In a market often defined by disruption and compressed decision-making cycles, business leaders are under pressure to make faster, smarter technology decisions: How much should the IT budget increase? How quickly should they scale artificial intelligence? How foundational is data transformation in driving performance?

The real challenge is not simply deciding whether to invest in data, AI, or larger IT budgets, but recognizing that these choices do not create value in isolation. Organizations operate as interconnected systems, where decisions about data maturity, AI scaling, capability building, and technical debt compound over time, shaping earnings and market performance in ways that are easy to miss when viewed linearly.

To make those dynamics visible, Deloitte’s Center for Integrated Research applied a system dynamics modeling approach (see methodology) to capture organizations as complex, feedback-driven systems in which investments, behaviors, and outcomes evolve together over time. In a typical S&P 500 company, the technology decisions leaders make about data, AI, IT budgets, and workforce capacity can materially reshape earnings per share over just a few years. The model comprises 63 variables that include the tech estate, digital capabilities, and organizational readiness, as well as 20 core variables that tend to most strongly influence outcomes, such as earnings per share (EPS).

This article focuses on the digital investment decisions that can impact EPS growth and the underlying capabilities that enable it. Throughout, outcomes are expressed in EPS terms, giving chief information officers, chief financial officers, and boards a common language for making technology trade-offs.

We conducted a peer-to-peer analysis of two comparable S&P 500 companies to examine the potential EPS impact of different strategic choices.

The first reflects a typical S&P 500 organization operating at an average competitive level. It has a median IT spend, moderate digital maturity, a fairly strong (but not exceptional) ability to manage change, and limited AI activity beyond pilots. Its strategy is to keep technology, data, and AI capabilities broadly in line with market averages. Based on historical S&P 500 patterns, this company starts at an EPS of US$2 in Year 1 and grows steadily to US$5.17 by Year 5, reflecting typical market performance.

The second company is similar in size and starting EPS, but is pursuing a targeted strategic intervention. Rather than maintaining average positioning, it is intentionally testing actions to strengthen competitive advantage—such as scaling AI, advancing data strategy, adjusting tech investment, optimizing tech headcount, and strengthening change execution—to assess how those choices may translate into different EPS outcomes.

Our analysis reveals that three key levers—data maturity, AI maturity, and tech budget adjustments—have the potential to generate significant EPS gains. Deloitte’s model quantifies the impact of specific decisions made in each scenario on EPS growth, identifying which strategic investment choices—or “interventions”—accelerate value creation and which undermine it. These financial estimates can help provide leaders with a clearer picture of how specific decisions related to bold technology strategies may impact EPS in the next two to five years.¹