How Process Intelligence Takes the Guesswork out of Intelligent Automation

From morning headlines to email newsletters, business leaders are constantly bombarded with information about artificial intelligence and what it should mean for them. Through the hype, the fatigue, and the ambitious claims of AI integration, it can be difficult to discern the facts from overgeneralized AI-washing – let alone know how to generate real value with the technology.

Innovation leaders Catherine Stewart, President & General Manager for the Americas at Novelis, as well as Marty Pavlik, Managing Director at Doculabs, joined ABBYY CMO Bruce Orcutt for an Intelligent Automation Month session exploring the power of process intelligence in guiding successful intelligent automation initiatives.

If you’re looking for a way to take the guesswork out of integrating AI into your automation strategy, you’ll want to keep reading (or click the banner to watch the recording on-demand).

 

 

Getting started with AI

Most companies investing in AI are driven in some part by the fear of falling behind or downstream pressure from executives, prompting them to rush implementation and guess where exactly to begin.

To determine the appropriate AI solution, you must first understand where the problems lie. The answer might not always be AI or automation; many challenges can be solved by re-engineering processes or training employees.

Automation experts recommend instead on specific business objectives to achieve and problems to solve, as well as leveraging process intelligence or process mining to gain unbiased insights into current process performance. By developing a thorough understanding of an organization’s workflows, decision makers can then make the appropriate call on where or where not to leverage AI.

 

Building a case for generative AI

The latest Gartner® Magic Quadrant™ for Process Mining Platforms predicted that process mining will help business leaders and analysts discover and assess opportunities for generative AI in their business. Automation experts agree, citing process intelligence as an effective tool for determining where to get the most value from generative AI.

One Doculabs client, for example, has heavily utilized generative AI in their call center, using it to create process maps of each call by classifying their different sections. They then identify where gen AI could achieve automation within calls.

While traditional automation is known for its rigid and rules-based nature, gen AI has been incorporated to identify patterns, understand unstructured data, and provide contextual responses to automate customer-facing workflows from end to end. This capability to enrich data makes gen AI highly complementary to traditional automation strategies.

 

Optimizing process automation

A frequent mistake in digital transformation is automating downstream tasks that cause further bottlenecks. Process intelligence alleviates this risk by offering unbiased insights into process inefficiencies, enabling effective process simulation, and anticipating ROI. It also drives the measurement of automation success through the identification of key business objectives and KPIs.

Disordered processes are also sometimes inevitable due to unpredictable business environments. Process intelligence enables accelerated handling of multi-step processes that require divergent decision-making. The key is to maintain visibility with human involvement and constantly monitor processes for opportunities to leverage AI.

 

Developing a digital twin

Gartner predicts that by 2026, 25% of global enterprises will adopt process mining platforms as the first step towards creating a digital twin of their operational processes. Digital twins act as virtual representations of real processes for evaluation and simulation, enabling businesses to be proactive in their pursuit of operational excellence. Targeting process mining across relevant systems and continuously monitoring real-time data is recommended for an effective digital twin creation.

 

Preventing sprawl

With multiple vendor solutions available for process mining, businesses risk backend system sprawl. Companies must periodically re-evaluate their business objectives and tools to avoid this. The right tool must align with the business objective and impacted KPIs. An intuitive low-code tool presence is essential to avoid process improvement from becoming a purely IT-driven initiative.

 

Data's crucial role in process improvement

Data is the cornerstone of process improvement. Successful process intelligence heavily relies on normalizing and cleansing data. Accurate data is integral to value acceleration, while inaccurate data presented to stakeholders could lead to credibility loss. Task mining, despite privacy concerns, is crucial for inferring useful information from available data. Education is key to assure employees that task mining isn’t a personal information harvesting tool, rather it enriches data from process mining to help understand processes better.

 

The road to process transformation

Innovation leaders aim to deliver speed, cost, visibility, control, and quality. With the shift towards real-time process insights, value acceleration and friction reduction in business transformation become priorities. Establishing and maintaining a process intelligence center promotes strong governance and change management practices.

 

 

ABBYY recently hosted four webinars for our yearly Intelligent Automation Month. This expert-led session on process intelligence featured insights from ABBYY Chief Marketing Officer Bruce Orcutt, Novelis President & General Manager for the Americas Catherine Stewart, and Marty Pavlik, Managing Director at Doculabs.

Interested in intelligent automation solutions, but not sure where to start? Tell us about your business process challenges and we can find a solution together. We look forward to learning about your digital transformation journey.