AI Artificial Intelligence
Artificial intelligence is not smarter than the people that use it. However, when used properly, it shifts your data analysis capabilities into high gear by greatly speeding up information processing and improving the quality of the results.
We take a pragmatic approach to AI. We support you in identifying use cases, selecting methods, preparing the relevant data, integrating AI into processes with a perfect fit, and we help you maximize employee acceptance. At the end of the day, you will achieve much better results.
Small Effort, Big Impact
AI is a highly versatile tool that can be used to implement a wide variety of projects. Fraud detection, binarization, natural language processing and legal tech – all are powered by AI, but each use case is unique. The key to unlocking this power is hidden beneath the surface.
But no worries: Your data holds answers to almost any business question. To unlock these answers, AI must connect your questions with the right answers. We help you devise and implement the best method for this.
AI is not a robotic entity, but a cutting-edge means to an end. At the same time, AI methods are so versatile that they can be used throughout all industries and scenarios. However, there is no one-size-fits-all solution.
Why this matters:
AI is a master of incremental improvement and disruption. That’s what makes it so very potent, because it allows organizations to cut costs while at the same time outmaneuvering their competitors. The AI calculation: faster + cheaper + better = more successful.
Don’t think about the future, think about today: Which processes are sluggish, which tasks involve a high degree of manual action? A small AI solution can provide intelligent answers to pressing questions. Welcome to the future.
From Use Case to Dedicated AI Solution
In AI and data analytics projects, the preparatory stage is critical: You first define the application scenario, then perform analyses and prepare all necessary data. The AI model is the dessert that is served last – with icing on top.
AI is not the best solution for every task – simple statistical methods will do the job in many cases. Analyzing the issue and the financial benefits will reveal the best approach.
Where does the data come from, is it already available, is it in the right format or does it need to be generated first?
A first simple evaluation of data, for example on gender distribution, can help to discover missing and implausible values.
Data is prepared for processing by a machine learning engine. The better the information quality of the features, the more effective the training.
Select an Algorithm
Will statistical methods, neural networks or decision trees best solve the problem? A small step with a huge impact.
Create a Model
The task concerns a narrowly defined subject area that requires specialist know-how. In this stage, you create the model and optimize it according to the objective.
AI models are constantly checked to see whether they deliver the desired results when processing real data or whether they need to be readjusted.
Finally, the model is put into operation and embedded in an automated process to optimize business processes.