What’s Ahead in Automation: AI, End-to-End Technologies, and More

Automation is a key driver in achieving digital transformation outcomes like agility, speed, and data integrity. But the efforts of many organizations have been stalled in recent years due to significant, unexpected business disruptions – disruptions that have only served to amplify the fact that slow, manual, error-prone processes aren’t enough to survive and thrive anymore.

That’s why we’re seeing so many companies double down on the development and deployment of automation as quickly and broadly across their organizations as possible. In other words, hyperautomation.

According to KPMG, automation is growing faster than any other part of digital transformation at a rate of a 12% increase each year. These efforts include adopting automation platforms with flexible, contingent workflow solutions that drive efficiencies and greater data integrity across multiple complex, data-intensive processes.

To learn more, we were joined for a webinar by guest speaker Maureen Fleming, Program Vice President for IDC’s Intelligent Process Automation Research. Maureen shared recent research and insights from IDC on the importance of process automation to digital transformation, along with predictions for the future.

Andrew Hayden, Sr. Product Marketing Manager at Precisely, shared insights from the automation provider perspective – including the challenges we’re hearing from our customers around automating complex, data-intensive business processes, and the features you should look for in an automation platform.

Watch the full webinar to hear all of the valuable insights and takeaways, and read on below for a few of the biggest takeaways you need to know about.

Demand for End-to-End Automation Grows

IDC research found that while automation efforts have a clear link to business process improvements like greater productivity, compliance, and customer satisfaction, it’s a challenge to scale those efforts. That’s because out-of-the-box automation platforms don’t generate the concrete metrics needed to demonstrate that value. If the return isn’t clear, the investments often falter.

These challenges are increasingly being addressed by the adoption of business value engineering, a data- and insights-driven framework that aligns technology selection with purpose – strengthening your ability to demonstrate the value that those investments produce.

Built on a business process management foundation, value engineering consists of data capture and analysis, documentation and design, and business observability elements that create one powerful methodology for process planning and continuous improvement.

“Business value engineering is a really important way to do planning for more complex use cases that are happening as organizations start pushing the idea of orchestrating multiple technologies together end to end,” says IDC’s Fleming.

Fleming notes that the IDC survey showed that there’s no one technology dominating automation and improvement projects, but 33% of respondents struggle to bring those diverse automation technologies together seamlessly.

How are organizations solving the problem? One approach is to broaden a single platform automation portfolio, enabling you to bring together multiple technologies like workflow automation, API integration, document AI, and more. With this approach, Fleming says, “It’s important when someone has a single platform, to make sure that they have the all of the capabilities that you need to be able to solve problems.”

Other businesses opt to build an orchestration layer that can seamlessly connect to best-of-breed, third-party automation technologies from customers and partners. Regardless of which approach is best for your business, it’s critical to keep governance and reliability intact throughout the process.

What to Look for in a Complete Automation Platform: Process and Data Automation

When we think about automation solutions in the context of digital transformation outcomes like agility, speed, and improved data integrity and quality, we can’t overlook the relationship that exists between business processes and the data they create and manage.

Consider a process that’s already creating bad data. If you attempt to optimize and automate that process for speed and agility without considering the associated data, you can end up simply amplifying those data quality issues.

The same holds true for data quality initiatives. If you merely focus on correcting data already in your databases and systems without looking at the processes that are creating and managing that data, then you’ll forever be correcting bad data.

The key to success is to adopt automation tools and platforms that give equal weight to both the processes you want to automate and the data that is going to be created and/or managed by said process.

Validating data, identifying problems, and enriching data before posting in your SAP ERP or master data management system can significantly improve initial data quality and help drive benefits across multiple aspects of your business. This is especially true for the core master data and associated reference and conditional data that you use to run your business.

Integrating these data quality steps effectively multiplies the benefits of your automation initiative and drives greater business success – all because you’re addressing the interdependence of processes and data.

Building on the seamless orchestration noted by Fleming, Andrew Hayden of Precisely adds that, ultimately, “A good automation platform should be able to drive a lot of your automation success, grow with you if you adopt a hyperautomation strategy, but also address the needs of other systems that may or may not traditionally work together.”

Finding the right platform for your business can lead to the measurable outcomes needed to continue driving scalable and successful automation initiatives. Precisely customers, for example, have seen:

• 97% reduction in time to create a customer
• 90% reduction in cost to process materials changes
• 75% reduction in time to market

The Transformative Power of Artificial Intelligence (AI)

Generative AI

When it comes to the buzziest tech trends, artificial intelligence (AI) tops the list. Organizations across industries are making investments in these technologies to enhance efficiency and achieve the ever-present goal of being able to do more with less.

In software, AI provides a unique ability to automate or accelerate user tasks, which results in greater efficiency and productivity, as well as a reduced dependence on manual labor. Automation and intelligence initiatives are no exception. The demand for generative AI (GenAI) truly is driving the next level of these developing technologies.

The research from IDC found that one emerging area to watch in this realm is the ability to generate recommendations that enable staff to make better decisions, as we move from business rules-based decision automation to AI.

The research firm also notes that automation teams have begun to experiment with GenAI. They’re focusing on initiatives like the expansion of document automation to include unstructured use cases, and creating specialized AI models based on regulations, code, and policies that extend automation to new use cases.

But with all this momentum, we still need to be sure to keep business value demonstration in mind. “In the IT and technology enablement business, we get really excited by new technologies – but it’s not going to replace the need to track the value of the technology investment in GenAI. There’s an accountability and reckoning that’s going to go into this,” explains Fleming.

Predictive AI

It’s also important to examine the role of predictive AI in the implementation of backend process automation. It predicts outcomes for you based on your own data history and changes over time – looking at the likelihood of field values, routing destinations, and more. That means that publicly available data isn’t being used for your process, and likewise, that your process isn’t training the model.

Let’s look at one example where AI-driven automation can make a huge impact: manual material master creation in SAP. This process is notoriously complex and error-prone. Companies often try to combat these errors and improve data quality by defining hundreds of data validation rules – but that approach presents its own time-consuming challenges.

With AI, you could instead start with a single small set of rules and create a new material with the following process:

1. Train an algorithm with your historical data. Then, enter the minimum number of input fields to start the creation request.
2. The AI takes that information and predicts output fields based on its confidence levels.
3. AI output fields are used to pre-populate and validate form fields.

With this process, manual, time-consuming data entry now becomes a streamlined data review. And with each review and round of adjustments, the algorithm’s confidence levels rise, your overall data quality becomes stronger, and processes move faster.

What are your current automation goals, challenges, and outcomes? What’s your next step in the journey?

For even more valuable takeaways as you move forward, be sure to watch our webinar, The Future of Automation: Insights from IDC and Precisely and discover how Precisely helps companies like yours achieve your digital transformation goals through automation.