The dynamics of modern markets make many traditional decision-making strategies of companies from trade and industry look ‘old’ in the truest sense of the word.
For sustainable and accurate decision-making, the perfect combination is the coordinated interaction of Artificial Intelligence (AI) and human intuition. In this context, the availability and ease of use of data is of particular importance. Before launching a new product, for example, the most extensive information possible must be obtained from the customer database, any existing competing offers, expected costs and the availability of resources. Modern AI-supported methods have proven particularly helpful in extracting usable insights from the huge volumes of data generated by this process.
How does DI improve business decisions?
The newest tool to ensure accurate decision-making is Decision Intelligence. It extends the traditional powers of descriptive business intelligence to include trend detection using predictive analytics and provides recommendations for action using prescriptive analytics. Depending on your requirements, these recommendations can be taken into account by human decision-makers or implemented by AI in a fully automated way. Such AI-based decision-making combines the respective capabilities of humans and machines – thus achieving better business results with informed decisions.
Combining the very best skills of human and machine
AI is essential when large volumes of data from different sources and formats need to be combined and examined for correlations in an unbiased manner. Because of the unprecedented volumes of data currently being generated, such a task exceeds human processing capacities – especially when analyses are required in real time or at least as quickly as possible. In addition to this, our decisions are too often shaped by unconscious perceptual biases.
For example, we tend to subconsciously prefer the existing and the familiar or to be guided by stereotypes but consciously believe ourselves to view reality objectively. Because of this, it is quite common for humans to incorporate information that is based on guessing or not even included in data sets – not to mention emotional feelings – into their deliberations. Therefore, merging the limited capabilities of humans with the speed, logic and lack of emotion of machines by leveraging AI-based decision-making is a promising path to successful business decisions.
The correct type of DI for every use case
In the context of Decision Intelligence, a distinction is made – in ascending order of AI autonomy – between three different modes of cooperation between AI and human decision-makers:
- Decision Support involves the use of AI just to prepare data and provide easy-to-understand analyses. All decisions are still made by humans.
- Decision Augmentation is mid-level where AI formulates predictions and makes recommendations that are then reviewed and approved by human decision-makers before any action is taken.
- Decision Automation is the highest level. With this, the courses of action to be chosen are based on the analyses and selected by AI and then executed automatically. Of course there will always be unexpected outcomes and possible risks so humans still monitor the process and its effects.
Which mode delivers the best results will depend on the specific application scenario. For example, extensive automation is ideal for optimizing inventory in distribution warehouses, for campaigns of recommendation in marketing or for dynamic pricing. In contrast, greater human interaction is more appropriate when consolidating business relationships with long-standing suppliers and customers or selecting suitable personnel for a newly advertised position.
A new data culture for higher-quality decisions
Companies should first identify which use case can benefit most from AI-assisted decision-making and then set specific goals for that area, such as:
- Increase in revenue
- Using fewer resources
- Development of new customer segments
In this process, all stakeholders for this area must be involved so that the broadest possible database is created and all silos are broken down. To make the analysis and evaluation of data as straightforward as possible, paretos’ Software-as-a-Service platform is the ideal solution: With interfaces to a large number of business applications, it facilitates the input and structuring of data and involves the use of DI even for business users who have no expert knowledge of Data Science.
What does “a new data culture” mean?
Such use of AI, even outside of traditional IT, should be accompanied by the introduction of a new data culture. This means understanding the limitations of humans in dealing with large amounts of data and trusting in fact-based decisions of AI, a continuous evaluation of processes and a move toward flatter decision hierarchies. Successful use of AI-based decision-making can thus become the decisive step in making a company fit for the requirements of data-driven business.