Decision intelligence is emerging as a solution that can combine decision management, decision support, and complex systems applications as the urgency to digitize and derive competitive advantage from new technologies like Machine Learning (ML) and Artificial Intelligence (AI) grows.
Organizations must make highly contextualized and accurate decisions with growing speed to deal with unprecedented business complexity and uncertainty. This means that IT leaders must develop the ability to quickly compose and recompose transparent decision flows, a practice called decision intelligence.
To ensure the highest rate of success, businesses require technology that can collect accessible consumer and employee data in a centralized location, interpret context and history, discover future and present bottlenecks, and offer real-time recommendations.
Here are a few strategies businesses can adopt to leverage decision intelligence successfully.
Begin with the Low-Hanging Fruit
Starting with a process that is exceptionally well-defined, low-risk, and has an extensive list of instances is beneficial. Many businesses already have similar procedures in place, but not all of them are entirely automated.
Even if a process has been automated before, adding extra parameters to the decision engine can increase accuracy. The more attributes a company has, the less likely those attributes have been correlated. For instance, considering the time of day or the user’s location can improve a risk scoring decision. However, the most important takeaway is that decision intelligence isn’t a one-and-done exercise. Businesses must adjust their strategies in response to customer input regularly.
Augment Processes for Data Collection
Intelligent systems may not be capable of replacing all decision-making when decision steps are less clear, outcomes are more ambiguous, or there are more risks of making incorrect decisions, but they may be able to augment it.
Decision intelligence’s automation component can be used during the data collection phase of the decision-making process. It isn’t required to reach a final judgment; instead, it can be used to develop reports or identify trends and correlations.
The old method of collecting data manually and preparing reports is no longer viable; businesses require data at a machine scale and real-time.
Be Wary of Biased Data
Decisions can only be as good as the information they are based on. If a company’s history has been problematic, a training set built on that history may inherit the same issues.
People are biased by nature. And they’ll look for data that confirms their prejudices. When people look at data, they do it through the lens of their personal experience with it. The pandemic has taught the world that the past cannot be relied upon to forecast the future. The answer is to provide the appropriate decision-making guardrails.
Utilize Synthetic Data
Synthetic data can compensate for the lack of training data in some instances. Machine learning systems can benefit from synthetic data, which is intentionally generated information precisely modeled for usage in place of real historical data. Companies can utilize it to apply automated intelligence to a wider range of scenarios. It can also help businesses prepare for unusual scenarios.
Tabletop Exercises can be Used to Simulate a Range of Results
Making the best possible decision is nearly impossible in many instances because too many external variables have an overwhelming influence on the outcome. They have the potential to have a significant impact on an enterprise, but they are unpredictable.
That isn’t to say that businesses are powerless. Instead, they can use simulations to prepare for a variety of eventualities. They can also get all the information they need to make the best decision possible.