In the 1990s, the landscape of business underwent significant shifts with the adoption of new technologies like enterprise resource planning (ERP) systems and the burgeoning internet, sparking widespread business process reengineering. Enthusiasm was fueled by academic and consulting leaders, with firms eager for substantial changes in overarching processes such as order-to-cash and the full spectrum from product ideation to market introduction.
However, while technology ushered in substantial updates, the results often fell short of expectations. Comprehensive ERP systems from providers like SAP and Oracle established vital IT frameworks for data sharing, yet they also enforced inflexible processes that became challenging to modify after their initial setup. Subsequently, process management largely shifted to minor, localized modifications — utilizing approaches like Lean and Six Sigma for routine tasks, and Agile and Lean Startup methodologies for development initiatives, all without significant technological support.
Presently, this concept is resurfacing in various organizations, anticipated to expand further, necessitating not only a deep appreciation for AI but also a revitalized understanding of business processes as a framework for enhancing operations. As AI is recognized as a versatile, essential technology, its potential to facilitate the radical overhaul of business processes envisioned during the reengineering boom becomes increasingly feasible. (One of our contributors, Davenport, authored the pioneering book on this topic.)
Modern reengineering is driven by technologies that differ fundamentally from those of the past. Primarily, AI enhances decision-making processes through automation, speed, and efficiency, largely by analyzing extensive datasets to forecast outcomes or categorize data, thereby supporting improved business operations. Today’s AI capabilities extend beyond mere production planning and control to include sophisticated functions like visual recognition and autonomous operations.
As the costs associated with these advanced methods have decreased significantly, AI solutions have become more accessible, not restricted to data specialists. The surge in affordable computing power, driven by cloud computing, cost-effective bandwidth, and cheaper sensors, has reduced the expenses linked to predictive modeling. AI’s role is increasingly integrated into broader automation contexts. Technologies like robotic process automation (RPA) structure workflow and automate data-heavy administrative tasks. When combined with machine learning, forming what is known as « intelligent process automation, » RPA can handle a wider variety of activities.
This resurgence of AI-driven reengineering is evident across sectors. Banks, for instance, are leveraging AI to revolutionize wealth management advice. Insurance firms are simplifying client onboarding and underwriting processes through AI, which also aids in automating damage assessments for claims through advanced image analysis. Industrial sectors are redefining maintenance and engineering protocols, while AI-driven telemedicine is transforming healthcare diagnosis and treatment in several countries.
These developments underscore the crucial role of AI in redefining how work is conducted and how organizations are structured. To fully capitalize on these advancements, businesses must revisit their end-to-end processes with a fresh perspective, considering how AI can fundamentally transform their operations.
Rethinking Business Processes: How AI Catalyzes Comprehensive Operational Transformation
AI demands a reevaluation of necessary tasks, their frequency, and the allocation of responsibilities between humans and machines within business processes. Most AI applications to date focus on enhancing specific tasks, but forward-thinking companies are beginning to see the introduction of AI as a catalyst for reexamining their entire process landscape.
For example, DBS Bank in Singapore has tackled the high rate of false positives in transaction monitoring required by regulatory bodies through AI, significantly enhancing the efficiency of their fraud detection processes. Similarly, Shell is reengineering its operational procedures using AI, transitioning from manual inspections to remote monitoring via drones and robots, significantly reducing the time required for comprehensive evaluations.
These shifts necessitate a reimagined approach to process management, often led by product managers who orchestrate both technical delivery and business transformation. Companies also benefit from incorporating « design thinking » to rethink workflows and tasks to better meet customer needs or internal efficiencies.
While many organizations have begun integrating reengineering with AI development, a more explicit focus on process reengineering would enhance the effectiveness of AI projects. Whether through designated reengineering efforts or more comprehensive process improvement initiatives, the integration of AI provides a transformative tool for businesses willing to rethink their operational frameworks comprehensively.
