed more individually. However, today's customers are still mostly served by personal customer advisors; the AI only has a support function, so to speak, because it is often strange for people to speak with a computer voice that does not understand our natural language. However, at the Developer Conference 2018, Google introduced Google Duplex, a technology that makes it possible to make natural calls and perform real tasks over the phone. The video featuring Google Duplex was viewed on YouTube with over 2.5 million views*. It's easy to see when you're watching: people can't tell the computer voice from the voice of a real person, and the system understands and responds to real human speech. For example, it arranges a hairdresser's appointment for the person he or she is talking to or orders a restaurant table.
AI systems take over human tasks
It is therefore conceivable that in the future the system will also be used by companies - not just banks and insurance companies - to record and process orders, complaints or damage reports by telephone, for example. The system constantly learns from the callers' feedback.
The British mathematician and computer scientist Alan Turing, one of the most influential theorists of early computer development, developed the Turing test in 1950. It is intended to determine whether a computer has a capacity for thinking comparable to that of humans and is therefore no longer distinguishable from humans. If a human questioner no longer recognizes whether he is communicating with a human being or a machine, the test is passed. Based on this criterion, Google Duplex has passed the Turing test, because: At least in the video, the people at the other end of the telephone line do not notice that they
are speaking with artificial intelligence.
However, the Turing test is controversial because it only tests the functionality of a system, but not whether artificial intelligence also has a consciousness and an intentionality. Moreover, in its basic form, it is merely based on a conversation via keyboard and screen: It does not involve auditory or visual contact between the participants. Google Duplex is only about acoustic contact. Nevertheless, Google Duplex passed the test at first glance because people on the phone can no longer distinguish between man and machine.
But has the test really been passed? That remains questionable, because, Google Duplex can only make calls it has received thorough training in the relevant area. The system cannot make general calls. In addition, communication has so far been limited to less than a minute. That's why Google Duplex didn't actually pass the Turing test, because the system would get into trouble if it had to have a longer conversation or a conversation about another topic.
Nevertheless, accepting damage reports or complaints, for example, could be a future AI field of application - provided the system has been trained to do so. This would be possible for other topics. This also results in possible applications in and for change management.
Importance for Change Management
The importance of the AI topic for change management should be considered from two sides. On one hand, the introduction and use of artificial intelligence in the corporate context is associated with a change process. This must be designed and accompanied. On the other hand, however, the consulting industry itself must also be open to the use of AI, because in the future it will also play an essential role in consulting (not only) on the topic of change.
The use of artificial intelligence inevitably requires a process of change in companies, and according to a survey conducted by Forrester in 2017, a large number of decision-makers see this as a major hurdle, at least if the goal of using AI is work previously carried out by people is now taken over by machines, because people are then only needed at the beginning to train the system.
In addition to a change in business processes, this also entails a change in corporate culture. For this reason alone, the affected people in the respective companies must be involved in the process. What exactly this means must be decided on a case-by-case basis. There is no patent recipe for accompanying such a change. This makes it all the more important to realize that the introduction of artificial intelligence inevitably requires targeted change management.
The financial sector is currently already embroiled in such a change - partly because FinTechs is questioning the market power of traditional institutions in many areas with its agile and customer-oriented way of working. For this reason, the established institutes will also increasingly rely on AI in the future and thus try to distinguish themselves as technologically innovative service providers.
Consultants need to deal with AI deployment
In addition to the financial services sector, the consulting industry is also affected by the change. They also have to think about the use of artificial intelligence, because the interface to the customer also plays a central role in it. So far, the possible applications of such systems as Google Duplex are still very limited, but this will change in the near future.
Let's stick to the example of the customer interface. Suppose a consulting firm is asked for a change process. Then the first thing to do is to understand the customer's concern. This requires telephone calls and a good analysis of the situation. So far, telephone calls have either been conducted by a back office or by the consultants themselves. These then arrange appointments on site to take a closer look at the situation and to get a concrete picture.
What would it be like if artificial intelligence were to carry out this analysis by telephone in the future? Imagine the following scenario: You as a customer call and ask for a change management consultation. Your request will be answered by an artificial intelligence, which will already ask you the important and correct questions in order to clarify your concern. The system then immediately connects you with an expert for this. This saves you time and analysis costs - even if the expert or consultant carries out another detailed analysis afterwards.
AI elements flow into the consulting process
The current existing AI systems cannot yet perform complex (telephone) analyses and consultations. However, there are already systems that support such processes. These are not yet AI in themselves, but a preliminary stage of it. Such a system is also used by Dr. Kraus und Partner (K&P) in the field of change management.
Due to our many years of consulting experience, our organization has a great deal of expert knowledge in this area. All K&P consultants and clients should benefit from this. Therefore K&P stores the individual and collective knowledge as well as the experiences made on the topic Change in a software. However, this concentrated know-how is only interesting for customers as far as it corresponds to their needs. They would quickly be overwhelmed if K&P, speaking pictorially, would simply pour out all its knowledge on their desk and leave them alone with it - possibly with the remark, "You simply have to tell us what you need".
Instead, with the help of the Change Management System, customers are shown the various options based on their situation. Customers then decide for themselves which options to choose. In the next step, they are shown the various alternatives for the selected option, from which they again select the options that are relevant to them. In this way, the system guides customers step by step through the key questions they should ask themselves in their current situation. This allows customers to immediately address their concerns without having to understand the whole world of change management. And the K&P consultants?
They are better able to enter into conversations with their customers in advance.
In its current version, the system still works without artificial intelligence. Rather, it is continuously filled and adapted with the knowledge of the K&P consultants. However, its functionality is similar to that of AI systems. This means that system branches of the software program that have proven successful are retained; branches that have been less effective, on the other hand, are adapted and improved.
AI helps to design customized solutions
K&P has had extremely positive experiences with the use of this system so far. Regardless of the size of a project, it helps to quickly get an overview of the customer's project or concerns and to develop customer-specific solutions. The feedback from customers is correspondingly positive, as they are usually only interested in the consulting knowledge they need. And does it turn out in the course of the consulting process that another option should be considered and thought through? Then all you have to do is run through the software program again based on the current level of knowledge and understanding.
K&P is convinced, among other things, by the experience gained with the use of the current software. "The use of AI - also in the consulting process - will continue to progress in the future, and both internal and external change consultants have to adjust to this. On the one hand, because the consulting itself must change and be prepared to use new technologies, and on the other hand, because it must be able to accompany the change processes resulting from the use of AI by customers; regardless of whether these take place at the level of the strategy, structure or culture of a company.
Arm yourself now for the future
The current state of development of the AI does not yet permit any conclusive application scenarios. However, it reveals numerous possibilities for accelerating business processes and making them more efficient in the future. However, it is important to become familiar with the possibilities and requirements of using AI-supported systems now in order to identify possible areas of application at an early stage and to prepare their operational use. This strategic preparation can decide which players will be among the winners in the increasingly dynamic corporate environment and which will disappear from the market due to technological disruption.
Whether we humans will understand artificial intelligence so well in the future that we will fall in love with it, as in the film "Her", remains to be seen and, above all, left to each individual. But one thing is certain: it will accompany us even more intensively in the future than it already does.
This blog was translated from the following article:
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