What is decision automation?

Decision automation uses artificial intelligence, data, and business rules to help organizations automate the decision-making process across several areas. Using automated decision-making increases productivity, and there is a reduction in risk and error in the decisions made. There is a level of consistency maintained across all decisions, which is not found when it is left to an individual or group of decision-makers.

Decision automation is usually applied to routine and repetitive decisions that are part of an organization’s daily functioning. Most of these tend to be operational decisions that support the everyday functioning of an organization.

What drives decision automation?

Operational decisions are generally driven by a knowledge of business rules and can be influenced by or dependent on data and other relevant information. There are also a few advanced cases where the techniques of using business rules and data will have to work in tandem to automate a decision.

Rule-based decision automation

Decision automation can be based entirely on business rules. In sectors such as finance, repetitive decisions can be automated to ensure consistency in quality. In the insurance sector, decisions such as claims approval and premium pricing can be automated. Business rules-based automation becomes critical in decisions that are made in highly regulated environments.

Data-driven decision automation

In this case, the decision is based not on rules but rather on how a particular situation is unfolding and the uncertainty surrounding it. For example, when quoting for car insurance, ascertaining how risk-prone the client is can be a factor riddled with uncertainty. These are cases where the predictive model works best in decision automation, using factors such as age, sex, and the car type to make predictions based on past claims. Combining these predictions with rule-based decision automation makes them more effective.

This data could be in the form of current information in the business, statistics, and intelligence available externally, material from the Internet of Things, social media, or almost any online source.

Applications and benefits of decision automation

There are several applications for automated decision-making; however, there are three main organizational problems that can benefit from automation.

Smart workflows

In the process of automated decision-making, workflows have to constantly adapt to new information coming in from a range of inter-connected data points. Each of these data points is chosen and set up based on specific properties they possess. Automated decisions take place based on the analysis of changes seen in the several data point properties. When this happens seamlessly, workflows become smarter.

Automatic error prevention and recovery

An automated workflow system can recognize potential errors, making decisions to repair or respond without needing human intervention. These levels of automation immediately speed up spotting and responding to errors, making systems faster and more robust.

Regulatory and contractual compliance

Automated decision-making also aids in regulatory compliance. An example is an audio set-up for a theatre. If decibels reach prohibitive levels, an automated decision will reduce it to regulation levels. This level of decision-making and automated action can increase compliance with business rules and industry regulations.

Business advantages of automated decision-making

There are several advantages to automated decision-making for a business.

Fast, error-free, consistent decisions

The primary advantage is the ability to make quick decisions that are error-free. It also ensures consistency, by offloading human decision-making to an automated process. This allows for the artificial intelligence to self-correct and take remedial measures without the constant need of supervision. This automation is constantly applied to every business decision, 24/7, leaving no room for inconsistency or error.

Better utilization of staff

By reducing dependency on human intervention for repetitive tasks, employees can concentrate on jobs that will improve revenue generation and open up new opportunities.

Increase in compliance

By bringing down the occurrences of regulatory or contractual errors, organizations can save themselves from fines for non-compliance. Decision automation does not eliminate all chances of error, but it does reduce the chance significantly.

Challenges with decision automation

Managing environments and security

Organizations need to ensure that automated decision environments are safe from any form of disruption, whether internal or external. At the same time, systems must remain flexible and handle any new sources of data. With an increase in security breaches and increasingly complex networked environments, vulnerabilities are constantly on the rise.

Solution: The simplest way to ensure safety is to monitor and control decision automation at any given time. Strong data security and IT systems are non-negotiable and need to be constantly under review.

The need for human intervention

Some decisions require human intervention, and ideally, decision automation should have a fully automated process with some leeway for human input. This could be built into the automation process, but the drawback here is that it introduces a disconnected experience. This means that there is no end-to-end view that ensures everything needed to make a decision is in one place.

Multistep and long running decisions

Decisions are often instant, but there are some cases where one decision may depend on results from other related decisions. These are called multi-step decisions. When interdependent decisions roll from one into another, these are called long-running decisions. These can be complex and difficult to follow.

Maintaining data control

Organizations have to stay agile while maintaining data control, retrieval, and storage for decision automation applications. This is essential for auditing. Considering that the Internet of Things generates massive amounts of data, and this volume grows by the day, managers need to constantly develop strategies to be able to handle new sources of data.

Adequate support to automated decision-making environments

Organizations must remember that deploying decision automation requires strong business rationale. They also require appropriate resource allocation. Poorly implemented systems may work against the organization’s purpose completely, creating unneeded decision criteria or incorporating too many other sources of data.

Managing ambiguous goals

The bottom line with any organization is to make better decisions that create profitability. Creating ‘good decisions’ is an ambiguous task.

Solution: Defining the criteria of a ‘good decision’ is the first step in removing ambiguity. Then it’s a matter of identifying rules and monitoring all possible decision outcome variables such as the rules applied to decision automation and the necessary assumptions made to help link the data that all come together to determine the quality of decisions.

Choosing between a machine, person, or hybrid approach

The role of humans in the decision-making process needs to be constantly assessed. Many advocate for a hybrid person-digital approach to decision making, particularly when there is limited or ambiguous data. In such cases, alternative options that use shared decision making and decision support are better solutions.

Solution: Decision automation needs to have rules that help people get involved when it comes to creative aspects or those based on assumptions, nuance, ethical, or judgment calls. It is best to have decision automation systems that come with a set of referral rules on when to consult a human.

The benefits of decision automation are plenty if they are utilized and implemented in the right manner. The worry that such moves will eliminate human work positions is not a concern, mainly because it is the ingenuity of humans that evaluates decision automation at every level. Computers, while being able to make decisions based on data, cannot utilize ethics and morals, and therefore, humans will always have a role to play.

Decision Automation Diagram