What's Old Is New Again

Learnings from the Past

In 1993, Michael Hammer and James Champy’s "Reengineering the Corporation" challenged businesses to fundamentally rethink and radically redesign their processes for dramatic improvements. Thirty years later, as Artificial Intelligence and Generative AI promise to redefine industries and unleash unprecedented innovation, the core fundamentals of Business Process Reengineering (BPR) resonate with startling relevance. This brief argues that these foundational principles for organizational excellence are not just relevant, but even more critical today as companies embark on their AI-driven digital journey.

Hammer and Champy called for a "clean slate" approach, inventing entirely new ways of working to achieve quantum leaps in performance. Their vision focused on end-to-end business processes, not siloed tasks, demanding cross-functional collaboration and aiming for dramatic (10x+) improvements rather than marginal gains. They saw technology not as a tool for automating existing steps, but as a catalyst for fundamentally new process designs, enabling the "obliteration" of old work. All reengineering efforts were driven by a customer focus and required strong, unwavering top-down executive leadership due to their radical nature.

However, the 1990s also provided cautionary tales from widespread ERP implementations. Many companies stumbled by focusing solely on cost-cutting and layoffs, underestimating the human element by failing to prepare or engage employees. A lack of clear executive sponsorship often led to initiatives losing momentum or being overtaken by technology teams, while attempts to simply "automate a mess" amplified existing inefficiencies rather than solving them. Crucially, ignoring organizational culture and adoption of streamlined processes often led to internal friction and rejection of new ways of working.

Reengineering with AI

For business leaders, effectively integrating AI into reengineering efforts demands a strategic, top-down approach that prioritizes a deep understanding of operations before deploying new technologies. This begins with establishing a comprehensive end-to-end process taxonomy to map and define all existing business processes, ensuring a common language and clear understanding across the organization. Following this, a comprehensive current state assessment is crucial to identify bottlenecks, inefficiencies, and areas ripe for AI-driven transformation, allowing leaders to pinpoint where AI can deliver the most significant impact.

Based on these insights, the next step is to define a future state operating framework that outlines how processes will be radically redesigned, incorporating AI to automate, augment, and even "obliterate" steps. This framework must clearly articulate new roles, responsibilities, and how human-AI collaboration will function. A critical component then becomes determining the quantifiable value of these reengineered processes, moving beyond simple cost-cutting to identify new revenue streams, enhanced customer experiences, and improved decision-making capabilities. Finally, this BPR work must be integrated into a broader strategy for organizational transformation, ensuring that business process changes and AI introduction are synchronized with talent development, change management, and a culture that embraces continuous innovation.

AI and GenAI Opportunities for Transformation

Today, AI and GenAI offer an unprecedented opportunity to revisit Hammer and Champy's vision of radical redesign. These technologies enable a level of process obliteration and intelligent automation that was only theoretical decades ago. AI agents can now operate semi-autonomously, analyzing data, orchestrating complex tasks, and executing decisions, truly obliterating entire steps or roles. This amplifies the need for Process Intelligence, as AI leverages historical data to uncover trends and identify optimization opportunities at a scale previously unachievable, even allowing for AI-driven simulations. Furthermore, GenAI, particularly Large Language Models (LLMs), thrives on unified knowledge and semantic management, underscoring the critical need for mature frameworks like taxonomies and knowledge graphs to feed LLMs with precise, actionable outputs. Finally, AI and automation initiatives are only as good as their data. Thus, the criticality of data lineage for trustworthy AI ensures transparency, traceability, and trust in the data fueling these models.

Guideposts for AI Implementation

As organizations embark on AI and GenAI implementations, the lessons from the 1990s serve as critical guideposts. Avoiding past mistakes will be key to unlocking transformative value:

• "Reengineer Before You AI": Resist the temptation to simply apply AI to existing, inefficient processes. AI will only automate and amplify existing problems. A fundamental rethink and radical redesign of processes (BPR principles) must precede or accompany AI implementation to achieve true transformation.

• Beyond Cost-Cutting: Focus on Value Creation and Competitive Advantage: Do not view AI merely as a tool for efficiency. Like BPR, AI should be leveraged to create new services, personalize customer experiences, accelerate innovation, and achieve a sustainable competitive edge. It should not be thought of as a technology to just reduce headcount.

• Prioritize Human-AI Collaboration and Reskilling: The human element is still paramount. Underestimating the need for comprehensive training, reskilling, and upskilling employees to work alongside AI agents will lead to resistance and adoption failures. A focus on "augmented intelligence" rather than "artificial intelligence" alone is crucial.

• Define a Clear AI Strategy with Executive Alignment and Oversight: AI initiatives must be driven by a clear, top-down strategy that aligns with overall business objectives. Without strong executive sponsorship and governance, AI projects risk remaining isolated within IT, failing to scale, or creating unforeseen ethical and operational challenges.

• Address Ethical AI, Trust, and Transparency: The data quality and lineage issues of the 90s are compounded by AI. Organizations must proactively establish frameworks for ethical AI development, ensure data privacy, mitigate algorithmic bias, and build transparency into AI systems to foster trust with customers and employees.

The adage "What's old is new again" rings true for business and digital transformation today. The foundational concepts championed by Hammer and Champy (radical redesign of end-to-end processes, empowered by technology, and driven by customer value) are more relevant than ever. AI and GenAI provide the unprecedented capabilities to truly realize the revolutionary promise of BPR. By embracing a holistic, strategic approach grounded in mature knowledge, process, and data capabilities, and by learning from the missteps of past digital journeys, organizations can transition from reactive operations to proactive, information-driven automation, positioning themselves to lead in a dynamic, AI-powered future.