Artificial intelligence (AI) now has a decisive influence on decisions in almost all areas of life. The technologies that make this possible are developing rapidly and are becoming more attractively priced. So why is it that the vast majority of companies have only used AI in pilot projects or in a single business process?

Numerous experts explain the technical components of artificial intelligence (AI) much better than I do. As a strategist, I am always fascinated by the technical possibilities of AI. Often, however, I also experience the limits and barriers; less the technical than the cultural and organizational.
In the 10 ethical guidelines for the digitization of companies it says under point 9: Artificial intelligence should be designed in a value-oriented manner.
Intelligent systems should be designed in such a way that people’s basic rights are protected and that they can have a good and successful life. When developing and using artificial intelligence (AI), ethical principles must be taken into account (value-based design). In order that automated decisions do not result in paternalistic effects that restrict the freedom of action of people, constant system control and the possibility of intervention in the system are required.

The following key questions are therefore in the foreground for me in this blog post:

  • What do employees expect from AI?
  • What role does emotional intelligence play?
  • How can AI be used properly?

Employees’ expectations of AI

If a company plans to introduce artificial intelligence (AI), the most important factor – people – is usually left out. Instead, the focus is on technical issues and the costs of the technology.
Interesting insights into the human factor are provided by a survey by the Wegofive initiative led by Professor Andreas Moring from the International School of Management ISM in Hamburg:

  • Employees want their managers to demonstrate important skills in working with AI – social and emotional intelligence, empathy and knowledge of human nature.
  • Employees are fundamentally willing to take on more responsibility and constantly develop new tasks and goals themselves. To make this possible, managers should – in line with the corporate culture! – guarantee an area of freedom.
  • The trust between manager and employee is rated more important than trust in the AI ​​applications and systems.
  • Employees expect transparency on the data used by the AI ​​in order to be able to assess the new technology and its results.
  • Employees want clear ethical rules for assessing AI prognoses and specifications, as well as permission to disregard the system – that is, to remain “master of the process”.

Role of emotional intelligence

Managers and affected employees are increasingly realizing that emotional intelligence – and thus skills such as self-reflection, self-management, social awareness, relationship management and communication skills – are core competencies for success in the digital age.

A study by the Capgemini Research Institute came among other things to the following interesting results:

  • Companies that have employees with high emotional intelligence enjoy considerable advantages: On average, 60 percent of the companies surveyed have higher profits of more than 20 percent from their employees with the said skills. The most important quantitative advantages include increased productivity, higher employee satisfaction and a growing market share.
  • Companies that invest sustainably in emotional intelligence achieve a ROI between 2.2 and 4.4 times, factoring in the effects on sales, productivity, costs and fluctuation.
  • Companies must prioritize emotional intelligence primarily in the areas of recruitment, training, remuneration & promotion, as well as culture in order to build crisis-resistant teams in a VUCA world.

Usage instructions for AI

The following information seems important to me with regard to cultural and organizational barriers:

  • It takes a “big picture”: Companies have difficulties moving from pilot projects to company-wide programs, for example from focusing on individual business problems, such as improved customer segmentation, to major tasks, such as optimizing the entire customer journey. For this, well-established thought patterns and working methods that oppose the use of AI must be consistently questioned and, if necessary, abandoned.
  • Interdisciplinary collaboration is required: AI can have the greatest impact when it is developed by cross-functional teams in which different skills and perspectives come together. Required data must be named and saved in such a way that they can be used by everyone. The solution is an internal platform for AI that all teams can access.
  • Correct data is required: Three data sources should be emphasized: (1) Customer data, including e.g. purchasing behaviour, (2) data about one’s own products or offerings, (3) industry and competition data.
  • Data-supported, evidence-based decisions are required: If AI is widely adopted, employees at all levels of the hierarchy expand their own judgment and intuition through the recommendations of the algorithms. In this way they arrive at better solutions than humans or machines could find on their own. The traditional top-down approach is obsolete!
  • Experts from the AI ​​field are needed to open up the new areas of application: research scientists, data scientists and research engineers. It is advisable to start with a data scientist who understands the possibilities AI offers and who can develop specific solutions tailored to the company’s customer group.
  • It takes a willingness to experiment: Rarely an idea is fully developed at the beginning of an AI initiative. A trial-and-error mentality is strongly recommended. Feedback from the first users can flow into the next version. This eliminates inconsistencies before they turn into costly problems. This accelerates development and enables small AI teams to develop products in a functional basic version within a short period of time (minimum viable products).
  • A corporate culture is required that, according to the Dale Carnegie study, focuses on three areas of work: trust on the part of managers in the workforce, transparency in communication about the use and purpose of AI, as well as support and training for employees.

Conclusion

Artificial intelligence is the most important accelerator of the second wave of digitization, which will lead to profound changes in all branches of the economy and in our everyday lives. For the first time, data is not only stored, transmitted and processed in a machine-readable manner, but the digital content is also understood by AI so that decisions can be supported on a knowledge-based basis.

The step from functional to interdisciplinary teams brings together different skills and perspectives at the beginning and the user input that is necessary to develop effective tools. Over time, employees across the company adopt new forms of collaboration. The closer they work with colleagues in other functions and areas, the greater the dimensions in which employees gradually think – from solving isolated problems to designing new business models. The pace of innovation is picking up speed as others in the company gradually adopt the learning from experience approach that has successfully driven the pilot projects. As AI tools spread across the company, those closest to the action are increasingly empowered to make decisions that were previously with their managers. This leads to flatter hierarchies in the company, which in turn encourage cooperation and encourage thinking in even larger dimensions.