Decision automation combines artificial intelligence, business rules, and process data to automate steps or tasks that fuel decision-making processes.
This type of automation is ideal for repetitive or routine procedures that power day-to-day decisions and operations. Decision automation also helps reduce risks, errors, or delays associated with leaving decisions to individuals or groups of people. There are many technologies or tools that can be used to automate decision making, including:
- Analytics tools
- Machine learning
- Predictive analytics
- Data management platforms
- Decision automation systems
- Business process automation platforms
This way, decisions are consistent, accurate, and enforced in a data-driven manner.
Learn about the benefits and capabilities of Al, no-code, and process automation technology
Types of decision automation
There are two types of decision automation: rule-based and data-driven.
Rule-based decision automation
This type of decision automation is informed fully by business rules and is often deployed to improve the consistency and quality of decisions. This type of decision automation can be useful for finance or purchase processes that require a high level of regulation or adherence to business policies.
Rules-based automation is also ideal for providing an explanation or reason for certain decisions because it follows a defined set of rules that are easily understood and explainable.
In the case of a purchase request, for example, a request may or may not be approved depending on whether the request is over or under a specified dollar amount. Whether it’s approved or not, the requester can easily see and understand the reason for the decision.
Data-driven decision automation
For this type of decision automation, data — not rules — power how decisions are made. Pulling from a wide range of data sources or factors like business data, statistics, predictive modeling, personal information, or artificial intelligence, decisions based on how a situation is developing and the factors or uncertainty surrounding that development.
Let’s say a procurement manager wants to determine whether to continue with or cut a supplier from their portfolio. Based on data points like cost, quality, delivery times, and potential risk, a decision can be made as to the future of this supplier relationship.
Challenges affecting decision automation
Security, data control, and environment management
As companies change, automated decisions need to be able to withstand any type of internal or external disruption. They must also be flexible and agile enough to process new data streams.
These changes increase vulnerabilities, so it’s essential for any automated decision environment to possess strong data security protocols and secure IT systems that are consistently reviewed.
To keep data secure, data auditing is a must for managing and developing strategies or procedures as data volume increases.
Long-running or multi-step decisions
Decisions aren’t limited to simple yes/no logic, and some may depend on or roll into other decisions. These dependent decisions are considered multi-step decisions. Interdependent decisions, or long-running decisions, are complex decisions that roll into another.
Depending on the complexity, this may present an obstacle for automating decisions.
Resource allocation
A poorly implemented automation system leads to poor results. Without the proper resources allocated to deploying decision automation, issues like unnecessary criteria, too many data sources, or inaccurate decision flows are much more likely to occur. High IT dependence can be the cause of this, as IT teams are required to manage any and all technical and process architecture changes related to decision automation.
Decision quality
An automated decision doesn’t guarantee quality or accuracy. After all, the decision will only be as useful as the rules or data applied. That’s why it’s necessary to monitor the outcomes to ensure that all the links in the automated decision are accurate and defined by data or rules.
Why (and when) certain decisions can’t be automated
There are many benefits and business advantages that can come out of decision automation. Workflows can become smarter thanks to data analysis, errors can be prevented, compliance and IT governance improves, staff are better utilized, decisions are made faster and much more consistently.
While the main goal is to omit human intervention and any associated negative impacts that may result from that intervention (i.e. errors or delays), there are some cases when skipping decision automation altogether (or to some effect) may make the most sense.
Note that human intervention should only be applied to decision automation applications in small doses. Otherwise, it may contradict any of the decision automation benefits and can potentially lead to unstructured, inconsistent, or disconnected experiences.
How to implement decision automation in business processes
The exact science required to implement decision automation really depends on the complexity of the decision-making process or steps. While implementing decision automation will be custom to your organization’s decision workflows, get started with this general framework.
Map all decisions
Similar to mapping a process ahead of an optimization initiative, the first step is dissecting the decisions you want to automate and mapping the manual flow of those decisions. For example, does a decision fall on an individual? If so, what steps do they need to take to validate and make a decision?
Identify manual and repetitive tasks
After mapping the decisions, it’s time to flag the manual, repetitive, or time-consuming work. These types of tasks are ideal for automation because they often can be replicated and don’t necessarily need human intervention. For example, if a purchase approval is dependent on whether it falls under a certain amount, if/and logic can be used to streamline the approval process.
Choose between rules-based or data-based decision automation
Determining which type of information is fueling the decisions is necessary to making accurate, reliable, and consistent decisions. Here’s a quick refresher on the types of decision automation:
| Rules-based decision automation | Data-based decision automation |
| – Decisions informed by business rules, policies, or procedures – Often used to improve the consistency and quality of decisions – Useful for finance or purchase processes that require a high level of regulation or adherence to business policies | – Decisions informed by data like business data, statistics, predictive modeling, personal information, or artificial intelligence – Often used to improve analyze and predict the most accurate outcome – Useful for decisions based on how a situation is developing and the factors or uncertainty surrounding that development, such as insurance risk assessments |
Decide whether to make exceptions for some human intervention and the scenarios that would merit it
Humans are central to decision automation, and there may be cases where some human intervention is required. For example, when there is ambiguous data or a decision becomes overly complex, human support may be needed.
Decisions based on creative, assumptive, or ethical judgment calls are also ideal candidates for human intervention. That’s because while there may be data or rules that can be used to reach a conclusion, nuance cannot be computed or quantified.
To ensure that inconsistent or inconclusive decisions aren’t introduced, referral rules will need to be introduced and implemented so that human intervention is only needed when absolutely necessary.
Examples of business transformation with decision automation
Digital acceleration is a key catalyst for business transformation, with investments in technology like automation, AI, and analytics being the key end-to-end digital business transformations.
The purpose of a business transformation is to improve the overall performance. In other words, increasing revenue by way of lowering operating costs, increasing customer satisfaction, and empowering employees so that productivity and efficiency goes up as well.
Decision automation can help with this transformation in ways like:
- Lowering IT dependency so they can focus on more value-driven activities.
- Easing employee decision fatigue caused by manual decision workflows.
- Reducing the amount of employee time spent on manual decision making.
Here are some examples of how decision automation can transform businesses and enhance decision-making capabilities.
P2P decision automation
A company’s accounts payable team streamlines the three-way matching process, eliminating payment delays, payment errors, and speeding up the invoice reconciliation.
Finance decision automation
Finance teams can establish an error-proof approval flow by automating purchase requests that exceed a certain value so they are automatically sent to a specific approver, like a manager.
Procurement decision automation
A spot purchase is fulfilled and delivered in a timely and cost-effective manner thanks to a data-based approval flow.
Customer service decision automation
Chatbots respond to routine customer inquiries, allowing human customer service agents to focus on more complex and specialized inquiries.
Retail decision automation
Data-based decision automation assess customer behavior to determine whether to upsell or recommend additional products or services during a purchase.
Sales decision automation
For lead qualification, a field asking “Is the lead qualified to move forward?” sets the path for qualified leads, ensuring that sales teams respond in a timely and informed manner. If the lead is qualified and the company size equals a minimum amount, the lead automatically moves into the negotiation phase. Faster categorization means faster action.
Automate company decision-making with a process automation platform
The ideal decision automation platform will make it easy to test, customize, deploy, and modify decisions and the processes that lead up to those decisions. A process automation platform like Pipefy is a great way to get started on decision automation because it offers a set of tools that can be used to automate processes, approvals, and decision-making.
That’s because a process automation platform can automate and align decisions with existing workflows. With Pipefy’s visual, no-code conditional automation capabilities, business teams — free of IT intervention — can automate decisions when specific events occur, like:
- When a request is created by a certain person.
- When a form field is updated with a particular value.
- When a request enters a process phase, and the due date is a tight turnaround.
Pipefy also seamlessly integrates with existing tools or software and databases to ensure that automated decisions are accurate and properly informed.
