AI transformation refers to the redesign of business models and organisations based on artificial intelligence. In this article, you will find the most important fields of action and measures for successfully shaping the AI transformation in your company.
What does AI transformation mean?
An AI transformation in a company goes far beyond the use and pure application of artificial intelligence. Rather, AI transformation means setting a holistic framework for the value-adding use of AI. On the one hand, this means demonstrating how AI can improve your business from an economic and entrepreneurial perspective. It also means creating the organisational and cultural conditions to anchor AI as a competence within the company. This allows companies to benefit operationally from increases in efficiency and productivity and strategically from new insights, ideas and the resulting innovations.
AI use cases in the company
Which use cases are relevant for your company and the drivers of your AI transformation is, of course, primarily a question of your business model. Here you will find a few general and specific use cases on how AI is transforming processes, organisations and business models. Which use cases are relevant for you is the result of your digital strategy.
-
Predictive maintenance: calculation of maintenance dates based on machine and quality data.
-
Systems, automation technology: AI models write software for controlling systems, e.g. Siemens.
-
Financial forecasts: Insurance companies and banks use AI to create financial forecasts.
-
Software development: The use of AI increases the efficiency and scalability of software teams.
-
Digital assistants: With AI, digital voice input and AI-supported processing take on a whole new meaning.
-
Automation of simple routine tasks, the processing of structured and unstructured data
-
Corporate management: AI supports and flanks complex decisions and the processing of data.
-
Marketing and creative work: Already today, generative AI can create images, write texts or support creative brainstorming.
-
Customer support: Automated and immediate processing of customer enquiries, without waiting times.
-
Knowledge management: Dissolution of knowledge silos and easy access to internal knowledge that is often just lying around somewhere unstructured.
Successfully shaping AI transformation
Like any transformation in a company, AI transformation does not happen by itself. Rather, AI transformation requires strategy and leadership so that AI can realise its full potential in the company. Here are a few steps and tips.
Understanding the basics of AI
Shaping AI transformation means understanding AI. This fundamental understanding is the basis for the value-adding use of technologies. This does not necessarily mean that every employee will become an AI professional. However, decision-makers in particular should have a basic understanding of how AI works.
-
AI works with data, and data can be structured or unstructured in different formats
-
AI can generate new content based on the content provided (generative AI such as ChatGPT), execute certain automated processes based on rules and optimise itself based on feedback.
-
AI can also capture and process large amounts of data quickly and provide users with effective access or offer decision support.
-
AI thrives on input and interaction. In addition to the data provided, this also includes feedback and user interactions (prompts).
In principle, there are hardly any limits to the imagination for the use of AI, as we are only at the beginning of AI development today. After all, all AI models are still considered ‘weak AI’.
Develop AI strategies
Based on a fundamental understanding of AI, you can now strategically assess the impact of artificial intelligence on your organisation and your business model. I often encounter the phenomenon that supposedly every problem can be solved ‘with an AI’. The digital all-purpose weapon against analogue processes and business models, so to speak. However, you should be able to assess and prioritise exactly what your AI transformation and strategy can look like. It is not at all intuitive or clearly visible where the greatest levers for your AI transformation lie.
Which core processes are particularly critical to success?
-
In which areas are there bottlenecks, which work steps are particularly labour-intensive or error-prone?
-
Which of these problems and tasks are suitable for the use of AI? If a basic understanding of an AI exceeds your digital expertise, then you should consult external consultants and AI experts for this rough initial technical assessment.
-
On the basis of which metrics (costs, speed, etc.) do you assess the actual and an ideal target state or the success of the AI?
-
How can you calculate a business case for the use of AI on this basis?
Once you have answered these business questions, you will be well prepared to discuss business and technical strategies.
Building AI solutions and your own AI asset
Finally, a third step in a successful AI transformation is to acquire AI solutions or build up your own technical assets. There are basically two different strategies:
-
AI in third-party software. This means that providers such as Microsoft or SAP integrate artificial intelligence into their software. Here you are literally spoilt for choice and can decide which of these external solutions best serves your strategy.
-
Build your own assets and model. A very advanced strategy is to build your own technical assets and AI models, e.g. based on open source projects such as Meta Llama.
Building competences and empowering employees
A final and perhaps the most important part of the AI transformation is that you empower employees to work with AI in a targeted manner. As you have seen from the example of software developers, the use of AI is not necessarily a sure-fire success. It starts with allaying employees’ fears that an AI could replace them. In a study, Deloitte found that around 1/3 of all respondents are afraid of using generative AI, while two thirds of employees are curious (remember early adopters & laggards). This goes hand in hand with creating time and opportunities to try things out so that employees learn how to use AI for their own benefit and that of the company. Ideally, you should have your own AI coaches or even an entire ‘enabling team’ to help employees realise their full AI potential. After all, you need to constantly optimise and develop your AI offering, because AI developments happen far too quickly for that.
Conclusion – The AI transformation won’t wait for you
What applies to digitalisation in general applies to the AI transformation in particular: The last one to bite is the proverbial dog. Even more than other digital technologies, artificial intelligence has a strong transformative potential. The earlier you start, the greater your head start. I have done multiple courses about AI, provided by the Vanderbilt University on the learning platform Coursera, which you can see here.
