The Path of Decision Automation: Misconceptions and Challenges
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.
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
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.