A new golden age of business process management (BPM) is on the horizon. Not since the 1990s, when the emergence of enterprise resource planning (ERP) sparked widespread digitization, have companies needed to revisit how they operate to stay competitive. Two factors are driving the change. First, companies realized that growth at all costs was not sustainable and shifted focus toward performance and efficiency to achieve healthy unit economics and positive ROI. Second, the Gen AI hype accelerated interest and adoption of the technology as company executives mandated teams to explore use cases, looking to gain market advantages.

The Imperative for Process Transformation to Win with AI

While traditional AI and machine learning solutions have been available for decades, companies have been slow to implement them. Generative AI changed the narrative, and companies seemingly aimed to be AI-first overnight. It is easier said than done. As McKinsey reports, 63% of executives characterize the implementation of Gen AI as a “high” or “very high” priority. However, only 11% of companies have adopted the technology at scale, and 74% of executives are purposely exercising restraint through Generative AI investments. The challenge stems from being able to operationalize and scale programs.

The most accurate model or intricate prompt does nothing by itself, which drives why BPM is now critical again. AI will touch nearly every workflow across the enterprise in the coming years. To operationalize any program, let alone scale it, the process must be discovered, analyzed, and redesigned to incorporate the new capability. This parallels the challenge of earlier digital transformation initiatives, which suffered from dismal success rates due to overemphasizing technology at the expense of people or process considerations.

AI Enhances the BPM Stack with New Capabilities

While operationalizing and scaling AI requires process excellence to be successful, it goes both ways. In recent years, BPM technologies have benefited from new capabilities enabled by AI. These advancements have occurred across the entire BPM landscape and drive significantly more value to the organization than earlier generations of BPM tools.

Discovery— AI has revolutionized process discovery by automating the identification of workflows through process and task mining. The technology swiftly analyzes system logs, automating the mapping of processes, which traditionally required substantial manual effort.

Analysis – Incorporating process mining, AI identifies complex patterns within large data volumes, enhancing accuracy. It predicts future process behaviors based on historical data, enabling proactive issue management. With machine learning, AI-enabled systems continually learn and improve, optimizing process and analysis over time.   

Modernization — Redesigning processes by leveraging intelligent automation (IA), intelligent document processing (IDP), natural language processing (NLP), computer vision (CV), and other emerging technologies is at the heart of incorporating AI into modern BPM.

Management – AI has brought about a paradigm shift in process management. From mapping to documentation to monitoring, modern platforms offer improved insights into process performance and aid more informed, data-driven decision-making.

Simulation – AI has significantly enhanced process simulation. Accurately modeling and predicting process behavior based on large amounts of data provides a more precise and detailed simulation at much less cost and effort. This enables businesses to test various scenarios and anticipate the potential impact of process changes.

Orchestration — Modern process orchestration tools incorporate machine learning and predictive analytics to identify bottlenecks, suggest improvements and predict future outcomes. They increase operational resilience by automatically adapting to changing business environments, enabling businesses to remain agile and competitive.

Compliance – AI aids in process compliance by automating the monitoring and enforcing regulations within business processes. It can scan and analyze vast amounts of data to identify any non-compliance issues or potential risks. This ensures regulatory adherence, saves time, and reduces costs associated with manual compliance checks.

Toward the Autonomous Enterprise

Advances in AI-enabled process technologies will lead to highly streamlined and efficient business operations, reducing manual intervention and improving decision-making. When routine tasks are automated and complex tasks are simplified, employees can focus on strategic and innovative initiatives. The shift will result in significant cost savings, increased productivity, and enhanced customer satisfaction.

With AI’s ability to learn from data, continuous process improvement at scale becomes possible and manageable, leading to innovation. Machine learning algorithms can learn from historical data and continually adjust business processes for optimal performance. This leads to a self-optimizing system that continually improves efficiency and effectiveness.

The transition to an autonomous enterprise also comes with challenges. Important considerations include ensuring data security and privacy, managing AI’s ethical implications, and upskilling employees to work effectively with AI. Despite these challenges, integrating BPM and AI can transform businesses, making them more efficient, agile, and competitive in the digital age.

Start Now, Think Big, Go Fast

AI’s market excitement and business opportunities have forced companies to revisit their strategy and invest more in technology. However, AI cannot measurably impact performance until it is deployed and operating at scale, and that requires looking at the target processes from the ground up. Fortunately, the next generation of BPM tools includes AI superpowers to make the exercise more efficient and more accurate at less cost.

To understand how best to enable your organization to harness the power of AI and drive sustainable business growth, let’s talk.