Artificial intelligence (AI) is changing the economy and society through numerous applications: Voice control, chatbots, automated fraud detection, text analysis within seconds, self-driving cars, … the list will be even longer in the future. And the more applications that are used, the more important it becomes for companies to embed the many solutions in a correct AI strategy.
One of the most interesting AI applications is pattern recognition. Intelligent software is able to scan large amounts of unstructured data such as texts, numbers and images reliably and in a short time and to evaluate the content. There are many areas of application, no matter whether research, production, market research, process optimization, compliance or processing. The big advantage for companies is the automation of activities that usually cost a lot of work and bring little added value.
However, the potential extends beyond pattern recognition. The number of AI applications in companies and public administration will rise sharply by 2025 – also because many technologies are mature. At the same time, the number of processes supported by AI is growing. This, however, makes the management of AI solutions much more complex. There is a risk that the total return on investment of all AI applications will be lower than desired if solutions are implemented in isolation. This is where strategy comes into play.
Checking and establishing AI readiness
Even if the AI added value is generally positive, companies should always review the situation critically in individual cases due to the huge number of possible applications. Each time, the question arises whether scenarios can be implemented or whether companies first have to make far-reaching changes to their IT infrastructure and processes before AI can be used with full efficiency. Even if there is added value, this does not mean that the bottom line is that implementation pays off.
The significance of AI should not be underestimated. The fields of application mentioned are so broad that the software could take over tasks in practically any area of a company. Thus also structures of an enterprise are questioned. Even if an application with added value is identified, it has to be clarified how it can be implemented within the company and which adaptation of the structures is necessary.
The examination and production of the AI-Readiness in the enterprise is not to be underestimated thus a preliminary work, before it becomes concrete: Typical questions that companies should answer are:
- Are the databases AI-capable?
- Is there enough data with which we can feed the algorithms?
- Are there suitable interfaces for the AI connection?
- Are the employees qualified?
IT strategy and AI strategy belong together
Investments in Artificial Intelligence are long-term and extensive works. Therefore, no company without a strategy should enter the AI world, otherwise there is a risk of bad investments. Such an AI strategy should not be formulated separately from IT and corporate strategy: AI projects should also go through the four proven strategic phases:
- Use Case Determination
- roadmap creation
As with other strategic plans, companies need a clear overview of what steps are needed in the existing organization to achieve AI readiness. The analysis includes, among other things, a detailed assessment of indicators of what needs to be improved and optimized in the company. These indicators could be, for example:
- a high IT-related manual effort in processes,
- the use of large amounts of data
- or rule-based procedures.
Based on the information of this analysis, the processes in which the use of AI achieves the greatest benefit can be identified. It also determines the environment affected by AI integration, such as departments and external partners and suppliers.
It is also important to find out whether an identified problem in the company can be adequately solved with the help of AI procedures. To do this, it is worth comparing the problem with known and proven AI applications. Either there is already experience, or a company bases its selection on external use cases. By comparing these use cases with the identified problems or processes in the company that can be improved, suitable AI applications can be selected for these tasks. The first selection is made without prioritization.
This then comes in the next step: the evaluation. The use cases are compared with the situation in the company and synergies and interdependencies are sought. On the basis of this calculation, a roadmap is then drawn up. The roadmap includes a project plan that leads to an efficient use of AI in the identified areas where these technologies will benefit the company.
In addition to purely technical aspects and process requirements, an AI strategy also includes culture, organization and integration management. At the cultural level, care must be taken during implementation to ensure that artificial intelligence is not seen by employees as a competitor but as a tool. To this end, understanding of the function and purpose of this technology should be promoted. In organizational management, the value of AI results must be weighted. And the integration must adapt to a new way of introduction (learning), a process that is not finished with the productive switching.
AI strategy as a long-term investment
A procedure for setting up an AI strategy outlined in this form enables companies to systematically approach the use of artificial intelligence from the outset and thus increase added value overall. Especially when companies have completed their first AI projects and the use of AI is increasing, it is important that the individual projects are interlinked.
Over the next few years, AI solutions will become an increasingly important part of a company’s IT infrastructure. The aim of a coordinated approach is to use synergies from different AI projects and applications and to prepare the ground for a complete, AI-based IT landscape in a timely and targeted manner. Companies that think about a detailed AI strategy at the beginning will get more out of their AI solutions in the long run.