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Blog | Jul 10, 2024

Decision Automation: Automate Your Decision-Making

Decision Automation
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Decision-making takes time and resources. Having the right information at the right time – and having the time to evaluate that information and act on it – seems a luxury at times. But with intelligent automation (IA) solutions such as decision automation, your decision-makers can take their abilities to the next level with the aid of cognitive technology.

What Is Decision Automation?

Decision automation utilizes machine learning (ML), a subset of artificial intelligence (AI), for automating business decisions – making it easier for organizations to expand their automation. It replicates human decision intelligence and employee skillsets to drive further value for the business.

As your operations grow, the business decisions you delegate to your digital workers should also increase. Those organizations using robotic process automation (RPA) only scratch the surface of the benefits and efficiencies they can gain from automating the decision-making process.

What are the applications of decision automation?

The limits of decision automation are your imagination. It can work across multiple industries and use cases, from financial services to healthcare. It’s useful for routine decision-making processes that could be optimized, where quality and consistency are crucial.

Where do I apply decision automation?

You want to automate areas involving multiple decisions and variables that are time-consuming to your decision-makers. By deploying decision automation, you can automate decisions through ML without needing data science expertise. It makes rules-based decisions while your employees focus on higher-value tasks. 

What is an automated decision tool I can use?

SS&C | Blue Prism® Decision empowers organizations to automate decision-making, creating explainable ML-based processes in hours rather than days. Automate operational decisions where you didn’t think possible with minimal effort and expertise.

Decision can:

  • Make ML more accessible: It incorporates active learning, asking you the most relevant questions to determine how you make decisions. You can then train the model by providing sample inputs with autoML. You can build statistical models in mere minutes – without any coding or data science expertise.
  • Create explainable ML-based decisions: Decision generates detailed audit logs highlighting the inputs used to make the decision and provides a confidence score for each decision made.
  • Automate human-like decisions: It enables you to create a single decision step in the process flow via an application programming interface (API) to reduce the design complexity. You can also enforce important decisions with a priority rules-based decision automation system that overrides the model’s predictions.

What Is an Example of an Automated Decision?

Decision-making happens across every enterprise, big or small, and in every industry. We’ll focus on two examples. But really, this technology is so flexible you can apply it virtually anywhere.

Paying a car repair invoice

Decision can streamline business processes by drastically reducing the number of steps the digital worker has to go through.

An accounts worker deciding on whether a repair invoice needs to be paid or not must consider:

  • Was the repair authorized?
  • Does the vehicle exist?
  • Do the details of the repair match our systems?
  • Does the invoice have a matching purchase order? If not, is the amount under a certain accepted level?

This type of administrative task is the ideal candidate for automation – and Decision can make it a reality.

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Applying for a bank account

Decision can streamline the process flow by drastically reducing the number of steps the digital worker has to go through.

An agent must consider the following (and more) when evaluating a new application:

  • Is the person applying for the account a resident of an applicable country?
  • Are they the appropriate age?
  • Do they have an existing account?
  • Are they a staff member?

Processing thousands of applications manually can become a significant time-sink – something Decision is primed to solve.

How Do You Automate Decision-Making?

For all those decisions not covered by existing rules or training inputs, Decision’s ML model can be applied to business rules automation in four steps:

  1. Define your model name, add input variables and highlight the decision that needs to be made.
  2. Create rules and provide decisions based on the variables so your model can train.
  3. Calibrate your data to ensure the model is generating the correct outcomes.
  4. Integrate and deploy your processes through Decision once they’re ready.

What drives decision automation?

Operational decisions are driven by business rules and influenced based on data. There are two types of decision automation:

Data-driven decision automation

Rather than basing a decision on rules, it’s based on how a situation unfolds and any uncertainty around it. For example, flagging risk for an unqualified client applying for a bank account.

Rule-based decision automation

This is where your decision automation is based on business rules. For enterprises that have repetitive decisions that can be automated, rules-based decision automation is ideal for ensuring consistency (e.g., claims approvals). Rules-based decision automation is especially critical for highly regulated environments such as finance and insurance.

Fully automated processes allow employees to focus on higher-value initiatives, further increasing productivity across business operations.

Here’s what Decision does:

  • Reduces design complexity for process developers.
  • Increases time-to-value with faster decision-making processes.
  • Recognizes potential errors and makes decisions to repair or respond – and doesn’t require human intervention.
  • Aids in compliance by automating regulation adherence.
  • Improves consistency by applying rules with 100% accuracy.

What Are the Benefits of Decision Automation?

Business decisions can be time-consuming, drawing employees from their other work. Automation serves to transform these processes using ML predictive models that serve relevant, useful data to users so they can make more informed decisions.

With all that, you get:

  • Happier employees
  • Faster processes
  • Better accuracy
  • Fewer risks
  • Bigger time and cost savings
  • Better resource allocation

Are there any challenges with decision automation?

Decision automation requires training your models on accurate data and ensuring your systems and people are connected.

Here are some of the challenges to look out for:

  • Auditability: Ensure your decision automation includes good data security, retrieval and storage.
  • Collaboration: Some decision automation tools don’t require any human-in-the-loop (HITL), and this may make your people feel disconnected from your automated workflows. If this is a concern, look for software that allows greater visibility into the end-to-end decision-making process.
  • Long-term decisions: Long-running decisions that include multiple steps can be complex, making these automations difficult to scale. Look for a decision automation platform that helps resolve these process complexities.

SS&C Blue Prism’s Decision answers these challenges head-on.

What Approach To Use With Your Automations

To thrive in today’s competitive environment, adopting decision automation is a strategic imperative; organizations can enhance productivity and free up human workers to focus on higher-value tasks that require creativity and critical thinking. Those who embrace decision automation will be well-positioned to lead in their respective industries, driving success through smarter, faster and more informed decision-making.

If you’re ready to take your business to the next level with decision automation, explore the capabilities of SS&C | Blue Prism® Decision. Empower your organization with the tools to automate and optimize your decision-making processes.

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