1. Home
  2. Getting Started
  3. Concepts
  4. What are Operational Decisions?

What are Operational Decisions?

◷ Reading Time: 10 minutes

Operational decisions are specific business decisions made every day within every business. There are millions of these taken – and thousands of different types. Everyday business uses operational decisions to run day-to-day activities using different personnel. Not a day goes by without these types of decisions being made in every business.

By definition, a decision means “a conclusion or resolution reached after consideration. The action or process of deciding something or of resolving a question”.

As evidenced here, a decision has both a conclusion (result or answer) and also a method of deciding or processing, which we call the decision logic. Operational decisions are no exception to this definition. Yet there are distinct differences between operational decisions and other types of decisions (i.e., strategical and tactical) that make operational decisions ideal candidates for automation.

Here are some examples of operational decisions in the day-to-day operations of a business:

  • How much tax should this customer pay?
  • What are the products or services that can be offered to a customer?
  • Is this transaction likely to be fraudulent?
  • How do we handle exceptions in this claim process?
  • Are we compliant with state regulations?

Characteristics of Operational Decisions

Operational decisions are mostly structured and are typically repeated many times every day. These operational decisions can be modeled once and then reused and executed multiple times against thousands or millions of records and transactions. For example, the calculation of a tax for a portfolio of investments, or the calculation of a bill for a patient in a hospital.

An operational decision structure may be illustrated as shown below:

Operational decisions’ conclusion can be different results or actions. The conclusion can be a simple value, list, or action that enforces a guideline.

  • They can provide a calculation. For example, the billing calculation of a customer in the telecom industry.
  • They can provide options. For example, the list of available treatment for a patient.
  • They can create configurations. For example, the correct configuration of MRI equipment in a leasing process.
  • They can offer product bundling. For example, the best options and price for up-selling and product bundling.
  • They can provide guidelines. For example, the right criteria for a specific procedure for a patient in a particular situation.
  • They can provide judgment. For example, whether or not a pharmacist is certified to work in a pharmacy.
  • They can guide a customer journey. For example, what is the best next action for students in achieving a specific goal.
  • and more…

As shown here, all of these decisions require input to describe parameters, situation, and a case. Then the decision logic is run against the situation and produces a result.

The effectiveness and efficiency of these operational decisions are critical to the success of business in a rapidly changing and competitive environment. Yet the details of the decision and the decision logic are often “hidden” within the organization.

Perhaps these are also hidden within the heads of experienced employees or buried deep within applications and automated processes.

Automation of Operational Decisions

While automation helps organisations to ensure the effectiveness and efficiency of operational decisions, companies often try to automate these using traditional approaches, such as building applications using code, iBPMS, and other technologies. Companies use codes and low-code platforms to build applications and automate operational decisions. Companies have also been using Excel (and other spreadsheet applications) to help operators in the complex calculation. And they have been using other business-oriented solutions such as business rules management systems (BRMS), decision management solutions (DMS) and business process management (BPM) in many cases to build the automation for operational decisions.

So, what’s wrong with these traditional approaches?

In the traditional approach of automating operational decisions (explained above), whether, through applications or traditional iBPMS (a more advanced flavor of BPM), all of these operational decisions are buried in processor code. When these are confronted with the required frequency of change and volume of data, it becomes critical to manage them independently. Otherwise, it makes it really hard to understand the decisions, much less understand how to use and apply them. The problem with that is simply that organisations cannot keep pace.

Also, as shown in the illustrated formula for operational decisions, these rely on data and have action and/or judgment associated with them. Over time, the data, integration, and circumstances driving different decisions change for various situations. The actions are taken mature over time based on the results of these operational decisions, as well as the decision logic itself. When automation of operational decisions is key to the success of the business, hiding decisions, data and required actions within processes and applications makes it very difficult for a business to keep pace. It stretches the boundaries of technology platforms and business practices and leads to less effective results.

Decision Automation Benefits

One of the main benefits of Decision Automation is a significant improvement in business productivity by using a Decision-Centric Approach.

In the first part, we discussed the importance of making decisions the front-line focus of day-to-day operations in organizations. In this post, we look at the high-level steps required to achieve this outcome. We also examine the advantages of a decision-centric approach once it is adopted within an organization. 

Every day, businesses make operational decisions. Not a day goes by without hundreds or even thousands of business decisions being made. Adopting a decision-centric approach changes the focal point from processes, applications, and systems to pragmatic business outcomes.

To improve business productivity, increase accuracy and consistently make the right decision at the right time, automation of operational decisions is critical. This type of decision automation can be achieved at different levels:

The decision-centric approach will enable organisation to fully automate operational decisions based on the Gartner’s model shown above is “Decision Management for Decision Automation“. It is very interesting to see that once again Gartner has started referring to the OODA loop in their recent articles.

What are the steps required to apply the OODA loop for Decision Automation?

Step 1: Identifying operational decisions

Begin by understanding which operational decisions are perceived as the critical to business and how these relate to the company’s overall strategic goals and objectives.
Figure out how these decisions might impact any metrics which are used to measure the relative success of the business. In other words, understand which decisions will give you the biggest ‘bang for your buck’. By identifying these decisions upfront, organization will establish focal points for eventual automation and the strongest return on investment.

Step 2: Separate These Decisions from Applications and Processes

The next logical step is to separate these decisions from current day-to-day business processes, applications, and IT systems. This enables companies to manage them independently from applications, processes, etc. To do that, start by modeling the decisions themselves. This ensures that decisions become first-class citizens of the organization. The decision-centric approach enables stakeholders to clearly understand how decisions are being made and how to assess ownership of those decisions moving forward. The business may then establish appropriate metrics and KPIs for these decisions if these do not already exist.

Step 3: Automate, Apply, Monitor and Refine

Fully automated decisions require the support of all four stages of the observe, orient, decide and act (i.e., the OODA loop). During the previous steps, we covered the ‘decide’ stage. On this step, we are going to discover and identify three pieces of knowledge for the observe, orient and act stages of the OODA loop.

  1. Firstly, we need to understand what those decisions need in terms of data, information, systems, etc. Where are these stored and how are these collected (observe)?
  2. Secondly, in what context (scenarios) are these decisions being used? Do we need to compile new information from the data collected (from the observer stage) for particular (context) scenarios? Will these need any form or rules and prediction in order to reason and create the context for the decision? (orient).
  3. Lastly, identify how to apply the results of these decisions. Identify the actions these decisions will perform once they reach their conclusions.

These three steps provide the basis for building both automated and intervention-enabled decisions.
The decision-centric approach enables organizations not only to identify and model these decisions and their relevant requirements in terms of data, information and dependency on other decisions but also empowers them to manage, execute decisions and perform actions once the decision is reached independent of applications, processes and etc.

So what are the specific benefits of decision automation for businesses when it comes to using the decision-centric approach?

Streamlined Change Management

The decision-centric approach allows organizations to manage, execute and monitor decisions as first-class citizens of the organization. Therefore, business decisions are no longer embedded in multiple systems and processes. As a result, the decision-centric approach streamlines any necessary changes to the day-to-day operations model, as this no longer relies on the full IT lifecycle. This means the separation of decisions from application and system requirements. Therefore, there are no longer lengthy delays while IT compiles a list of applications, systems and processes requirements for a new release. It provides an independent life-cycle for decisions.

Superior Customer-Centric Engagement

Having a good product or some unique features will not last long, as with new technologies it has become easier and easier for everyone to implement new capabilities. Therefore, good customer engagement becomes more important for companies as it has a huge role in a company’s competitive advantage and unique value proposition. Customer-centric organizations provide a highly optimized and personalized customer journey to deliver the best customer experience by:

  • Adapting to changing customer exceptions
  • Delivering new forms of value to customers

And on top of those, companies need to continuously tap into new types of customers for existing and new market segments. The decision-centric approach enables organizations to pursue this path by empowering them to test and adapt to a new customers’ journey quickly and effectively.

Straight-Through Processing

Decision automation using the decision-centric approach means less interruption in processing due to items being delayed as a result of being put into work queues. For instance, in claim handling scenarios in many industries, regardless of whether the company’s platform is fully digitized or not, claims are still being processed manually. In the decision-centric approach, systems are designed to ensure that only exceptions require some form of manual intervention or manual processing. Even when this occurs it is minimized, as the necessary decisions are clearly defined for the staff who are responsible for managing exceptions as part of the decision automation platform.

Business Value Alignment

More often than not it is a big challenge for data scientists and technical teams to articulate the benefit of accuracy in analytics and AI models. The decision-centric approach enables the team to clearly state the benefit of mathematical accuracy and build the business case on how this will have a positive impact on business values. The decision-centric approach in decision automation enables the team to utilize a common business language with different stakeholders and exemplify the business benefits of the solution.

Breakdown a Sophisticated AI and Analytics

There are many references as to why moonshot AI projects have not been successful. To reduce the risk of AI projects, a good way to look at these is that they are about decision automation and consequently break them down into smaller decision models. The next step is to solve each individual decision using technology. Usually, that will result in the resolution of bigger decisions in conjunction with the use of different technologies and AI. The decision-centric approach enables organizations to consolidate, execute and manage these effectively. It lets the team break the problem down and solve it one piece at a time.


Today, when companies have complex operational decisions, they simply cannot scale with traditional approaches. Some have even tried to use any one of many decision management platforms to plug into data and processes. However, that approach creates a disconnected decision experience that often becomes unmanageable in complex and changing situations. That’s where we need to take another approach. The impact of the disconnected decision experience is that it puts organisations back into the first place when there is a need to balance the control between IT and business, which in turn leads organisations to implement these types of solutions in the first place.

This type of business agility decision-centric approach empowers companies and is essential in an environment that embraces rapidly changing policies and responds appropriately to competitive pressures. It gives executives and middle managers control over day-to-day operational decisions and benefits both customers and staff alike. Customers receive personalized service and staff work more effectively by focusing on higher-value tasks. It also helps IT to articulate the benefits and breakdown of complex analytics and AI projects. It’s a win-win situation all around.

Updated on August 8, 2019

Was this article helpful?

Related Articles