Learning from the AI pioneers
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In the survey for the Digitalization Engine 2020, 68 percent of company representatives whose companies do not yet have any AI initiatives in place confirm that they do not know how to approach AI. If this problem paralyzes you and leaves you inactive, you are missing the boat and a great opportunity for your company.
Companies that already have AI initiatives are leading the way. They approach the topic of AI with the help of external consultants (42%), gather information at trade fairs (25%) and enter into partnerships (24%). Start-ups are also in demand as partners for collaborations or exchanges (20 percent). However, AI is not a theoretical phenomenon.
For example, 21% of respondents are showing drive and experimenting with AI in their own "special projects" or innovation labs. The AI pioneers also have a head start when it comes to data requirements in companies.
While the dependence of successful AI on the availability, quality and traceability of data is recognized by the average respondent (62%), companies with AI consider this to be much more fundamental (99%). Without data, AI is simply not possible.
Despite the immense importance of data, availability (43%), quality (39%) and analysis skills (37%) are generally still underdeveloped. In many companies, this is preventing the successful launch of AI projects.
Companies without AI initiatives have a lot of catching up to do here. Comprehensive data management concepts are also gaining in importance as a result, but are not yet fully developed.
Around one in five use a holistic data management concept (23%). 16% of respondents state that they are not prepared to systematically collect or analyze data for legal reasons such as the GDPR.
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Good news for the so-called latecomers: it's not too late to get to grips with the topic of AI. These companies should start by identifying suitable internal and external multipliers and discussing use cases and pilot projects. It is important not to be discouraged in the initial phase by the lack of processes and responsibilities for the use of data. It is also important for companies attempting their first AI projects to avoid making a specific mistake - failing because their expectations are too high. It is important to define realistic expectations for AI initiatives. Source: BearingPoint