Case Study – During the 1990s, business process reengineering gained immense popularity as companies embraced emerging technologies like enterprise resource planning (ERP) systems and the internet. These innovations allowed for radical transformations in comprehensive end-to-end business processes. With support from academic and consulting experts, companies had high hopes for revolutionary changes in key processes such as order-to-cash and the development of new products from conception to commercialization.
Despite significant technological advancements, many implementations fell short of the lofty expectations. While large-scale ERP systems such as SAP or Oracle provided a valuable IT infrastructure for data exchange, they often resulted in inflexible processes that were challenging to modify after implementation. As a result, the subsequent approach to process management primarily focused on incremental changes to local processes. Methods like Lean and Six Sigma were employed for repetitive processes, while Agile Lean Startup techniques were utilized for development. Unfortunately, these approaches lacked technological support, operating independently from any assistance technology could provide with Case Study
The resurgence of business process reengineering is evelliny in companies, necessitating an understanding of AI and a renewed focus on leveraging business processes for improvement. As AI becomes a universally applicable technology, it holds the potential to facilitate the radical redesign of processes envisioned by reengineering’s pioneers, including Davenport, who authored the first book on the subject & Case Study.
The resurgence of reengineering is now being fueled by the capabilities of AI, which offers improved decision-making, automation, and transformative potential. Unlike the transactional and communications-based technologies of the past, AI enables faster and more accurate decisions by learning from large datasets.
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This general-purpose technology has brought about dramatic changes in various domains, such as production planning, visual image recognition, autonomous operations, and content generation. The cost of implementing AI has significantly decreased, making it more accessible and mature enough to be readily available. Combining AI with technologies like robotic process automation (RPA) allows for intelligent process automation, empowering organizations to handle a wider range of tasks. Examples of AI-driven reengineering can be seen in wealth management advice, client onboarding and underwriting in the insurance industry, automated claims estimates using deep learning analysis, industrial maintenance and engineering processes, and the reshaping of healthcare through AI-based telemedicine with case study.
The integration of AI into business processes necessitates a reevaluation of tasks, frequency, and human-machine roles. While many AI applications aim to enhance specific tasks, forward-thinking companies perceive the introduction of AI as an opportunity to reexamine end-to-end processes comprehensively. An example from DBS Bank showcases how AI was employed to predict and score fraud risk, leading to increased efficiency for surveillance analysts. Similarly, Shell is undergoing a major AI initiative that involves reengineering work processes. By leveraging AI, tasks such as monitoring and inspection in energy and chemical plants can now be carried out remotely by robots and drones, accelerating the inspection cycle and reducing manual labor requirements. AI is driving process reengineering by unlocking new possibilities and streamlining operations across various industries.
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The integration of AI in work processes has led to a reevaluation of roles and tasks for inspectors and maintenance technicians. They now have the opportunity to focus on higher-value activities such as project prioritization and advanced verification, while also taking on new responsibilities like image annotation and managing machine learning models. These shifts have transformed physical work processes into digital tasks performed by multidisciplinary teams.
Case study Initially, there was resistance to this change, but gradually inspectors have been convinced as they witness the time savings and comparable accuracy achieved through image processing. Shell is actively involving these engineers in rethinking their work processes, empowering them to drive the transformation alongside remote surveillance centers. Shell has found that AI-enabled reengineering is not just a temporary change but a permanent way of operating. Each individual project may have a short duration, but as Shell embraces digital technologies, data, and AI to redesign processes, they uncover further opportunities for improvement. This transformation is particularly crucial as the company pursues its goal of becoming a net-zero emissions energy company.
The responsibility for AI-enabled process change is evolving, with a shift towards involving product managers who oversee the successful deployment of AI systems and the necessary business changes. While process improvement has traditionally been led by operations managers, incorporating process design and improvement within AI initiatives is crucial to fully harness the potential of AI. Shell, for example, designates a product owner and a product manager to manage the business change and technical delivery, respectively. Some organizations also utilize design thinking exercises to analyze and redesign workflows based on customer or internal needs, aligning with reengineering principles.
However, there is a need for more organizations to recognize the importance of process change alongside AI development. Explicitly addressing the reengineering role and activities, including high-level design, detailed process flows, cost and cycle time measurements, and skills and training analysis, would be beneficial.
Case Study Automation-focused projects, in particular, have a direct impact on process flows and are more likely to incorporate formal process improvement steps. Companies like Voya Financial combine process improvement and automation efforts, ensuring that no automation project proceeds without a prior process improvement effort. These examples highlight the significance of considering both aggressive process change and the integration of powerful AI technologies like machine learning for successful transformations.
As AI continues to gain widespread adoption, it is transitioning from a hyped technology to a standard tool, similar to ERP systems, statistical packages, and spreadsheets. Its potential to reengineer processes is accessible to a broader range of companies through AI platforms. However, it’s crucial to recognize that AI is a means to an end, not an end in itself. Organizations that grasp how to leverage AI as a tool within the broader context of process reengineering are likely to derive the greatest long-term benefits from this technology. Understanding the strategic integration of AI within process improvement efforts will pave the way for maximizing its potential impact.
