AI is here to stay, but …

You can find them everywhere, the (self-proclaimed) AI experts who talk about revolution, efficiency gains, huge potential, intelligent data analysis for better decisions, and so on. Or surveys or even studies are launched, which then confirm, for example, that a large proportion of managers see considerable potential in GenAI... oh, what a miracle... congratulations on this insight. Sorry for the exaggerated description, but the sentiment is pretty much the same when you scroll through LinkedIn, X or other platforms. Basically, the buzzwords mentioned are not wrong, but…

Let's ask  Google's Gemini, what the essence of these studies is and where the great potential lies:

• Increased efficiency: GenAI can automate tasks and streamline processes, saving time and money.

• Improved decision making: GenAI can analyze large amounts of data and identify patterns that humans cannot. This can help managers make better decisions.

• New business models: GenAI can enable new products and services that reach new markets and customers.

Let's start with increasing efficiency: GenAI should be able to automate tasks and optimize processes. For this to happen, the tasks and their data must be available in a digital form. Das Gleiche gilt auch bei der Optimierung von Prozessen. The same goes for process optimization. If a process, or at least some of its artifacts, are not available in digital form, it will be difficult to derive the insights needed to optimize it.

The same systematic approach torpedoes improved decision making. If the large amounts of data are not available (digitally) or are distributed throughout the organization and no one has an overview, the story becomes even more complicated. This is often compounded by other factors such as poor data quality or a lack of connectivity between data silos.

New business models are all well and good, but if the company lacks the appropriate platform to map the business model, or if the existing platform is not flexible enough to accommodate the new business model in a timely manner, then even the most innovative ideas are of no use.

Interim conclusion: If the company is not set up accordingly, it will be difficult to realize the existing potential, with or without AI. And simply licensing Microsoft Copilot Pro is nice, but still far from the revolution that companies are hoping for.

In practice, few companies are set up to use AI in a consistent and value-adding way. It is often used selectively (because the manufacturer of product X has added a quick AI button) and in a patchwork approach. If you really want to realize the potential of this new opportunity, you need a sensible and focused digital strategy and transformation.

A thorough analysis of the existing systems, data and processes, involving the relevant stakeholders and, where possible, the (end) customer, forms the basis of our approach. This shows where and how the data is available and how structured or unstructured the individual sources are.

The second step is to define the vision, taking into account different perspectives such as customer, business and technology. In other words, this is the point at which the form in which the data will ultimately be stored and the systems on which it will be stored are defined. This also leads to the identification of missing or obsolete systems.

In the final part, the concrete steps are poured into a roadmap, taking into account the dependencies, of course with a corresponding investment recommendation for financial planning.

Do you want to prepare your business for the future and lay the foundations for the use of AI technology?  Contact us and we will help you determine your posistion and guide you trhough the transformation.