Augmented workflows focuses on a future of work where humans and artificial intelligence (AI) interoperate as well as on how organizations can prepare for it. Augmented workflows are work processes characterized by the collaboration of humans and AI-based technologies, which changes the scope, focus, or outcome of task accomplishments. Multiple challenges need to be solved in this context. Communication leaders should reflect on how AI-based technologies can (or should) augment practices of human workers and modify workflows in communication units.
When thinking about the future of work, artificial intelligence (AI) will inevitably pop up. While robots taking over the planet will hopefully remain science fiction, a lot of AI applications are already part of our everyday life. Be it the ranking of query results by search engines or the automatic prioritization of emails. Organizations are increasingly working on AI-based technologies with the aim of enhancing business value and obtaining competitive advantage. AI promises, among other things, to improve productivity by performing routine tasks, reducing or elimating human error, and generating insights that improve decision quality.
At the same time, most occupations involve solving a variety of tasks, some of which are easy to automate with AI, some of which are difficult. Therefore, especially in knowledge work (i.e., work which relies on the creation, distribution or application of knowledge), a realistic scenario is that AI-based technologies will augment rather than replace human workers. Thus, AI-based technologies will not take over the tasks of people completely but collaborate closely with human workers. This will change job profiles and modify existing workflows. The design of what we call augmented workflows will affect how human workers respond to the introduction of AI-based technologies and ultimately whether the potential of such technologies can be realized in practice.
Technology is no longer just a tool we use to accomplish a task or achieve a goal. This assumption about the nature of the interaction between humans and technology is challenged by AI. While an interaction between human and technology typically used to be initiated by a human, AI-based technology now can be responsive to the environment. This means that it constantly monitors information from the environment (e.g., acoustic or visual information) and responds autonomously to it. Examples for these responsive systems are the security software in a car or smart speakers listening for their “wake word” (e.g., “Hey Siri”). This brings new problems as responsive systems might be (falsely) activated by unspecific information (e.g., noise) or misinterpret signals without human awareness.
Furthermore, new self-learning AI-based technologies are contextual and adaptive. This means that the functionality of a system will evolve during use. Because AI-based technologies do not explain their decisions or behavior to humans, it is difficult to understand how they work. This could have different implications for human workers, for example skepticism regarding AI recommendations or the impossibility to meaningful question them.
Introducing AI-based technologies promises to create business value. The following examples illustrate possible workflows that could be augmented by AI-based technologies:
How can communication leaders weigh up the opportunities and challenges by AI-based technologies for their organization, its stakeholders, and the business model of the communication department?
The Communications Trend Radar 2023 project was conducted by a research team of the Leipzig University, the University of Duisburg-Essen and University of Potsdam.
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