10 Tips to Get Started with Decision Intelligence

Artificial Intelligence (AI) enables companies to make better decisions. Decision Intelligence (DI) helps to increase sales and reduce costs. Here we show you how to make your business fit for the future and get started with Decision Intelligence in ten steps.

1. Decision-making diagnosis: In order to establish the starting point for all future actions, begin by diagnosing the current state of decision-making within your company. At which point is the decision process so complex that it has become unmanageable? Where is there a lot of data but only a few insights? Where is there an opportunity to merge multiple decision-making silos?  
2. Identify goals: Based on your answers to the above, define the top use cases where DI can be implemented most easily or quickly, or where AI-powered decision-making can have a significant impact on your business. Examples of relevant cases include customer and product recommendations, dynamic pricing and warehouse optimization. Articulate the most important decisions in your business that serve these goals as concrete questions, for example, “How much of product ‘A’ should I buy each week?”.
3. Acquire tools: Having identified the business use cases, you can now determine which technology you will need to purchase and which tools and models you will use to achieve your defined goals. This is where paretos sets new standards. With the help of an AI-based Software-as-a-Service platform, companies ranging from start-ups and mid-sized businesses to large corporations can perform comprehensive data analyses without the expertise of data science specialists or even any prior experience whatsoever. A real game changer to help you get started with Decision Intelligence.
4. Collect data: On this basis, you can now use existing data sources or access new ones. Do not plan a decision just by looking at the available data in front of you, but always by looking ahead at the business outcome you expect from this decision. When it comes to AI-based decision-making, all data from every available source is compiled, compared and evaluated with the specified goal utmost in mind. The data is retrieved from internal KPIs, external criteria (such as weather data and traffic volume) and unstructured data (such as images, audio and video files from social networks).
5. Break down silos: Data-driven decision-making processes can only be effective if every department and team within the company is able to define their own decision goals, have access to all analysis results and are empowered to interpret and use them. This requires breaking down all silos within your company by removing the IT Department’s monopoly of handling all data and interpretation and sharing the responsibilities with the entire organization.
6. Learning by doing: DI is not a one-time process. It is essential for businesses to continuously optimize their approach on the basis of constant feedback. Tools such as paretos provide valuable insights into which factors effect pricing and customer behavior and how they work. Based on these instant insights it’s possible to adjust parameters flexibly and in line with business requirements.
7. Not all DI is the same: AI-based decision-making can be divided into three categories: Decision Support that backs up human decision-making with analytics and data exploration. Decision Augmentation that makes available recommendations and predictions that are already created from the analyzed data. Decision Automation that involves machines performing both decision and implementation autonomously. Categorize your decisions into these three groups using a matrix diagram that considers frequency on one axis and complexity on the other. The simplest and most frequent of these decisions should be automated, whilst the most complex and least frequent should be supported.
8. Create a DI mindset: Develop new habits and routines within your organization by encouraging decision-makers to systematically apply the best practices. These include critical thinking, trade-off analysis, recognizing bias and allowing other opinions.
9. Trust the AI: DI is based on data-driven decision-making which works with facts. Sometimes a recommendation could seem at odds with the gut feeling of a decision-maker, especially if they don’t understand the technology behind it. Michael Feindt, strategic consultant and founder of Blue Yonder, a supply chain management technology company, has observed many times how employees can struggle to accept that their instincts are wrong. The solution, he believes, is to involve at least one person who understands how analytics work and is trusted by management in these types of decisions.
10. Scale DI: The initiation phase to get started with Decision Intelligence is over and customized tools have been applied and optimized, step by step, for the appropriate cases. Now is the time to apply the DI approach to decision cases throughout the entire company. Again, the procedure corresponds to the previously mentioned steps: goal analysis, data collection, categorization according to the decision intelligence matrix and iterative adjustment of the parameters.