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Automated-Decision-Intelligence to empower millions of data workers to use the power of AI

There are more people out there who use data to take business decisions, than those who actually work with data. Researchers say there are 1 to 2 million data scientists and nearly 5-10 million data and business analysts, but an estimated 50-65 million data workers take business decisions based on data. Such decision makers often use rudimentary tools like spreadsheets. Automated Decision Intelligence is targeted at this last but most important group of people – with low data literacy, but who want to use data to drive decisions.

There is a clear indication that automated decision intelligence will become a foundation of analytics for business decision-makers. It will be used for competitive differentiation in order to better address the requirements of individuals with low data literacy who need business insights to make data-driven decisions.

Automated Decision Intelligence software is typically deployed as a cloud service and employs Machine Learning (ML) and Artificial Intelligence (AI), crunching very large volumes of data to find anomalies and outliers – and more often than not the reasons behind them – and deliver the results to decision-makers in easily consumable formats.

Results are delivered via email or application alerts, for example, rather than via BI dashboards. Business-friendly explanations using natural language generation (NLG) to communicate the insights by explaining them in plain English are another essential aspect of these offerings. Real-time alerts driven by automated anomaly detection are currently garnering the most interest from users.

The heart of the Automated Decision Intelligence process is to bring AI into the workflow as a primary processor of data. For routine decisions that only rely on structured data, we’re better off delegating decisions to AI which is less prone to human’s cognitive bias. While humans are removed from this workflow, it’s important to note that mere automation is not the goal of an AI-driven workflow. Sure, it may reduce costs, but that’s only an incremental benefit. The value of AI is in making better decisions than what humans alone can do. This creates step-change improvement in efficiency and enables new capabilities.

Removing humans from workflows that only involve processing of structure data does not mean humans would become obsolete. Many business decisions depend on more than just structured data. Vision statements, company strategies, corporate values, market dynamics are all examples of information that is only available in our minds and transmitted through culture and other forms of non-digital communication. Such information is inaccessible to AI, but extremely relevant to business decisions.

The key is that humans are not interfacing directly with data but rather with the possibilities produced by AI’s processing of the data. Values, strategy and culture are our ways of reconciling decisions with objective rationality. This is best done explicitly and when fully informed. By leveraging both AI and humans, we can make better decisions than using either alone.

For example, AI may objectively state the right inventory levels in order to maximize profits. However, a company may opt for higher inventory levels to provide a better customer experience, even at the expense of profits. In other cases, AI may determine that investing more dollars in marketing will have the highest ROI among the options available to the company. But a company may choose to temper growth in order to uphold quality standards. The additional information available to humans in the form or strategy, values, and market conditions can merit a departure from the objective rationality of AI.

Moving from data-driven to AI-driven is the next phase in our evolution. Embracing AI in our workflows affords better processing of structured data and allows for humans to contribute in ways that are complementary. We’ll surely see the emergence of new companies that embrace both AI and human contributions from the inception, and build them natively into their workflows.

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