“He will win whose army is animated by the same spirit throughout all its ranks.” ~ The Art of War, by Sun Tzu.
Picture two firms—same business, similar size and similar target markets. In the first, the CEO talks about data the way they talk about office space: a cost to be managed sensibly. In the second, every business conversation involves the firm’s data and AI capabilities. It doesn’t take a decade anymore: the second is already accumulating more ways to disrupt the first than the first has answers.
The difference was never the technology budget. Leadership that treats data and AI as infrastructure to be managed has already forfeited the compounding advantage, however well it executes everything else; genuine intent rebuilds the organization around intelligence. And AI has shortened the clock on that verdict. Foundations that eroded quietly over a decade now decide who fields a credible AI capability within quarters.
Leadership & Intent, the Prioritize dimension of PRAISE, assesses exactly that difference: whether conviction lives at the top, whether it translates into direction the businesses can act on, and whether it persists as leaders change. The three run in order; each depends on the one before. When leadership gets this right, the rules of the competition change. Few traditional financial institutions demonstrate this more clearly than Capital One.
Capital One — Leadership intent that rewired an industry
Capital One’s transformation into a data-driven institution began in 1988—six years before the company existed. Richard Fairbank and Nigel Morris built their information-based strategy inside Signet Bank and pitched it to the national retail banks. Twenty said no. The credit card division it produced was spun off as Capital One in 1994, with the conviction explicit from the start: an information-based strategy business that happened to sit in financial services. Harvard Business School’s case study of the period documents how data, analytics, and large-scale experimentation were elevated to the core of executive decision-making—credit underwriting, pricing, marketing—long before this became common practice in banking. Data was framed by leadership as competitive advantage tied to growth, risk-adjusted returns, and market share, never as infrastructure or compliance overhead—and the analytics scaled the credit card business through the late 1990s, gaining share while holding charge-off rates at or below the top-ten issuer average through 2001.
The strategy outlived every transition beneath the top. Over the following decades—well before the completed Discover acquisition changed the firm’s scale in 2025—CIOs, data leaders, and business heads rotated and moved on, and initiatives built on prior foundations rather than resetting; the chief data officer role, established more than two decades ago, still reports into the firm’s enterprise leadership. The commitment survived its hardest test mid-flight: a 2019 breach affecting some 106 million accounts and an $80 million regulatory penalty citing the cloud migration itself—and yet no reversal, with the firm announcing in November 2020 that it had exited its last on-prem data centers, the first US bank to go all in on the public cloud. One caveat belongs on the table: Fairbank has run Capital One since the beginning. The strategy never had to survive a change of CEO: continuity under an unchanged founder is a postponed test, not a passed one. The result was still not just operational efficiency, but durable differentiation in how the bank priced risk, acquired customers, and scaled new businesses—proof that when leadership intent is real, data maturity compounds even as executives change.
Read the case back and three things carried it. Leadership held the conviction and owned it: the strategy was the company, not a program inside it. The conviction was articulated as direction the businesses could act on: underwriting, pricing, and marketing knew what the data was for. And what was built compounded: each initiative inherited platforms and momentum instead of replacing them.
The Signals to Look For
1. Is data treated as a first-class business asset?
What to look for: When leadership talks about data, do they frame it as a way the business competes and wins, or primarily as infrastructure that needs to be managed? And perhaps most tellingly: do they talk about it at all?
What you’re looking for here is an intentional adoption of data as a business driver at the very top. The belief and commitment should be owned by the CEO personally and reinforced at the board level. Without that sponsorship, data inevitably defaults to infrastructure: important, expensive, and necessary—but not strategic.
Many digital or transaction-heavy businesses manage data at scale simply to operate. That alone does not make them data-driven in a competitive sense—operating on data is not the same as competing on it. When leadership does not articulate why data matters beyond keeping the lights on, it usually tells you data is viewed as cost, not leverage.
The intent must also be made visible—wherever leadership speaks to the whole organization, and to whoever holds it accountable. Data priorities should appear repeatedly in town halls, board papers, investor communications, internal narratives—not as a technology initiative, but as a way the business wins. Just as importantly, the intent must translate into execution: roadmaps reflect it, departmental goals align to it, and mid-level management is motivated by it and equipped to act on it.
In rare cases, this mindset comes from leaders who have already experienced data-driven advantage firsthand—executives who lived through a transformation elsewhere and know what’s possible. At Capital One, that conviction came directly from the top. In most institutions, however, leaders have not lived through that competitive edge. In those cases, progress depends on change agents—senior leaders who can articulate data’s value in business terms, anchor it to real drivers, and execute with enough consistency to build confidence over time.
At a leading investment firm, I once pitched the heads of independently run businesses, each a CEO in their own function, on a centralized data practice: real engineering muscle, enabled data intelligence practices, broader vendor reach, and a budget contributed by every business. The north star was business efficiency: cutting the time from hiring new investment teams to them trading, and becoming the kind of firm that talent and investors choose, discretionary and systematic alike. The mission was simple and in line with their business goals: strategic enough to remedy challenges at the root, while giving every business breathing room to innovate further.
2. Is there a clear and credible data strategy?
What to look for: When you hear about the data projects and roadmap, do they generate genuine conviction—or does it feel vague, recycled, or disconnected from business priorities?
When the direction is set and prioritized by leadership, functional managers and business heads understand where they are encouraged to innovate, what problems are worth solving, and how data and its solutions are expected to contribute. That clarity reinforces critical thinking and purposeful experimentation—teams know which questions matter and can pursue them with confidence.
There is a test for whether the direction ever landed, and you can run it from any seat in the building—call it the debate-altitude test. A credible strategy, both for business and data, reduces debate rather than creating more of it. When direction is clear, teams argue about designs and implementations, not foundations or goals. They debate which features to prioritize, which design patterns to use, not which platform architecture to adopt. They refine execution, not revisit ownership. In a firm with clear direction, a team might debate whether to prioritize churn prediction or lifetime value modeling first. But if they’re still arguing about whether to store data in a relational database or a data lake, or whether analytics should live in the product team or a central data group, the foundational strategy never landed.
Crucially, data innovation should never exist in isolation. A credible data strategy makes explicit how data and analytics advance concrete business objectives: whether on the top line through growth, new products, or market expansion, or on the bottom line through efficiency, risk reduction, and operational leverage. Technology upgrades that cannot be traced to a business advantage, even indirectly, erode conviction over time. When leadership consistently frames data initiatives in business terms, innovation becomes purposeful rather than performative.
A sound data strategy also reflects a deliberate balance between near-term impact and long-term optionality. Some initiatives are expected to deliver measurable results quickly; others are intentionally exploratory. Consider how financial institutions in the early 2010s invested in machine learning infrastructure—not because the ROI was clear, but because they understood that algorithmic trading, credit modeling, and fraud detection were evolving rapidly. Firms that built those capabilities early gained years of advantage over competitors who waited for certainty. Similarly, pharmaceutical companies investing in genomic data platforms today may not see immediate returns, but they’re positioning themselves for a future where personalized medicine becomes standard of care. What matters is not certainty of outcome, but clarity of intent: leadership understands which bets are foundational, which are experimental, and why both exist, which in turn helps with the right prioritization, budgeting and sequencing decisions.
I faced this balancing act myself, often. At the end of 2022—when AI brought excitement and anxiety in equal measure, small players declaring disruption inevitable while established executives saw doomsday—we chose to make an early move: a centralized chat interface exposing frontier models to the whole firm. The near-term impact was real. Adoption spread, institutional learning, momentum built, and we had a front seat in the race. But the long-term option it bought mattered more: the log traces revealed the most common use cases, the knowledge bases people actually needed, and who the early adopters were: all the raw material needed for a more intentional agentic ecosystem for discretionary investors, built on those lessons, guardrails, and proven flows.
3. Do data initiatives persist across leadership cycles?
What to look for: When leadership changes—a new CEO, CTO, or CDO—do data initiatives build on prior progress, or does each transition bring a reset with new platforms, vendors, and rebranded strategies?
Continuity doesn’t mean stagnation—it means new leadership extends what’s working rather than starting from scratch. At the highest-performing organizations, data initiatives compound over time. New executives inherit platforms, processes, and momentum, then build on them and refine them. When that doesn’t happen, there’s a write-off—not just financial, but in lost time, eroded trust, and organizational momentum. Those costs should be taken seriously at the highest levels before allowing strategic reversals.
One of the clearest tells of instability is a strategy that keeps changing without clear reasoning. One year, the priority is centralized data ownership—a single platform team controlling standards, quality, and access to ensure consistency across the business. The next year, leadership pivots to a federated model where each business unit owns and manages its own data, citing agility and accountability. Both approaches have merit, but when the shift happens without explaining what failed in the first model or what specific problems the new one solves, teams are left guessing. Engineers who built the central platform wonder if their work mattered. Business units hesitate to invest, assuming the next reorganization will undo it. When priorities reverse without clear closure or institutional learning, teams stop internalizing the strategy.
The biggest risk often comes at the hiring stage. When bringing in new senior leadership, especially into organizations where a data strategy is already established, alignment on direction matters as much as capability. Consider an established investment firm that commits to cloud modernization—migrates critical data footprint, builds team expertise, and begins scaling adoption. A new senior hire joins with strong credentials but without deep assessment of whether the person believes in the existing strategy. Within months, the new leader begins pitching a return to on-premise infrastructure, arguing the firm needs more control and shouldn’t depend on external vendors. Management, without fully interrogating the trade-offs or value, approves the shift. The result: millions spent rebuilding infrastructure that had already been modernized. The new on-prem systems are never properly adopted, the cloud ecosystem becomes orphaned, and the people who built it begin to leave. The misalignment proves costly.
Now run the signals on your own firm. How does leadership frame data when no one from technology is in the room? Does the roadmap end arguments or start them? Did the last transition extend the platform, or reset it? Read honestly, the three answers are a position—where conviction lives, whether it lands, whether it survives—taken before the results arrive, while it can still be changed. That is the point of reading intent early: unlike results, it is still yours to change.