Artificial intelligence is used in many places in everyday life: when typing text messages, the smartphone provides alternatives for the next word, e-mail or chat programs suggest answers, translation programs facilitate the writing and understanding of texts. Here, the results are already of high quality.
Companies and public authorities use AI increasingly, and more and more organizations are interested in it. According to a BITKOM (Germany’s Digital Asssociation) study, one out of ten companies in Germany wants to invest in AI in 2023. AI processes are getting better with more data available. However, according to search and AI expert IntraFind, organizations should keep a few things in mind:
1. Targeted use of AI
No organization should use AI just because it's "in" to do so. Rather, companies should define goals how they want to improve their processes and what they want to achieve. Together with AI experts, it is important to consider where AI can provide useful support: Which processes can be automated or partially automated? At what point human intervention is needed? At what point can my organization use AI to do routine work up front?
In most cases, AI can cover a particular activity in a process particularly well. Here, it is then far superior to human capabilities. The surrounding tasks in the process chain often must be done by humans, but the one task that AI does increases the efficiency of the overall process enormously.
Therefore, AI always requires a specific use case and must be deployed in such a way that it brings more benefits to people inside and outside your organization: to the employees in companies or public authorities who use AI and, to the customers and clients on the other side of the process.
2. AI requires preparation
Statements around the topics of machine learning and artificial intelligence often sound like it's all very simple and effortless. This assessment is deceptive. The introduction of artificial intelligence requires thorough preparation as well as regular monitoring and readjustment. This starts with which method you choose to achieve your goal. Not every algorithm is suitable for every problem. And it all depends on the database. An AI model is only as good as the underlying data. Moreover, in a dynamic economy, business requirements change. AI systems must be able to adapt to these changes.
3. AI needs high-quality data
AI usually requires huge amounts of data. This is because artificial intelligence uses machine learning methods to recognize patterns in data, derives rules from them and applies these rules to new situations. To achieve good results in practice in a company with less data, experts like IntraFind use transfer learning. Here, pre-trained machine learning models are applied to and trained for a special case in a similar kind of problem with less data.
To work with these data, they must be collected, analyzed and, if necessary, processed accordingly. Data management is necessary to provide AI with high-quality data. Machine learning can support data management, which in turn provides a foundation for AI systems.
For some generally valid analysis goals, it is also possible to fall back on already trained models - in this case, the manufacturer guarantees the quality of the training data in advance. For example, to be able to analyze the data pool regarding GDPR-relevant data, an extractor must be able to capture qualitatively difficult data. With machine learning, intelligent enterprise search software can automatically enrich existing data with relevant metadata and classify them into predefined topics.
4. Setting up AI requires qualified personnel but using AI does not
To develop AI, experts are needed who, in addition to classic computer science knowledge, have in-depth knowledge of machine learning, deep learning and neural networks in combination with Big Data, among other things.
However, there are ways that non-experts can further train AI. For example, with AI-based software for contract or document analysis, any user can train the AI by annotating and labeling text passages and thus continuously improve it.
5. AI must be transparent and explicable
Artificial intelligence must be explicable and comprehensible to strengthen trust in its use. For specialist departments and those involved in decision-making processes, there must be transparency about the usage of the technology. They must be able to understand and assess the basic modes of operation of AI systems. At the same time, it is also important to demand transparency from vendors, who must demonstrate that their solutions are based on sound models and flawless data sets.
In this context, the European Union's efforts under the Digital Services Act (DSA) should also be mentioned. For this purpose, the EU is establishing the European Center for Algorithmic Transparency. It is intended to contribute to a safer, more predictable, and trustworthy online environment for people and companies. https://digital-strategy.ec.europa.eu/en/news/digital-services-act-commission-setting-new-european-centre-algorithmic-transparency
Conclusion: Organizations that take all this into account are already one step ahead. AI can bring them considerable added value: Speed up processes, relieve employees of standard tasks, and improve business models.
