The technology exists, and it holds incredible possibilities. Beyond the possibilities of generative AI, industry can also benefit from neural networks and the existing mathematical models. Especially in digital transformation, the opportunities opening up here can no longer be ignored. We have therefore given some thought to how digital transformation works in times of artificial intelligence and what concrete added value it can create in industry. STRG.AT now has 20 years of experience supporting companies on this path. We have learned which concepts work and what is required for successful integration. But we have also learned where the pitfalls are and what companies regularly stumble over.
Through the application of artificial intelligence, digital transformation goes further than it has to date. Alongside modern structures, it often also requires a changed corporate culture:
Radical thinking and questioning existing practices and processes
Innovation and experimentation as a culture within the company
Continuous improvements and optimizations
All these approaches must follow a clear guideline. They must be able to intervene in the company’s operating business. The goal is to improve productivity and make the company leaner and faster. This requires an agile culture, but above all also a clear “Reason Why”, a clear strategic objective, and use cases that are capable of measurably improving the company’s operational situation.
Some Project Examples
We have selected only a few examples here that seem essential to us for industry. There are, of course, many other use cases for which there can be no Out-Of-The-Box solutions and which must be integrated deeply into the existing systems.
Some Project Examples
We have selected only a few examples here that seem essential to us for industry. There are, of course, many other use cases for which there can be no Out-Of-The-Box solutions and which must be integrated deeply into the existing systems.
The Strategy and the Use Cases
Digital transformation needs a clear strategy. It must be able to pursue objectives and also be measured against those objectives. This requires clear metrics in which evaluation can take place and to which everyone can refer consistently.
Based on this need, we work with our partners to develop use cases that describe a possible integration of AI models into the company process. These must contribute to the company’s operational results. It is not about using AI. It is about driving new potential in the company’s economic perspective through the use of AI. That is how success is created.
Projects - Projects - Projects and Rapid Prototyping
In many companies, lengthy strategy processes are initiated. What is often overlooked is that the integration of AI and digital transformation in times of AI must be fast if it is to succeed. We have therefore developed an approach in which we test ideas with Proof-Of-Concept projects that can be implemented with limited budgets. If they can be implemented successfully, they are then pursued further. This creates a rapid sequence of implementations, tests, and trials.
Data Analysis
Prototype (Proof Of Concept)
Visual Prototype
Finished elaboration with graphical interface and Production-Ready
This methodology also makes it possible to achieve the highest possible cost efficiency. This step-by-step approach is used primarily in projects whose feasibility has not yet been 100% secured and where possible failure or project termination is possible. In data projects, for example, where patterns are being sought, this is of course always possible.
Data Applications
Almost everything that can be done, and that is essentially the objective of deep integration of digital capabilities and, even more so, artificial intelligence, ultimately has to do with the use of data that is available within the company. Artificial intelligence opens up large fields of application that can intervene in operational processes. Few people and employees are aware that data applications follow an 80:20 rule. This means that only 20% of the effort will really lie in the development of algorithms and mathematical processes. The majority is always spent on cleaning the data and, above all, making it accessible so that it can be worked with. Just a few years ago, people said that data was the new oil. I no longer see it that way today. Data is an absolutely necessary foundation for all these applications. No AI is capable of “beautifying” data mathematically; the principle of Shit-In / Shit-out applies. If anything, data is crude oil that has to be refined to make it usable. But: Do not wait for this data to be available in cleaned form. We will have to clean it in joint processes as part of the projects. Otherwise it will never happen. Ultimately, however, added value is only created when we are able to process, combine, compare, and truly visualize this data and these results:
AI Guidelines
The use of artificial intelligence in digital transformation requires guidelines and strategies that go beyond digital transformation. The European Union’s AI-Act sets out guidelines that companies in Europe must comply with. Data can often reveal unexpected patterns that a human would never have seen “with the naked eye”. These insights may conflict with applicable law. For this reason, we have developed guidelines with several companies defining the areas in which artificial intelligence may and should be used. Likewise, the areas in which it must very clearly be avoided.
In this area, we also work with lawyers who assess our concepts and, where necessary, recommend a more in-depth review by a commission. Especially in the area of critical infrastructure, the requirements are rightly very strict. Costly mistakes must absolutely be avoided in this context. In principle, we advise all companies to create a two-part guideline specifying the areas in which artificial intelligence should be used.
Summary
The necessary steps overlap and, in part, run in parallel. Always make sure to begin concrete implementations as quickly as possible. Frustration with long strategy processes can kill the entire momentum. At the same time, starting from management, work must be done on the corporate culture, because
Develop the Digital Transformation Strategy Together
AI Guidelines for the Entire Company
Conceptual design in the subareas while simultaneously developing PoCs (Proof of Concept)
Overall concept and architecture
Implementations and initial visualizations (interfaces)
Completion and rollout of models and systems across the corporate group