This section covers the design concepts for intelligent business systems and machine learning in SAP business software.
An intelligent SAP system aims to empower the user to achieve a better, more informed result. When preparing to design apps with AI, ask yourself the following questions before starting with the detailed application design:
- Who will be using your application?
- What context do the users work in?
- What tasks are they trying to complete?
The most important thing is to find the sweet spot where AI adds value, that is, where AI capabilities overlap with the user needs. For example, AI that finds suspicious postings amongst many thousands of records can be of immense value for an accountant.
Once you’ve identified the areas that would benefit from AI support, you can start to think about how to best apply AI enhancements. This article outlines the guiding principles, concepts for involving users, and typical application patterns you need to be aware of to make informed design choices.
Principle 1: Human in control. In a business environment, actions triggered in a system have a tangible outcome in the real world that impacts the goals and profits of the company. Because the responsibility and accountability for these actions still lies with the human user, humans must always remain in control of the outcome.
Principle 2: Augment human capabilities. To gain the user’s trust and foster successful adoption, an intelligent system should aim to upskill human experts, rather than replacing them. Providing better transparency and efficient tools for decision making process, integrating user feedback, and presenting information in a way that makes it easier understand are all measures that extend the power and reach of the individual. By contrast, hiding information, simplifying the truth, or reducing the number of options without sufficient transparency puts the user at the mercy of the system. The user must be able to understand and control the intelligent system.
Principle 3: Ethically aligned design. Machines do what they are told to do – there is no moral judgement in an algorithm. Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications. We need to establish clear and binding ethical rules that intelligent systems must obey. We support the IEEE Global Initiative for Ethically Aligned Design.
Principle 4: Efficient automation. There’s a common misconception that artificial intelligence inevitably means automation. But this depends on the business case and what you want to achieve through automation. We believe that intelligent systems should reduce the effort a user needs to invest to get something done. This means defining the right level of automation for each use case. Where full automation is not feasible, we should aim for greater efficiency. By combining automation with better use of existing information, transparency, and learning effects, intelligent systems can help users to obtain the same result with fewer steps.
When designing intelligent systems that employ machine learning models, how we communicate and engage with users is at the forefront. Two aspects are important here: explainable AI and establishing a feedback loop.
Explainable AI: Build trust and empower users
One of the main design principles for intelligent systems is to empower end users. This can be achieved by providing sufficient information about the underlying model, and explaining the reasoning behind the results of an algorithm. Empowerment helps to build trust between human and machine.
For more information, see Explainable AI.
Feedback loop: Learn from users for users
Feedback is the communication channel between your users, your product, and in the end also your team. Using feedback is a powerful and scalable way to improve the product. It is one of the key sources to improve the product and the way it performs.
For more information, see User Feedback.
This section provides a very general overview of the typical machine learning task types.
Categorization and classification: Assign datasets to predefined groups.
Example: As a service agent, I want to classify the priority of incoming requests (high/medium/low) based on their content to improve customer service.
Clustering: Discover new groups (clusters) and distribute data between them.
Example: As a system administrator, I want to analyze the existing technical role assignments across users to cluster them and create different business roles.
Detection and matching: Assign relationships and detect similarities and anomalies in a given dataset.
Example: As a master data specialist, I want to reduce the number of duplicates in the system during consolidation.
Predictions: Predict future data based on patterns identified in past data, taking into account all potentially relevant information.
Example: As a master data manager, I want to estimate the potential number of change requests my team will need to process in the next quarter to leverage the workload.
Relevance and ranking: Distinguish between relevant and less relevant datasets of the same type in relation to the current context.
Example: As a purchaser, I want to see the top X suppliers for a specific product, in the context of a given purchasing request.
Simulation: Explore the outcome of changing variables in a dataset to play out different what-if scenarios.
Example: As a transportation planner, I want to simulate different transportation plan options for a set of freight units.