The AI transformation manifesto

The companies that are truly innovating with AI are doing something very different from their peers: They are conceptualizing and developing AI capabilities that reshape their products, services, core business processes, and organizational systems.

These leading companies—many profiled in the second edition of our seminal book, Rewired: How Leading Companies Win with Technology and AI—are already realizing game-changing results and creating competitive advantage. Their advantage, however, does not come from the tech they use; those tools are broadly available. Their advantage comes from how—and how fast—they apply technology to solving real business problems at scale.

We summarize our perspective on how they do it in this AI transformation manifesto. This declaration captures the defining themes that separate the companies that are successfully transforming their business with tech and AI from those that are not. And while there’s no question that agentic AI is pushing the boundaries of what’s possible, the themes are enduring because they focus on what it takes to harness technology to drive business goals.

These themes are extracted from the six capabilities that are featured in Rewired: strategic road mapping, talent, operating model, tech, data, and adoption and scaling. In calling these themes out, we are highlighting what our work on hundreds of large-scale tech and AI transformations has shown really makes a difference. These themes should become a checklist for change and operate as guideposts along your transformation journey to value.

1. Technology alone doesn’t create advantage; enduring capabilities do. Who are the early winners at AI? The same companies that have been winning before by building capabilities that allow them to harness any technology effectively. We call them Rewired companies. When these new capabilities are built—and they take time to build—the company accelerates its business transformation with technology and outperforms its peers. The capabilities become the competitive advantage.

   Are you building enduring capabilities for the journey, or merely delivering one-off solutions?

2. Economic leverage points are your best focal points. Any business model has a few key economic leverage points that provide the biggest impact when improved with AI. In mining, for example, process yield and throughput is a key economic leverage point, and that’s where Freeport-McMoRan achieved game-changing impact. In automotive, supply chain integration is a key leverage point, and that’s where Toyota had its AI breakthrough. Most companies have long lists of use cases. Successful ones focus on achieving deep business transformation in the few areas that matter strategically. That’s where they double down to build AI systems.

   Have you disproportionately focused your AI efforts on your economic leverage points?

3. If the value you’re creating doesn’t move the business, you’re getting it wrong. We studied the impact achieved by 20 companies across industries that have proved themselves to be leaders in AI. On average, their technology- and AI-driven business transformations delivered a 20 percent EBITDA uplift, reached breakeven in one to two years, and generated $3 of incremental EBITDA for every $1 invested. These companies concentrated their efforts on one to three business domains, reinventing them with AI. That required creative problem-solving, coordinated use of tech and nontech levers, maniacal focus on the customers/users, and clear accountability for the business KPIs that mattered most. They made substantial, stage-gated investments and still continue to improve and stay ahead.

   Will your business transformation plan result in game-changing value, or will the wins be incremental?

4. Building the tech and AI muscle of your senior business leaders should be a top priority. We don’t have a single success story where senior business leaders were not in the driver’s seat. IT leaders can support the transformation, of course, but it’s business leaders who need to drive it. At leading companies, they actively own the tech agenda—from defining how the business will be reimagined with technology to steering solution development to ensuring value delivery. These leaders, usually one or three levels below the CEO, combine deep business domain expertise with technology, data, and AI know-how that makes them formidable business transformers. They are conceptualizing, building, and running AI systems that power key aspects of the business.

   Are your senior business leaders tech- and AI-capable?

5. Every tech and AI transformation is a people transformation. Leading companies increase their tech talent capability and density by following what we call the “30–70 shifts”: more than 70 percent of talent should be in-house, more than 70 percent of them should be “doer” engineers who build great software-based solutions, and more than 70 percent of them should perform at higher skill levels (that is, competent or expert). This produces small, highly skilled teams that outperform large armies of lower-skilled staff. On the business side, leaders evolve into domain and solution owners, accountable for outcomes and leading cross-functional agile teams. Leading companies have largely completed this transition, which results in higher talent density and much tighter business ownership.

   As AI agents take on more of the coordination, execution, and routine decision-making work, human roles shift up the value stack. Engineers spend less time on routine coding tasks and more time designing architecture, workflows, constraints, and quality controls. Business and solution leaders focus less on task management and more on setting objectives, defining success metrics, and making trade-offs. The result is fewer people doing higher-leverage work, with clearer accountability and faster learning loops.

   Have you progressed enough on your people transformation?

6. Speed is the defining organizational advantage. Businesses are in an innovation race with companies that have access to the same technologies. Companies win that race when their operating model redeploys resources more rapidly to important opportunities, empowers teams to act without excessive dependencies, and reduces the “latency” from insight to decision and decision to action. Speed requires embedding AI engineering and other functional talent directly in the business, maximizing technology and data reuse through platforms, and governing with clear business outcomes and sustained funding tied to results, not projects. This shortens cycle times dramatically. Without it, no company can truly innovate with technology and AI at scale; they will simply be too slow.

   What are you doing to increase the metabolic rate of your organization?

7. Tech platforms are strategic assets; invest in them that way. Platforms determine a company’s execution speed, drive down its unit costs through reuse, get technology and data into the hands of the people who need them, and enable AI to scale responsibly. They provide standardized, safe, and shared tech and data capabilities that teams can access. Leading companies manage their platforms strategically with dedicated teams, road maps, budget, target service levels, and users whose needs shape how the platform evolves. As a senior executive, understanding your technical architecture, the latitude it gives you, and how it drives competitive differentiation is now as essential to leading a modern company as knowing your profit and loss.

   Are platforms understood and discussed as strategic assets?

8. Make data easy to consume—and enrich it for advantage. As David Baker, winner of the 2024 Nobel Prize in Chemistry, observed when reflecting on recent breakthroughs: “AI needs masses of high-quality data to be useful.” Without good data, AI breakthroughs are impossible. Yet in most organizations, data often still acts as the constraining factor. Scaling AI therefore starts by productizing data—making it easy to discover, access, and consume across many AI-powered applications. That requires investments in building data products. Over time, the game shifts to data enrichment, deepening its quality, context, and uniqueness for sustained performance gains with AI. In Rewired organizations, data is a business-owned performance asset.

   Can your teams easily consume your data, or are they still wrangling it?

9. Design for adoption and build for scale. AI systems create value only when they are adopted and scaled. That may sound obvious, yet it remains one of the hardest challenges. Adoption often fails because adjacent upstream and downstream processes are left unchanged. An AI solution may predict equipment failures days in advance, but if maintenance still follows calendar-based scheduling, nothing happens.

   Scaling is a different, but equally difficult, challenge. Expanding AI solutions quickly and economically across markets, factories, customer segments, or product lines requires modular solution architectures and a well-choreographed dance between central teams and the receiving units. These considerations—including required investments and run costs—must be addressed up front, not retrofitted later.

   Can your organization repeatedly adopt and scale AI, or is it still relying on isolated heroics?

10. No trust, no right to deploy AI. When AI systems fail, they challenge trust with customers, regulators, employees, partners, and society at large. Digital trust grows when stakeholders have confidence that your organization protects consumer data, enacts effective cybersecurity, offers trustworthy AI-powered products and services, and provides transparency around AI and data usage. The challenges are only increasing with the expansion of agentic technologies, requiring much more time for testing agentic systems and automating risk controls. It’s a fast-moving space, and the excitement for agentic AI may be getting ahead of companies’ ability to manage the more complex risks associated with the technology.

    Would your AI deployments withstand public, regulatory, and customer scrutiny today?

11. Agentic engineering becomes the next capability to master. Foundation models are now capable of sustained, autonomous work over long periods, making it possible to build complex agentic workflows. Nowhere is this more evident than in software development, where the productivity gains have been astonishing. Leading companies are moving quickly to master agentic engineering. They are ingesting unstructured data, extending their AI platforms with agentic capabilities, automating guardrails and controls, and rapidly experimenting to codify what works into a repeatable agentic playbook. We’ve seen this pattern before. Rewired leaders consistently absorb new technologies faster because they’ve built the underlying capabilities to do so.

    Will agentic workflows be your next engineering advantage—or your next catch-up problem?

12. (Re)learn like your business depends on it. One of the reasons we love working in this space is that it’s constantly changing. The half-life of skills is shortening as innovation accelerates. The organizations that learn, unlearn, and relearn the fastest have the advantage. Taking your leadership team on learning journeys is the most important thing a CEO can do to effectively accelerate business transformation with AI. As we have observed numerous times, these journeys are crucial for the top team to reach the point of conviction when both the strategic opportunity and transformation pathway become clear. At that point, every C-suite leader understands their role and the transformation truly accelerates.

    Becoming the leader this era demands starts with committing to continuous learning; are you personally investing enough?

Building the complete set of rewired capabilities is the cornerstone of every successful tech and AI transformation. Companies can accelerate their way through developing them, but they cannot skip over the foundational work. This gets at the idea of compounding value as capabilities build off one another and competitive distance increases. That’s how leading companies consistently outperform their peers, again and again.