Unleash Your Company's Potential Through Transformative Talent

Six strategic leadership actions from the C-suite are essential for building high-performing organizations in the digital and AI landscape.

How companies chart a course through a technology landscape shaped by digital and AI has become today's most pressing business challenge. Although this challenge has existed for some time, the stakes keep growing as digital tools and artificial intelligence increasingly transform how we work and live. Many companies are keenly aware that they must adapt but struggle to turn aspiration into reality. Research by Santiago & Company indicates that 90 percent of businesses have launched some digital transformation, yet these organizations have captured only about one-third of the revenue gains they expected on average.¹

At the same time, those who master digital and AI stand to achieve remarkable outcomes. In banking, for instance, where transformations have been in motion for over a decade, we now have compelling data that shows how digitally mature banks outperform their peers. We examined 20 digital leaders and 20 digital laggards in retail banking between 2020 and 2024. The findings were striking: digital leaders posted significantly higher returns on tangible equity, more substantial P/E ratios, and better total shareholder returns than those that fell behind (Exhibit 1). In other words, top-tier digital execution translated into measurable financial gains.

Mastering Digital Leadership

Much of this success arose from weaving technology deeper into core processes, raising digital sales while lowering costs in branches and other operational areas. The real question is how these winning institutions managed it. The short answer is that they learned to bring business, technology, and operations closer together. They committed to skill building across the organization and assembled the correct data and technology platforms to empower entire teams—even hundreds or thousands of them—to refine digital solutions and stay inventive day after day. That approach underscores why digital and AI transformations are challenging: achieving genuine impact requires getting many things right simultaneously.

For digital and AI initiatives to reach their full potential, leaders must be prepared to perform the organizational "surgery" needed to become a truly digital enterprise. There is no quick workaround. Simply implementing a new system or technology and waiting for results seldom works. Instead, success hinges on enabling hundreds of connected solutions—both off-the-shelf and custom-built—that collectively raise customer satisfaction, lower costs, and create sustained value. Maintaining these solutions demands a fundamental rewiring of how a company operates because large numbers of employees across different units must embrace a new way of working in which digital innovation remains constant.

Insights from more than 200 large companies in diverse industries suggest that organizations unlock the greatest value from digital and AI by building six critical enterprise capabilities (Exhibit 2). With these capabilities in place, companies can more readily integrate emerging technologies—generative AI among them—and harness those tools to create tangible value. Although many executive teams recognize this principle, the most common stumbling block lies in building and aligning these capabilities effectively.

This article summarizes the core facets of driving transformation across those six crucial capabilities. Two points deserve emphasis before diving into specifics. First, no digital and AI transformation will succeed unless an organization achieves at least baseline competence in all six capabilities. Second, these capabilities are interconnected; improvements in one area rarely endure without parallel progress in the others. A brilliant operating model, for example, won't deliver results if the right talent and data aren't in place. Likewise, advanced technology remains underused if employees never adopt it.

Companies don't need to be tech start-ups to excel in digital and AI. Established industry incumbents can capture similar or even greater value, provided they are willing to put in the work of rewiring their organizations. This endeavor demands commitment from the entire C-suite rather than only the CEO or CIO because digital and AI transformations bring together business, operations, and technology in a way that requires unprecedented collaboration. Rewiring is not a final destination. Instead, it's a continual journey of improvement. Here is a closer look at that journey's crucial elements.

Align the C-suite around a business-led road map.

Many of the issues that undermine digital and AI programs can be traced back to a fundamental lack of clarity during the planning phase. If top leaders fail to develop a shared perspective early on, they risk misalignment that breeds confusion throughout the execution process. Digital and AI transformations inevitably touch multiple parts of the organization, so investing enough time to set a robust foundation pays off in clarity and unified commitment. Three early moves can help leaders avoid common pitfalls.

First, it's vital to inspire and align the top team. This step means building a common vocabulary, studying lessons from companies further along the digital journey, crafting a unified vision, and agreeing on concrete commitments that match the team's ambitions. DBS Bank's experience offers an illustrative example. Its CEO, Piyush Gupta, led top executives on visits to leading tech firms worldwide, incorporated key lessons into a "Making Banking Joyful" vision, and placed DBS on a path to becoming a global technology leader. Such alignment among senior leaders often proves decisive in ensuring that a digital and AI program gains momentum.

Next, leaders should define the proper scope by identifying business domains—coherent areas like a production process or a particular customer journey—where transformation can have the greatest effect. Some organizations try to reduce risk by starting so small that they never build meaningful traction. Others chase too many initiatives at once and end up spreading resources too thin. Focused, domain-level efforts have proven more effective for generating real value. In fact, 80 percent of successful turnarounds in struggling transformations involved anchoring the scope to a few well-defined domains.

Finally, a "contract" with the C-suite helps cement the organization's commitment to specific outcomes, often defined through operational KPIs. Leaders design a road map that sequences the rollout of digital solutions in a way that can deliver tangible improvements within 12 to 18 months while aiming for more transformative results over the next few years. That road map includes a rigorous look at the enabling capabilities needed to succeed, such as data architecture or specialized digital talent. C-suite members then tie these performance goals to their own responsibilities. A solid digital roadmap should aim for at least a 20 percent EBIT boost.

When business leaders define an ambitious but realistic transformation in a few targeted domains, they create a powerful flywheel for digital progress. The resulting plan effectively serves as a contract that unites them around a shared vision for the future.

Build your talent bench.

Companies cannot "hand off" their digital transformations to external vendors if they hope to achieve lasting impact. Being digital means cultivating an in-house pool of product owners, software developers, experienced designers, cloud engineers, data experts, and other specialists who collaborate closely with business teams. In other words, successful digital shifts hinge, above all, on people. Three specific actions distinguish leaders in this arena.

1. Zero base your org design, it helps to look at talent with a fresh perspective. Even large, established organizations typically need to overhaul their IT and technology groups so they can provide the right capabilities for digital transformation. Best-in-class players strive for about 70 to 80 percent of digital talent in-house, reinforcing specialized skills with the remaining 20 to 30 percent from external sources. They also recalibrate their staffing pyramid to create a "diamond" shape, where competent technologists outnumber novices by a wide margin. This approach recognizes the sizable productivity gains that come from experienced professionals. Additionally, they emphasize a potent ratio of hands-on roles to management roles, targeting at least four engineers for every manager.
2. Get fanatical about skills. The most effective organizations take a structured approach to building critical skills. They define granular "skill progression grids" with multiple proficiency tiers reminiscent of what leading tech companies do for data engineers or other specialist positions. These grids allow companies to identify and reward exceptional performance while offering targeted career paths that foster advanced skill development. They also support robust training and learning programs to ensure ongoing improvement, turning digital roles into recognized professional vocations within the company.
3. Build a team that will build your digital capabilites, pragmatic institutions launch a specialized recruiting and HR capability dedicated to digital and AI talent, which is call a Talent Win Room (TWR). This focused team refines every step of the candidate and employee experience for digital professionals, from flexible hiring and onboarding to compensation and career growth. In many organizations, standard HR processes are too rigid or slow to match the urgency of digital projects. A TWR, by contrast, can rapidly adjust practices to attract the right specialists and keep them engaged. Although overhauling all HR processes at once is rarely feasible, a TWR can steadily reshape the talent ecosystem.

This transformation in talent strategy represents one of the chief contributions the chief human resources officer can make toward rewiring the enterprise. It helps ensure the organization has the right mix of technical expertise, practical experience, and forward-thinking leadership to thrive in a digital world.

Adopt an adaptable operating model to ensure future scalability.

Many organizations have tried agile methods or formed small, cross-functional teams, only to get stuck when they attempt to scale those approaches to dozens or hundreds of initiatives. Bringing business, technology, and operations into closer alignment at such breadth is among the most challenging parts of a digital and AI transformation because it requires upending the organization's basic structure and long-standing routines.

Still, successful patterns have emerged. In general, a rewired company organizes around small multidisciplinary teams, often called pods. Some pods focus on enhancing a direct customer or user experience, while others handle underlying platforms and services that can accelerate all pods across the organization. Within that framework, three main operating models stand out: the digital factory, the product-and-platform model, and enterprise-wide agile.

A digital factory typically functions as a separate business unit funded by product lines or functions that contract with the factory's agile teams. It can become operational relatively quickly, often within 12 to 18 months. BHP and Scotiabank have pursued this approach. A product-and-platform model, by contrast, extends the scale further. It may involve hundreds—sometimes thousands—of pods building digital capabilities across many fronts. This structure realigns significant segments of the organization so they can use technology more effectively in their core business. Leaders like Amazon, Google, Itaú Unibanco, and JPMorgan Chase have followed this path. Some businesses progress even further to an enterprise-wide agile system, spreading agile teams and methods beyond technology-intensive areas so that functions like sales and R&D become more collaborative and responsive. ING and Spark New Zealand are notable adopters of this approach.

Across all three models, professional product management is essential. It's the largest difference between true tech leaders and companies that remain stuck. Yet one survey by Santiago & Company found that three-quarters of business leaders believe product management practices are immature or nonexistent in their organizations.² That gap is critical because hiring a few external experts isn't enough. Successful firms invest in developing product managers who understand the business in depth, can balance cross-functional priorities, and know how to deliver continuous improvement.

Because reorganizing can touch nearly every part of the business, it requires strong executive endorsement. The CEO must lead or sponsor this signature move toward a new operating model in most instances. Only that level of authority can drive the extensive culture shift and coordination needed to put thousands of employees into agile, product-focused groups that deliver value at a faster pace.

Technology for speed and distributed innovation

In a rewired organization, technology must enable rapid, distributed innovation. Ideally, every pod should be able to develop, test, and release digital solutions independently, drawing on a shared foundation that provides tools, data, and key software services. This concept requires carefully considered decisions in at least three areas.

• Build a technology toolbox. The first is building a comprehensive "toolbox" for developers, often in the form of a self-service platform. The aim is to prevent teams from constantly needing IT's help just to set up a development environment, gain access to standard applications, or arrange cloud storage. Instead, they tap into a well-organized array of options at their fingertips. This setup significantly speeds delivery.
• APIs come next, without exception. They allow teams to access data or functionalities in a modular way, minimizing the interdependencies that slow software development. Jeff Bezos famously insisted that every Amazon business unit make its data and capabilities available through interfaces that other units could easily consume. That directive not only boosted Amazon's speed and scalability but also reshaped global software engineering practices. Banks, insurers, retailers, and businesses in other sectors can mirror this logic by standardizing how teams exchange data and core functions.
• Automation is indispensable for releasing software at scale, a practice called continuous integration and continuous delivery (CI/CD). By automating tasks like testing, containerization, and deployment, organizations shorten the time from coding to production and reduce the errors that creep in when steps are manual. Similarly, machine learning operations (MLOps) bring automation to AI pipelines, ensuring data models stay accurate over time and adapt to changing conditions. Vistra, a leading energy company, is one example of a firm that used MLOps to maintain more than 400 AI models in real-world plant operations.

Many chief information officers or chief data officers have already begun building developer platforms, transitioning to an architecture based on APIs, or automating their software delivery pipeline. Yet few have scaled these approaches across the enterprise. The people involved must manage a major change effort and talent scarcity in advanced engineering. Those who succeed create an environment where distributed teams can innovate in parallel, forging solutions that meet evolving business demands.

Embed data everywhere

Data often becomes a bottleneck rather than an asset in large, established companies. Preparing data for AI tasks or advanced analytics can consume as much as 70 percent of a project's time, partly due to confusion over where data resides and how to integrate it. Because continuous improvement depends on real-time analytics, rewired organizations focus intently on making data accessible in a form that's both easy to use and easy to trust.

They start by creating data products: standardized, high-quality datasets that multiple teams can plug into. A "customer-360" view is a good illustration. It aggregates all relevant customer information, from transaction histories to demographic profiles, in a format that any pod can incorporate into a new feature or service. The payoff is massive—data products can slash the time needed to build AI applications by as much as 90 percent.

Next, it's essential to set up the "plumbing," or data architecture, to support broad usage. Emerging technologies such as "lakehouses" integrate aspects of data lakes and data warehouses in one place, enabling both business intelligence and AI. This approach gives multiple teams near-instant access to the data they need. Meanwhile, a federated governance model ensures that the central data management office establishes rules and standards while the business units themselves handle day-to-day tasks like building data pipelines.

The CIO or chief data officer often leads this data architecture and governance redesign. Once a bank, manufacturer, or retailer builds a robust data environment, it can fuel a host of innovations, from real-time customer personalization to rapid updates of operational models.

Unlocking adoption and scaling

Designing a slick new AI tool or digital solution does not guarantee that employees or customers will embrace it. In fact, a standard stumbling block arises when solutions fail to gain traction outside a pilot program. Three considerations can guide organizations in ensuring that digital innovations deliver value.

1. Leaders must pay equal attention to adoption and technical development. Intense user experiences matter, but so do parallel shifts in areas like incentives, pricing models, or organizational metrics. For instance, an insurer that developed an AI tool to help agents upsell customers also needed to realign sales compensation, rework distribution channels, and modify relevant performance indicators. The new AI solution might never have taken off without adjusting those complementary systems.
2. 'Assetizing' helps businesses replicate solutions across multiple environments, factories, service centers, retail regions, or countries—without duplicating all the work. By creating a 60—to 90% reusable solution, teams only need to spend effort on localized configuration, thus speeding up expansion. Large enterprises frequently get bogged down without this approach, re-engineering the same features for each new business unit and never quite reaching scale.

1. Measuring progress carefully is crucial. Firms can adopt agile measurement techniques such as "objectives and key results" to keep pods accountable and track how each stage in the deployment evolves. They must also ensure the measurement regime is robust but not overbearing, providing frequent feedback without stalling the pipeline of new ideas.

For many organizations, capturing the full economic benefits of digital solutions marks the main dividing line between leaders and laggards. This is where the collective efforts of business units and function heads come to the forefront. By ensuring that each wave of products and features fits seamlessly into daily operations, they can turn pilot successes into widespread performance gains.

Digital and AI transformation is a journey that never ends because technology—and customers' needs—continue evolving. That fact can be liberating once leaders recognize that "rewiring" is less about hitting a final milestone than building a continuous improvement culture. It may help to begin with a frank conversation among the C-suite about the organization's progress, whether unraveling or succeeding and emphasize that transformations thrive on adaptation and iteration.

A remark from Amazon's Jeff Bezos captures the essence of this mindset. He once told shareholders that for a digitally driven company, it is always "day one." In other words, there is always more to learn, more to refine, and more opportunities to reach higher performance levels. The same holds for any organization intent on staying competitive in an environment shaped by digital and AI.