The distance between digital and AI leaders and other industry players is big, and it’s getting bigger. Over the past three years, the spread in digital and AI maturity between leaders and laggards has increased by 60 percent.
This development provides a compelling counterpoint to the underwhelming results of digital and AI transformations many companies have experienced to date. Earlier research has shown that companies on average have captured less than a third of the value they expect from their digital transformation initiatives, despite significant investment. But a set of leading companies are not just figuring out how to harness digital and AI to generate value but are also doing it more quickly and putting ever more distance between themselves and other players.
To better understand the scope of this value premium and how leading companies are creating it, we undertook detailed analysis of more than 1,000 companies, with a deep dive into the banking sector (see sidebar, “More about the research”). Key findings include the following:
- The value of digital and AI is real and sizeable. Evidence suggests that companies that have leading digital and AI capabilities outperform laggards by two to six times on total shareholder returns (TSR) across every sector analyzed.
- The distance between leaders and laggards is increasing because digital and AI, when implemented well, provide compounding advantages.
- Leaders implement digital and AI by investing in a holistic set of hard-to-copy capabilities.
- Laggards can catch up if they’re willing to commit to the hard work of rewiring how their companies run.
The value of digital and AI capabilities is real
While most business leaders may accept the need to incorporate digital and AI more deeply into their business, many remain skeptical of the effort and investment involved, wondering if it will add up to sustainable outperformance. Our data suggests that building up digital and AI capabilities adds up to real value, a trend that holds true for every sector analyzed (Exhibit 1). For example, digital leaders in insurance have five-year growth in TSR that is six times higher than lagging companies. Consumer packaged goods (CPG) and retail leaders perform three times better than peers in their sector, while companies in energy, materials, and agriculture perform two times better.
But how do these innovative capabilities directly drive outperformance? The retail banking sector offers a strong example of this linkage. Between 2018 and 2022, digital leaders in banking achieved average annual TSR of 8 percent, versus 5 percent for laggards (Exhibit 2).
There is an important distinction between creating a digital channel and translating the use of that channel into value. From 2018 to 2022, while both leaders and laggards increased mobile app adoption, leaders maintained, though did not increase, their advantage. More importantly, by better integrating digital and AI throughout the entire customer journey and thereby reducing friction points, leaders extended their advantage in online sales. At the same time, they progressed more quickly on omnichannel customer interactions, supported by automation and analytics, dramatically reducing the cost to serve. These effects contributed to their profit-and-loss (P&L) edge and multiple expansions, resulting in significant TSR outperformance.
The compounding value effect that increases distance over laggards
As might be expected, levels of average digital maturity varied by sector, with retail and high tech leading the pack. More meaningfully, however, spreads in digital and AI maturity between leaders and laggards are substantial and growing within every sector (Exhibit 3). Even in many sectors where average maturity is relatively low, there are businesses that operate as top digital and AI companies.
This digital and AI leadership is not static. Leaders increasingly pull ahead in terms of their digital and AI capabilities, which, over time, provide a compounding effect in terms of performance advantage. The average spread of digital and AI maturity scores between the top and bottom performers has jumped 60 percent between the two periods studied (from 2016–19, when the spread was 10 points, to 2020–22, when it was 16 points) (Exhibit 4). In other words, the capabilities that leaders in digital and AI have invested in are continuing to improve, creating even more distance from laggards.
This effect is true in every sector analyzed. The compounding benefit was greatest in high tech, banking, and insurance, where the underlying business models have particularly benefited from their reliance on data and software. The rate of change for media, entertainment, and healthcare has been particularly pronounced, likely accelerated by the need to build out capabilities during the COVID-19 pandemic as people consumed more digital media at home and turned to digital health options more frequently.
Why leaders are pulling ahead: Building capabilities, not just digital and AI products
The compounding effect of digital and AI happens because leading companies rewire their organizations. This provides them with hard-to-copy capabilities and allows them to better identify where their business model could be improved with technology, to develop digital solutions, and to effectively drive their adoption and scaling. As a result, these companies can target value better, go after it faster, and capture a greater share of it, repeatedly and consistently. This is the hard work of digital and AI transformations.
But which factors contribute the most to this ability to execute? Our analysis of more than 200 large-scale digital and AI transformations highlights key capabilities in the following six key areas (Exhibit 5):
- Strategic road map: Align top management around an opportunity to improve the performance of a business domain (be it a customer journey or a core business process) with technology to generate value. This includes committing to making sufficient investments to sustain the transformation.
- Organization and talent: Ensure a digital talent bench with top technologists who can both rapidly iterate and deploy solutions and understand business models and data.
- Operating model: Develop a successful operating model that allows hundreds of cross-functional teams to work autonomously on products (offerings or services used by customers and employees) and platforms (the back-end technology and data capabilities that support products).
- Technology: Create a distributed technology environment to enable teams to access the data, applications, and software development tools they need to rapidly innovate and build. This requires a modular tech stack based on the cloud that makes it easy to access capabilities and upgrade components over time without affecting the rest of the architecture.
- Data: Build a data architecture centered around the development of reusable data products. These are curated and packaged data elements that teams and applications can easily access and use in different products.
- Adoption and scaling: Plan, resource, and govern change management from beginning to end. This allows the organization to broadly adopt new digital and AI products and turn them into assets that can be reused across lines of business and markets.
Not only do leading companies focus on building this broad set of capabilities; they are also substantially better than their peers across all of them, performing 2.0 to almost 2.5 times better in each one. Companies don’t necessarily need to be exceptional in every capability, but they do need to hit a baseline of competency across all of them. That’s because these capabilities are mutually reinforcing. Building a strong talent bench, for example, allows a company to develop better technology and data capabilities. This pattern holds true within each sector analyzed. It is telling that the largest gaps are in creating a strategic road map to realize value and in adoption and scaling, as opposed to in technology: being successful in digital and AI is less about technological tools and more about how well business leaders align their organization.
As generative AI begins to unlock significant new capabilities in knowledge work, marketing, and customer service, we expect this gap to continue to grow. But it will require leaders who can sufficiently develop the talent, data foundation, and execution (“from build through adopt”) muscles.
Laggards can catch up
Understanding what to do is one thing—many of these capabilities are well known to most executives. Understanding how to actually build those capabilities is another. This is the area that stymies most businesses: our in-depth analysis of the banking sector shows that digital and AI capabilities are hard to build and hard to copy from elsewhere.
But laggards can catch up. Companies with aspirations to outperform need to focus on where they can best deploy digital and AI innovations in their business and whether they are building the capabilities necessary to capture value quickly, efficiently, and consistently. The data shows that companies that commit to this level of change can make meaningful improvements (approximately 15 to 20 percent improvement, on average) in digital maturity and increase EBIT by 10 to 20 percent within their targeted domains in two to three years. One global CPG company, for example, focused on rewiring its business over time and was able to move out of the bottom tier of performers, making significant progress toward becoming a digital and AI leader (Exhibit 6).
The company’s turnaround began when it realized it needed to focus on a few priority domains. It had been working on more than 200 digital and AI pilots, but its scattered resources and focus meant that none of these initiatives could break through and capture significant value.
Leadership aligned on three priority domains—digital demand, digital operations, and digital business models—and corresponding use cases with clear estimated values. The CPG company then managed performance through clear metrics and KPIs. It also established strategic partnerships with cloud platform providers to accelerate technical progress and train thousands of employees in key digital skills.
As teams built up their technical and data capabilities in the cloud, the company was able to start making connections between domains. This allowed it to unlock new sources of value. For example, within these domains, it connected use cases on revenue growth management and digital marketing by integrating the data and ROI metrics. It could then understand where to invest to generate greater returns. With maturing capabilities, the company is looking to its next horizons of growth, including automation. These moves have led EBIT to increase by more than $400 million.
Rewiring a business with key digital and AI capabilities constitutes a true competitive advantage. If a company lags for too long, it will be very hard to catch up and remain competitive. The sooner companies commit to building the right digital and AI capabilities, the sooner they can start generating compounding growth.
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