Business process reengineering with AI


Business Process Reengineering is an approach aiming at improving a business process through elevating efficiency and modifying or eliminating non-value adding activities. It involves the gathering of information to determine how well a process currently runs and what needs to change. Traditional BPR activities provide informal frameworks which are not sufficient to drive process-driven changes.

AI has the potential to significantly impact BPR by identifying patterns as well as significant signals in a process and by helping in the development of optimal workflows, finding hidden inefficiencies and solving complex problems. Let’s examine how this works.

AI in BPR - Machine Reengineering

For the longest time, data-based re-engineering decisions have been made by organization leaders, and the practice typically requires a person interpreting data and manually adjusting related processes. This would normally take a lot of time and effort but with AI, this changes. Imagine if your business processes could learn to regulate themselves for enhanced performance & results automatically. That’s precisely the role of AI in BPR; Machine-learning techniques can be applied to processes and workflows which will learn to adjust automatically.

Most businesses today aren’t ready for a full portfolio of cognitive applications; so you may want to start with implementing just one or two. Therefore, as a first step, it’s imperative to identify the processes which are suitable for intelligent reengineering. Most-definitely they will be the knowledge-intensive processes which constitute a greater amount of data such as sales, procurement or logistics, product development, marketing, service delivery, as well as information and risk management.

Early Adopters

Two-thirds of the businesses that have adopted smart BPR are speeding up or improving at least one process by more than 500 percent.

Nearly half of early implementers reported improvements in top-line performance. Over one-third of these early adopters also saw gains in bottom-line performance using machine-reengineering to cut 15% to 70% of costs from particular processes. Likewise, some saw a tenfold improvement in workforce effectiveness or value creation.


AI-enabled Business Process Reengineering is already making business processes far more agile, efficient and productive. These early adopter's statistics are just an indication that AI and Machine Learning are the future of BPR.

Haley Welsch
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