Rewired

Rewired Plot Summary

Introduction

Organizations today face a critical challenge: how to transform their business in an increasingly digital-driven landscape where artificial intelligence is redefining competitive advantage. Many leaders have witnessed the widening gap between digitally advanced companies and those struggling to keep pace. The question is no longer whether to embark on a digital transformation journey, but how to execute it successfully when the majority of such initiatives fall short of expectations. The key to outcompeting in the digital age lies in developing six fundamental enterprise capabilities that enable companies to continuously innovate and adapt. This journey involves aligning leadership around a clear vision, building a strong digital talent bench, adopting an agile operating model, creating a flexible technology architecture, transforming data into strategic assets, and implementing effective adoption mechanisms. Success requires not just implementing new technologies, but fundamentally rewiring how the organization operates to harness their full potential.

Chapter 1: Establish a Clear Vision and Leadership Alignment

Creating a compelling vision for digital transformation begins with establishing three foundational elements: vision, alignment, and commitment. Without these, even the most sophisticated technology implementations will fail to deliver meaningful results. A strong vision articulates not just an aspiration but includes a timeframe and quantifiable value, such as "Provide a frictionless customer and employee experience by leveraging AI across our core operational processes to deliver industry-leading customer satisfaction and a 15% lift in EBIT in three years." At a large agricultural company, the CEO and top team recognized competitive pressure from digital entrants and identified pain points they could address to improve cross-selling and customer retention. Rather than launching disconnected pilots, they developed a comprehensive vision that reimagined how they would serve their growers through digital solutions. They identified priority domains where digital would create the most value, focusing initially on supporting agronomists in better serving customers and making it easier for growers to do business with the company. The company's leaders recognized that alignment meant more than agreement—it required everyone understanding their respective roles. This was particularly important because their digital transformation demanded tight cross-functional collaboration between sales, marketing, pricing, customer service, and order fulfillment to successfully shift to digital channels. Research shows that companies reporting successful transformations are nearly four times more likely to report shared accountability. To build this alignment, the agricultural company invested in experiential learning journeys, including visits to digitally advanced organizations, executive training on digital fundamentals, and workshops to build pattern recognition. Each top executive devoted at least 20 hours to learning before engaging in defining the digital roadmap—proving to be the most critical activity in the early stages of transformation. The company's commitment manifested in four concrete ways: a compelling digital business case with clear performance improvements, real investments in foundational enterprise capabilities, CEO-led transformation governance, and executive role modeling. The CEO personally championed being customer-focused, collaborative, tech-savvy, and agile—all qualities of great digital leaders. By establishing this foundation of vision, alignment, and commitment, the agricultural company created the conditions for meaningful digital transformation rather than pursuing "digital magic"—the mistaken belief that small investments can create outsized value without fundamentally changing how the organization operates.

Chapter 2: Build a Strong Digital Talent Bench

No company can outsource its way to digital excellence. To truly compete in the digital age, organizations need their own bench of digital talent—product owners, experience designers, data engineers, data scientists, and software developers—working side by side with business colleagues. This requires a strategic approach to talent planning that begins with a crucial question: "Do we need to own this talent?" Freeport-McMoRan, a global mining company, faced this question when embarking on its AI transformation journey. Rather than relying solely on external partners, they made the strategic decision to build internal capabilities. They started with a first class of 16 upskilled data scientists who came from process engineering or metallurgy backgrounds across the company, supplementing them with external data engineering experts. They adopted a "buy (hire), build (upskill), borrow (contract)" talent strategy that allowed them to move quickly while developing core skills internally for long-term competitive advantage. One apprenticing data scientist joined Freeport just a year earlier as a junior metallurgist working at a mine in Arizona. She had some experience with computer programming in college and was excited by the opportunity to learn new skills. Less than three months later, she was presenting her work modeling and optimizing concentrators to the president of the business—demonstrating how Freeport attracted talent by ensuring data scientists and engineers worked on management's top priorities. To systematically build their talent bench, successful companies like Freeport establish a Talent Win Room (TWR)—a dedicated team focused on adapting HR processes specifically for digital talent. This interdisciplinary team works in an agile fashion to redesign and execute new talent acquisition approaches, conducting candidate-centric recruiting that demonstrates speed, relevance, and agility from the first interaction. An effective approach to digital talent also includes creating flexible career paths. While some digital colleagues want to progress into general management roles, more than two-thirds of developers don't want to become managers. The best companies implement dual career tracks where individuals can grow either in a traditional managerial path or in an expert engineering path that allows them to keep their craft sharp and pursue ever more sophisticated digital challenges. Companies must also recognize that digital talent has a keen understanding that their value is tied to their skills. Continuous learning is essential in a rapidly evolving technology landscape. DBS Bank, for example, invested heavily in developing a learning infrastructure with multiple programs: a curriculum that equipped employees with data translator skills, an innovation hub that organized over 300 hackathons, and a peer-to-peer learning culture that trained more than 5,000 employees in digital and analytics capabilities. By embracing these approaches to talent, organizations create an environment where digital professionals can thrive, ensuring they have the skills needed to drive competitive advantage through technology innovation.

Chapter 3: Adopt an Agile Operating Model

The concept of agility has become almost cliché, but it remains at the heart of what it takes for companies to operate at the pace of digital innovation. Building and scaling digital solutions requires organizations to be much faster and more flexible in how they develop technology, and having an agile operating model is the way to achieve this. However, this represents perhaps the most complex aspect of digital transformation because it touches the core of how people work together. ING Bank undertook this challenge by completely rethinking its operating model. Rather than simply implementing agile ceremonies, the bank recognized that effective agile transformation required a focus on outcomes rather than activities. As Ken Meyer, Truist's chief information and experience officer, explained: "One of the key shifts was focusing on the kind of value we were trying to drive. Changing the model to focus on the employee experience, as opposed to focusing on whether projects were delivered on time and on budget, was a fundamental change." ING's approach centered around creating autonomous, cross-disciplinary teams organized in a product and platform model. Each product team was responsible for a specific business capability, with engineers, designers, product owners, and business experts working together to continuously improve their product. Platform teams provided standardized, reusable components that multiple product teams could leverage, enabling faster development and greater consistency. The bank discovered that getting three agile ceremonies right was crucial for success. First, they established a rigorous process for setting missions and objectives and key results (OKRs) for each team. Each OKR was tied to business outcomes that everyone on the team shared, with objectives that were bold and specific, but limited in number to maintain focus. Second, they implemented disciplined two-week sprints where teams would develop features, test them with users, learn, and adapt quickly. Finally, they instituted quarterly business reviews (QBRs) where leadership took stock of progress and value delivered, redirecting teams if needed. ING found that this operating model required changes to how performance was measured and incentivized. They shifted from evaluating individual contributions to team outcomes, and from annual reviews to more frequent, informal check-ins focused on professional development. Leaders became coaches rather than managers, helping teams remove obstacles and secure resources rather than directing their work. This cultural shift was as important as the structural changes in making agile successful. As ING scaled from a few agile teams to hundreds, they discovered the need for coordination mechanisms to manage dependencies between teams. They established chapters (communities of practice for specific roles like product owners or engineers) to share knowledge and ensure consistency, while also creating clear governance processes for decision-making across teams. This balance of autonomy and alignment was critical to scaling agile beyond pilot projects to transform the entire enterprise. Through these changes, ING was able to deliver new features to customers in days instead of months, respond more quickly to market changes, and significantly improve customer satisfaction—proving that an agile operating model could deliver concrete business results.

Chapter 4: Create Technology Architecture for Innovation

Technology architecture forms the backbone of any successful digital transformation, enabling teams throughout the organization to continuously develop and release innovations to customers and users. Yet many established companies struggle with legacy systems that were designed for stability rather than adaptability, creating a significant barrier to digital progress. Building a modern technology environment requires rethinking the fundamental architecture to support speed and distributed innovation. Amazon provides an instructive example of how architectural choices can enable or constrain innovation. Jeff Bezos famously issued a mandate that changed Amazon and the world of software. He required all teams to expose their data and functionality through service interfaces (APIs), with no other form of interprocess communication allowed. These interfaces had to be designed from the ground up to be externalizable, and anyone who didn't comply would be fired. This approach decoupled Amazon's systems, allowing teams to innovate independently without creating ripple effects across the organization. Emirates NBD, a leading bank in Dubai, followed a similar path when undertaking its API transformation. As Saud Al Dhawyani, the bank's Chief Technology Officer, explained: "We prioritized our APIs by structuring the existing services in standard banking domains such as customer and product. We also prioritized certain non-banking APIs as 'common' or 'channel engagement,' such as campaigns, offers, and OCR functionalities." This architectural shift enabled the bank to migrate from a legacy enterprise service bus to microservices accessible via standard APIs, ultimately developing roughly 800 microservices. The bank invested heavily in building developer-friendly tools, including a user-friendly portal with good documentation and search functionality. They trained developers on API guidelines and standards from the beginning, laying the right foundations to scale when the time was right. While initially facing challenges in finding the right talent, they succeeded through a balanced approach of hiring and developing existing talent, establishing dedicated learning journeys with internal and external courses and certification programs. Beyond APIs, modern technology architecture requires a shift to cloud computing, providing flexibility, stability, and speed. Companies like Freeport-McMoRan found that migrating to the cloud enabled them to access scalable computing resources, modern development tools, and advanced analytics capabilities. The company developed a cloud foundation with clear standards and governance, allowing teams across the organization to innovate without creating technical debt or security vulnerabilities. Another crucial architectural shift involves automating the software development lifecycle. Netflix created a cloud-based IT architecture that allows its developers to launch hundreds of software changes daily. Each service is developed by a dedicated team using continuous integration/continuous delivery (CI/CD) pipelines that automatically build, test, and deploy code changes. This automation enables Netflix to deploy new code within hours, while most companies would need months. By making these architectural changes, companies create an environment where hundreds of teams can innovate independently while maintaining system integrity and security. This technological foundation becomes a source of sustainable competitive advantage, enabling the organization to respond rapidly to market changes and customer needs.

Chapter 5: Transform Data into Strategic Assets

In established companies, data is often a source of frustration, trapped in legacy systems and difficult to access. As much as 70% of the effort in AI solution development involves wrangling and harmonizing data. For this reason, successful digital transformations require architecting data thoughtfully for easy consumption and reuse, making it a strategic asset rather than a liability. DBS Bank recognized this challenge and launched a comprehensive set of data initiatives to become a truly data-driven organization. They modernized data governance, introduced a new data platform, and drove culture change across the organization. Instead of relying on slide decks, they used dashboards to drive decision-making, track performance, and assess impact. This foundation allowed them to radically change how they served customers, using AI to deliver "intelligent banking" with more than 50,000 personalized daily recommendations to consumers. The bank's shift to cloud computing gave them the scale and speed to use AI and machine learning with their data across multiple domains. In marketing, they provided personalized solutions in context; in human resources, they better predicted when employees might consider leaving (resulting in the lowest turnover rate in the industry); and in compliance, they developed comprehensive surveillance processes for anti-money laundering. Through these AI initiatives, DBS generated an estimated S$150 million in additional revenue and another S$25 million from loss prevention and increased productivity in a single year. Central to DBS's success was treating data as a product rather than just raw material. They created high-quality, ready-to-use data sets that teams across the organization could easily access and apply to different business challenges. Each data product had a dedicated owner and a cross-functional team funded to build and continually improve it, enabling new use cases to be delivered as much as 90% faster than traditional approaches. DBS complemented this with a federated data governance model where a central function set policies and standards, while business units managed day-to-day data activities. They established forums at both the domain and executive levels to align on strategy and address roadblocks. They also implemented DataOps practices to reduce the time needed to develop new data assets and update existing ones while boosting data quality. By taking this comprehensive approach to data, DBS transformed it from a neglected byproduct of business operations into a strategic asset that drove innovation and competitive advantage. Their experience shows that with the right architecture, governance, and culture, data can become a powerful engine for digital transformation.

Chapter 6: Drive Adoption and Scale Solutions

Even the best digital solutions fail to deliver value if users don't adopt them or if they can't be scaled across the enterprise. This is the "last mile" challenge that derails many digital transformations, with leaders lamenting: "We seem to be forever stuck in pilot purgatory," or "The pod delivered a good solution but business would not adopt it." Resolving these issues requires a sustained commitment to managing digital solutions through the entire process from development to adoption. Freeport-McMoRan demonstrated this commitment when implementing its throughput-recovery-optimization-intelligence (TROI) solution for optimizing set points in copper concentrators. Rather than simply delivering the solution, the development team worked side by side with frontline users for eight months after initial rollout. They created check-ins every three hours, 24/7, bringing together operators, mill engineers, and metallurgists to discuss the AI model's recommendations and make operational changes in real time. This approach ensured that frontline teams knew how to use the solution, learned to trust it, contributed to improving it, and became true advocates. The results were impressive: in just one quarter, throughput at one mine exceeded 85,000 tons of ore per day—10% more than the previous quarter—while its copper-recovery rate rose by one percentage point and operations became more stable. This achievement was particularly noteworthy in an asset that had been operating for more than 50 years. Scaling this success across multiple mines required Freeport to "assetize" its solution—refactoring and repackaging it so it could be easily adapted to different environments. The modular design allowed 60% of core code to be reused, while the remaining 40% was customized for each site. The company also invested in a centralized code base that site-specific modules could call on rather than recreating code for each implementation. This approach accelerated deployment while maintaining solution quality. Vistra, a leading energy company, took a similar approach when scaling AI solutions for optimizing its power plant fleet. From the beginning, designers worked with operators to understand their daily activities, ensuring the AI tools made their lives easier. The solutions were integrated into interfaces operators already used, with simple displays showing green when plants ran optimally and red when they didn't, alongside recommended actions and their associated value. When a solution demonstrated value at a pilot site, a team of software and ML engineers immediately took over to refactor, modularize, and containerize the code, creating a single core package that could be updated and improved. Dedicated customization teams then worked with each plant to tailor the solution to its unique conditions, while an MLOps infrastructure brought live data from all power units into a single database for monitoring and continuous improvement. These examples illustrate that driving adoption and scale requires both technical excellence and deep attention to human factors. Companies must design solutions that fit naturally into users' workflows, provide clear training and support, and adapt the broader business model to accommodate new ways of working. This comprehensive approach turns promising pilots into enterprise-wide transformations that deliver sustainable value.

Chapter 7: Foster a Digital-First Culture

Digital transformation ultimately succeeds or fails based on an organization's culture—the mindsets and behaviors that either accelerate or impede progress. While technology provides new capabilities, it's people who determine whether and how those capabilities are used. Building a digital-first culture requires intentional effort across leadership development, broad-based learning programs, and role-specific reskilling. DBS Bank understood this challenge when they set the goal of becoming a "30,000-employee start-up." The Singaporean multinational bank recognized that fostering a strong experimentation culture was essential to their transformation. They invested heavily in learning infrastructure, establishing multiple programs including a curriculum that equipped employees with data translator skills, an innovation hub that organized over 300 hackathons, and a "hack-to-hire" program that resulted in hiring more than 200 employees. Through these initiatives, DBS trained over 5,000 employees in various digital and analytics capabilities. Of these, more than 1,000 employees were upskilled and moved into more pivotal roles for the digital transformation. The impact was significant: employee engagement increased by six percentage points, and employee retention improved by 40%. This demonstrates how investing in people's capabilities creates a virtuous cycle that reinforces the digital culture. Roche, the pharmaceutical company, took a different approach by focusing intensively on senior leadership. They launched a change process that invited more than 1,000 leaders to learn a new, more agile approach to leadership through a four-day immersive program. This experience introduced them to the mindsets and capabilities needed to lead an agile organization. Within six months, 95% of participants had launched agile experiments with their own teams, engaging thousands of people in co-creating innovative ways to embed agility within the organization. Majid al Futtaim (MAF), a real estate and retail conglomerate in the Middle East, developed a "school" of analytics and technology to build the capabilities of its 40,000 employees. They detailed specific learning objectives for five segments: senior executives, tech experts and business practitioners, mid-level managers, frontline staff, and entry-level practitioners. They then designed learning curricula and journeys tailored to each group, with a healthy mix of simulations and games to bring skills to life in real-world scenarios. These examples highlight several principles for fostering a digital-first culture. First, leadership must model the desired behaviors, demonstrating curiosity, collaborative decision-making, and comfort with technology. Second, learning should be continuous and relevant to people's roles, with opportunities to immediately apply new skills. Third, traditional career paths and performance management need to evolve to reward digital behaviors and capabilities. Perhaps most importantly, organizations must recognize that culture change isn't separate from the work of digital transformation—it emerges from it. By engaging people in meaningful digital initiatives, providing the skills they need to succeed, and celebrating progress, companies create the conditions for a digital-first culture to take root and flourish.

Summary

Digital transformation represents the defining business challenge of our time—one that requires organizations to fundamentally rewire how they operate. Success demands developing six interconnected enterprise capabilities: a clear vision with aligned leadership, a strong digital talent bench, an agile operating model, technology architecture for innovation, data as strategic assets, and effective adoption mechanisms. Companies that excel in these areas create sustainable competitive advantages that others struggle to match. The journey is both technical and human, requiring investments in technology, processes, and people. As Jeff Bezos aptly noted, "It's still Day 1" for digital transformation—a recognition that this work is never truly complete but rather an ongoing evolution. The most successful organizations approach this challenge with humility, curiosity, and determination, continually learning and adapting as technologies and possibilities evolve. Take your first step today by selecting one capability area where your organization faces the greatest gap, and begin the rewiring process that will position your enterprise for success in the age of AI.

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Eric Lamarre

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