Whether you start trembling in the face of this metaphorical draft or experience it as a welcome refreshment is, like so many things, in the eye of the beholder and is significantly linked to the ability to envisage future transformations. SMEs in particular must ask themselves today how promptly the implementation and sustainable integration of AI mechanisms should be started. In this article, we want to approach the topic of expert and knowledgeable AI integration: What does AI integration mean? Which aspects need to be considered? And how important is it to anticipate future change in the process?
What is AI integration?
AI integration refers to the process of embedding AI-based mechanisms into the company's own value chain. At first, it doesn't matter exactly where work is being done: from production to sales to customer relations, there are opportunities to be braced by AI components. What generally goes under the label “AI” are actually rudimentary machine learning procedures that are able to deliver tremendous added value, especially when it comes to repetitive tasks. Simple and efficient data consolidation, automated monitoring of access to sensitive information or even the continuous background update of security patches on proprietary hardware are just three examples of how the process of AI integration is worthwhile for almost every company.
AI integration is not a linear process, but rather a scalable endeavor that can take various forms.
In the following, we will take a look at various forms of possible levels of AI integration.
Exemplary stages of AI integration
For the sake of simplicity, we have roughly arranged the various steps according to the strength of the respective AI services, increasing from relatively weak Towards terrifyingly potent.
A first step in implementing relatively weak AI (if you're even willing to talk about one) lies in basic automation: At this level, AI-like mechanisms are used to automate simple tasks, such as processing data or answering frequently asked questions. Sophisticated, self-improving algorithms run in the background, which are based on machine learning logic and are therefore highly specialized.
The next step is about the advanced analysis: This is primarily about identifying patterns and trends within sometimes huge data sets, which can be extremely beneficial for decision-making.
Subtle cuts in analog business activities must be accepted at the next stage, namely when it comes to an emphatic personalization works: AI is used in this context to create personalized, highly individual experiences for or users. This includes, for example, adjustments based on previously shown behavior.
They also already exist autonomous systems: AI-controlled systems can independently perform tasks, such as self-driving cars or autonomous robots in production.
The next level is about natural language processing, or natural language processing (NLP) and corresponding dialogue systems: AI is used in this context to “understand” human-like communication as input and to generate output in a similar way, which is the basis for advanced chatbots and voice-controlled assistance systems.
Die furnishings cognitive systems represents a further step along the said continuum: AI systems can solve complex problems by imitating human-like thinking and corresponding learning processes.
You can immediately follow this (co-) creative processes Be established: Due to increasing humanization, AI is being used heuristically to generate proto-creative content such as visual art, music and/or literature.
Die autonomous decision making including a successive Integration into users' everyday lives is also a double-edged sword: AI is increasingly being used to make decisions independently by analyzing data and making recommendations or even taking direct action.
At the end, there is the speculative vision of a Superintelligence/singularity: Although this is still a hypothetical stage in which AI mechanisms reach a level of intelligence that explicitly exceeds that of the entire human race, a crippling fear of such a scenario is just as foolish as the mindless affirmation of any technological innovation.
Conclusion
As tried to show, there is a veritable continuum of AI; it is therefore a straight granular connection, by no means a monolithic undertaking; individual needs show how far we should actually go. In order to be future-proof, it is of course not insignificant, in addition to anticipating short-term needs, to address the contingencies that really lie dormant under the surface of function-oriented AI mechanisms in the here and now. When planning a viable path, help or advice from outside is not just “nice-to-have”, it is an essential part of sustainable and responsible corporate development.
