How Scaling Fuels Decision Automation for Enterprise Organizations

Decision-making drives the operations of every business. In large enterprises, the number of daily decisions could be in the hundreds or even thousands. Some are more complex than others, and many fall into categories where automation can accelerate the process. Decision Automation (DA) is the natural evolution of driving maturity when organizations leverage transformation to involve hyperautomation. 

The road to reaching DA involves orchestrating end-to-end processes that can be departmental or interdepartmental with a thread of interconnection. The foundation of DA consists of process automation, which connects processes and task automation and drives employee productivity. 

With process automation in place, the next hurdle is scaling your automation program. The drive toward scalability includes layering automation across your lines of business, including front, middle, and back office. At this juncture, you’ll realize significant ROI from automation.

After completing these steps of the journey, you can reach a place where you are ready to launch effective Decision Automation. This level of automation has impacts on customer, employee, and supplier experiences.

Simply put, the ability to scale is the fuel for Decision Automation. In this guide, we’ll cover everything you need to know about the strategy.

Decision Automation describes leveraging automation to make decisions. The type of decisions varies from basic to complex. There are two categories of Decision Automation: rules-based and data-driven. 

The first is accomplishable with Robotic Process Automation (RPA). Digital robots can “make” decisions when the task is simple and repeatable. The robots use your business rules to make a determination. 

Data-driven Decision Automation involves Intelligent Process Automation (IPA), which combines RPA with Artificial Intelligence (AI) and Machine Learning (ML). In this segment, predictive models fuel decision-making. In some situations, technology doesn’t just make the decision but supports humans who need context to make a final decision. 

The motivation for Decision Automation typically comes from a need to speed up processes and make more accurate decisions. Additionally, organizations want to be as efficient as possible and realize cost savings.

DA can be the first step or one much later in an automation program. Where it lands depends on your goals and priorities. In most cases, process automation happens first, but building a Decision Automation tree doesn’t have to be tied to an automated process. Designing Decision Automation is dependent on the rules and data supplied, so gathering that data and defining those rules is the beginning of the journey.

Decision Automation is a natural progression of an automation program, and it can lead to significant ROI. However, many factors can hold an organization back from capitalizing on it. You may be stuck on some of these, too:

A Lack of Awareness or Apprehension

Even if you have a robust automation program, you may not know the potential of automating decisions. Your program may have started small with RPA removing repetitive tasks, and over time you have been able to adopt IPA. Automation may be delivering a lot of benefits to your business, but you have the opportunity to drive your organization toward even greater value with decisioning.

Conversely, you may be aware of the potential but unconvinced that it’s achievable. Decision Automation does require changes in processes and workflows. It will disrupt operations, so you’ll want to develop a readiness plan. 

Skepticism and opposition could be on the horizon, along with some fear of losing jobs to robots. However, Decision Automation is not meant to supplant human intelligence. It’s here to augment what human workers do and support them. With a strong strategy, targeted outcomes, and well-planned change management system, you’ll get buy-in from employees and can ease their fears.

Defining the Problem and Business Outcome

First and foremost, you need a clear understanding of the problem you seek to solve via automated decisions. Additionally, you have to correlate this to your data sources and be aware of the impact of these decisions.

Second, you need to outline your expected outcomes that align with business goals and priorities. You should also determine the specific key performance indicators (KPIs) that would demonstrate success. Misalignment can stall Decision Automation, and you might not build the “right” kind to make a difference in workflows.

Look at your automation program as it stands now, and look for opportunities for either rules-based or data-driven decisioning. Shore up your strategy and vision.

Digital Transformation Maturity

The maturity of your digital transformation initiatives impacts Decision Automation. Review the KPIs in your strategy and ensure they correspond with digital transformation goals. 

Having the proper structure in place to support your program is useful, as well. Ideally, you should have these things in place:

• Support and maintenance teams
• Processes to monitor the automations
• Robust data infrastructure
• Governance and compliance
• Comprehensive testing and validation 

If you lack any of these, that doesn’t mean you can’t implement Decision Automation. You can develop them over time, and they’ll likely evolve.

Concerns Over Having the Right Talent to Design and Implement Solutions

A Center of Excellence (CoE) is the core of your automation program. You’ve leveraged your CoE to scale automation at the enterprise level. They’ve built and continue to govern your processes and procedures regarding automation, so they are instrumental in Decision Automation. The concerns you may have about taking automation to this next step are understandable, but you can find the expert guidance you need by partnering with an organization that specializes in this work.

Measuring Success

The impact of automation is measurable in many ways. Monitoring and building on it as a scaled model gives you more opportunities to see a return. What success means for your organization goes back to your goals. What decisions do you want to automate? How will they deliver on an objective, such as making decisions faster to drive process optimization, enabling your employees to focus on more complicated choices, or providing a tool for customers to get support?

Companies Thinking They Are Too Big to Use It

Some organizations think they are too large for Decision Automation. That’s not the case. DA applies to many business processes and is scalable throughout the enterprise. It’s not going to solve every business problem you have in terms of operational efficiency. Look at it holistically and consider how it could improve the daily lives of employees, customers, and suppliers.

Contrary to this common misconception, the size of a business doesn’t matter. What does matter is that automation is only as good as the process, and some are flawed. Automation won’t magically make them better. You’ll want to focus on those you’ve perfected that involve a decision.  

Lack of Trust in Machines

Putting trust in machines to make decisions can make anyone have doubts. It’s a question that’s part of the automation discussion: Will the machines make the “right” decisions? 

You may be coming to the table with prior bad experiences in which things went wrong. However, automation technology is reliable when the design of the program is solid. Designing such a program is not something that happens quickly. There are many stages, including:

• Developing a proof of concept
• Defining use cases
• Looking at the actual quality of data and processes available and finding the gaps and flaws
• Testing

Another aspect of this concern is bias. You may have fears that machines will add more bias to decision-making than humans do. In most cases, Decision Automation makes recommendations based on data, so the output depends upon how “unbiased” that data is. In the end, humans are still making final decisions. Minimizing bias works best when you combine humans and digital robots.

Don’t Overlook Change Management and Training

Implementing Decision Automation can lead to a significant change across your organization, and that can be challenging for employees. To ensure the transition goes smoothly, develop a structured approach to the changes you’re implementing and support those changes with ongoing training for employees who will be impacted by the automation.  

Don’t Move Too Fast

Moving too fast before you have proof of value can derail your project. Instead, start small, measure performance, refine, and then scale. Scaling too quickly without a value measurement will erode confidence in the program and its ability to deliver ROI. Without clear goals and metrics, failure will be imminent. A test-and-learn approach can help you avoid this failure.

Don’t Forget to Get Buy-In Early

Lacking alignment with and buy-in from leadership can be a barrier to success. Leaders need to be involved in early conversations so you have champions who will support the program from the top down. When you have this buy-in, change management can be smoother to ensure adoption. Discussing the plan with all stakeholders is essential, too.

Don’t Build a Tech Stack Too Early

Choosing technology before you define your expectations and strategy is something to avoid. Map out your processes and the decisions and complexities involved to lead you to the right platforms.

Don’t Remove the Human Element

Remember that automation is a support tool, and human oversight and input are still vital. An over-reliance on technology could detract from the experience of your employees and customers.

Don’t Expect Technology to Resolve Bad Processes

Technology is a powerful tool, but it’s not a magic wand. You’ll want to focus on continuous improvement in your processes, but keep in mind that tech isn’t a quick fix for inefficient processes. 

Don’t Overlook Data Quality 

For Decision Automation to be accurate and effective, your data must be complete and clean. Address any data issues before you scale.

• Empower employees with more meaningful work.
• Accelerate processes for greater throughput.
• Streamline processes to improve efficiency and reduce the strains on human labor.
• Increase accuracy in decision-making with fewer errors.
• Reduce costs associated with manual and high-volume tasks.
• Make data-driven, strategic decisions.
• Improve consistency in decision-making.
• Gain new insights into the fundamentals of the decision-making processes that weren’t apparent before.
• Fill gaps caused by a shortage of workers in some areas.
• Drive more profitability.
• Achieve consistency, accuracy, and speed in decision-making, which helps you operate more cohesively and maintain market share as competition increases.

If your enterprise wants to accomplish goals like these, scaling will be an essential component. Let’s look at the steps to do this. 

1. Get executive alignment and support by proving value early and often.
2. Develop an implementation plan.
3. Involve employees and stakeholders in your plan.
4. Train employees on new skills to help them further their contributions because they’ll have more time to do so with automation providing decision-making.
5. Build and empower your CoE.
6. Be prepared for rollout across the enterprise with a readiness plan.
7. Set clear goals and KPIs.
8. Research and select the processes and technologies that will support your goals.
9. Monitor and measure the program.
10. Iterate based on learnings and seek continuous improvement.

With these steps, you’ll be ready to establish Decision Automation. 

Resume Scanning

HR and recruitment can use Decision Automation to do an initial review of resumes. The automation can make decisions based on the role’s requirements and keyword inputs, delivering the best-fit resumes to humans for further evaluation.

Benefits realized: Meaningful work, efficiency, and cost reduction

Credit Decisions

Decision Automation can speed up credit decisions by determining their creditworthiness based on a bank’s or lender’s thresholds fed to a Machine Learning model. As a result, applicants can receive an instant decision.

Benefits realized: Efficiency, more accuracy in decisions, and better customer experiences

Fraud Detection

Automation can analyze patterns and detect anomalies in processes to identify and detect possible acts of fraud.

Benefits realized: Reduced risk and efficiency

Supply Chain Management:

Decision Automation can play a crucial role in streamlining a supply chain and making it more resilient. It can support inventory optimization and demand forecasting.

Benefits realized: Efficiency, streamlined processes, reduced costs, data-driven decisions, and better employee and supplier experiences

Customer Service

Decision Automation can run as a recommendation engine for self-service when customers have common questions. It can also send automated responses.

Benefits realized: Efficiency, reduced costs, meaningful work, and an improved customer and employee experience

Healthcare Solutions

Healthcare is a sector requiring lots of decision-making regarding treatment, diagnosis, and triage. Decision Automation can speed up these processes with insights provided to clinicians. Another healthcare application is for administrators to apply it to their resource allocation models.

Benefits: Streamlined processes, efficiency, reduced costs, increased accuracy, and a better experience for customers (patients) and employees

Marketing

Decision Automation is an excellent tool for marketers. It can help with such activities as identifying signs of customer churn, lead scoring, segmentation, personalized marketing, and targeted marketing.

Benefits realized: Efficiency, reduced costs, meaningful work, streamlined processes, greater accuracy, and boosted revenue