Digital Economy Dispatch #213 -- AI-Driven Business Process Reengineering

Digital Economy Dispatch #213 -- AI-Driven Business Process Reengineering
8th December 2024

It's all coming back to me now. Those countless hours hunched over conference room tables, my eyes glazing over as we meticulously crafted intricate business process maps. They were beautiful, really – a colourful mosaic of boxes and arrows describing arcane working practices that could be redesigned to bring streamlined efficiency and cost savings.

But the reality was often different. Those carefully designed blueprints, once the pride of boardroom walls, ended up as crumpled reminders of many wasted hours of work in dusty filing cabinets (remember those!). Despite our best intentions, the complex operational landscapes of businesses proved resistant to change. It was a stalemate between the drive for innovation and the stubborn inertia of established working practices and legacy systems. The business analysts' will was frustrated by the administrators' won't.

Now, a digital revolution is sweeping across industries, with AI seen as a powerful ally in process optimization. Powered by its ability to analyse vast amounts of data, AI might help unlock the true potential of business process reengineering.

Yet, given previous experiences, perhaps we need a healthy dose of scepticism. A key question remains:

Will AI succeed where previous business transformation attempts failed?

BPR Redux

Business Process Modelling (BPM) and Business Process Reengineering (BPR) became popular in the 1990s as revolutionary approaches to organizational design and management. By fundamentally questioning and redesigning core business processes, companies aimed to dramatically improve efficiency, cost-effectiveness, and customer satisfaction. This approach often leveraged emerging technologies like enterprise resource planning (ERP), human resources management (HRM), and customer relationship management (CRM) systems to streamline operations.

Early success was most notable in manufacturing and regulated industries with highly governed, repeatable processes. However, in other areas, BPM and BPR faced increasing challenges. Organizations wanted processes that were not just efficient, but also agile, innovative, and resilient – qualities that detailed modelling techniques struggled to deliver.

Many reengineering efforts were complex and costly, dependent on coordinated efforts across multiple teams and extensive organizational change. As a result, these projects frequently took too long, cost too much, and drifted far from the real-world environments they intended to improve.

Yet, these process improvement activities laid a crucial foundation for digital transformation. The techniques expanded to more flexible system architectures and new technologies like robotic process automation (RPA), predictive process monitoring, and data-driven optimization.

The Return of Business Reengineering, Powered by AI

Today, AI is reviving the reengineering concept. Unlike past transaction-focused technologies, AI can be applied to more agile organizational processes to enable smarter, faster, and more automated decision-making. By analysing vast datasets and exploring solution alternatives, AI can predict outcomes, classify information, and drive operational improvements.

AI-driven Business Process Reengineering (AI-BPR) is transforming multiple sectors. In manufacturing, AI enables predictive maintenance and advanced quality control. Banks are enhancing wealth management, insurance companies are streamlining claims processing, and healthcare providers are reshaping clinical practices using AI-powered solutions.

Such experience with AI is driving high expectations for how dramatically it will enable reengineering of core business processes. For example, looking at the UK public sector, a recent study declared that AI could create efficiencies in the tax and welfare departments equivalent to  4,300 years of work by using AI to automate and redefine call centre handling practices. Similarly, in the US, Elon Musk’s promises to save $2 Trillion in US government spending appear to be largely based on redesigning processes with AI.

Yet, to progress, AI-driven BPR must find ways to overcome familiar obstacles reminiscent of the early days of BPR.

One issue is that modelling and designing AI-enabled processes require collaboration between a diverse set of operations managers, data scientists, and business leaders. This can only be achieved with a focused product management approach to ensure the successful deployment of AI solutions and to support the necessary organizational changes. Such coordination is difficult to achieve and maintain.

More fundamentally, despite AI's advanced capabilities, many of the underlying organizational and cultural challenges can overwhelm technology-driven change. As we experienced in recent years, the resistance to change, fear of job displacement, and a lack of understanding about AI's strengths and limitations can deeply impact the pace at which the organizations adapts to new ways of working.

Moreover, the complexity of organizational structures and entrenched processes can make it difficult to identify and prioritize areas for AI-driven transformation. Like earlier BPR activities, AI initiatives often require significant investments in technology, training, and change management. If not managed effectively, these costs can outweigh the potential benefits. While if overly managed, heavy-handed governance and control stifles innovation and brings progress to a halt. Ultimately, the success of AI-driven BPR may well depend on establishing the right balance in these areas and adopting a holistic approach that appropriately aligns technological and human factors.

BPR Lessons for Leaders

Much is expected of AI as a driving force for change. To achieve these goals, business processes must be redesigned and new ways of working introduced. The impact of AI’s disruption to previous working practices can be immense. However, so is the opportunity to drive efficiencies and open up new ways of working. Business leaders must critically examine their readiness to embrace this technological revolution.

The stark reality is that AI-driven BPR is much more than a process redesign. It is a fundamental reimagining of organizational structures, capabilities, and behaviours. The disruptive nature of AI forces a more profound shift – one that demands a rethink of common ways of working and challenges traditional boundaries between human expertise and machine intelligence. In doing so, it raises important new questions about ethics, bias, safety, and productivity. All of these must be addressed in the context of today’s significant economic and political uncertainty.

Our previous experiences with BPM and BPR can help point the way to address these concerns. They indicate that to navigate this complex landscape, leaders should re-examine their AI adoption strategies based on three pivotal questions:

1. Data Robustness: Have we developed a comprehensive data strategy that goes beyond collection to ensuring quality, addressing potential biases, and creating a robust infrastructure that can power AI-driven insights? The effectiveness of AI-driven BPR hinges on the depth, breadth, and integrity of underlying data ecosystems.

2. Organizational Adaptability: Are we prepared to fundamentally redesign our operational models, creating flexible frameworks that seamlessly integrate human creativity with AI's computational power? AI-driven BPR requires more than technological implementation – it demands a cultural transformation that embraces continuous learning and adaptive workflow design.

3. Strategic Alignment: Can we articulate a clear vision of how AI-driven BPR directly contributes to our core business objectives, rather than treating it as a standalone technological experiment? Successful implementation demands a holistic approach that connects AI capabilities directly to strategic value creation.

Past BPR efforts failed by underestimating organizational complexity and overestimating technological solutions. AI is in danger of repeating this error. AI presents powerful optimization potential, but successful implementation requires a deep understanding of organizational and behavioural issues.

As we move forward, decision makers must invest in comprehensive change management, prioritize data quality, and create adaptive organizational structures that can integrate AI-driven insights. The most effective AI-driven BPR will be strategic, measured, and focused on tangible business outcomes rather than technological novelty. Those who approach AI as a strategic capability – not a magic solution – will be best positioned to drive meaningful business process improvement.