Gartner puts global IT spending at $5.43 trillion in 2025, growing to $6.31 trillion in 2026. IS researchers study the most consequential investment category in modern organizations.
I saw the Gartner number a few weeks ago and I am still sitting with it. Global IT spending reached $5.43 trillion in 2025 and is forecast to grow 13.5% to $6.31 trillion in 2026 (https://www.gartner.com/en/newsroom/press-releases/2026-04-07-gartner-forecasts-worldwide-it-spending-to-grow-9-8-percent-in-2026). For reference, Japan's GDP in 2024 was roughly $4.1 trillion. The world is spending more on information technology in a single year than the third-largest national economy produces in total output. That scale is not background context. It is the subject of Information Systems research, and I think our field sometimes undersells the stakes of what it investigates.
When an IS researcher asks whether a technology improves firm performance, or why adoption rates vary across organizational contexts, or how governance structures shape the conversion of IT investment into value, they are asking questions that apply to the allocation and utilization of trillions of dollars annually. The answers are not academically interesting in a narrow sense. They have direct economic consequence in a way that most academic disciplines cannot claim.
The core question in IT value research is deceptively simple: does IT spending produce commensurate value? Erik Brynjolfsson's 1993 productivity paradox made this question famous by pointing out that decades of IT investment through the 1970s and 1980s had not produced visible productivity gains in macroeconomic data. His four explanations, mismeasurement, time lags, redistribution, and mismanagement, are still the organizing framework for why IT spending does not straightforwardly translate into performance improvement. Here we are in 2026, with annual global IT spending approaching the economic output of the third-largest country on earth, and the core mechanism problem has not been resolved. Technology spending scales. The organizational capability to capture value from that spending does not scale automatically with it.
The mismeasurement explanation feels particularly important right now. National income accounting was not designed to capture software, data, and networked service outputs the way it captures manufactured goods. When a hospital deploys an AI diagnostic tool that catches a condition earlier, where does that value appear in GDP? When a firm's data platform eliminates a category of costly decisions, how does that register in productivity statistics? The answer is: imperfectly, if at all. The official numbers undercount the value that well-deployed IT produces. But they also do not help us see whether the vast majority of the $5.43 trillion is producing value proportionate to its cost, because the measurement framework is not sensitive enough to answer that question at the firm level, let alone the aggregate level.
The redistribution explanation is equally uncomfortable for organizations trying to justify large IT investments. IT can make a firm genuinely more productive, more responsive, and better at serving customers, while competitive markets push all of those gains to customers in the form of lower prices rather than letting the firm capture them as profit. Brynjolfsson and Hitt (1996) documented this dynamic at the firm level. For a CIO trying to justify an AI investment to a board, this creates a real problem. The investment may be necessary to remain competitive. It may even produce real productivity gains. But if every competitor is making the same investment in the same technology at roughly the same time, and if markets are competitive enough, the gains flow outward rather than inward. The investment is still worth making. It is just harder to defend using a standard ROI framework.
Melville, Kraemer, and Gurbaxani (2004) gave IS research the integrative framework that I think best captures why this happens. IT resources alone do not drive organizational performance. IT resources combined with complementary organizational resources improve business processes, and improved business processes drive firm performance. The mechanism runs through organizational capability, not investment level. This is the part of the value chain that the $5.43 trillion either hits or misses. When organizations invest in technology without building the complementary capabilities in governance, process redesign, and human skills, they are paying for the raw material of value without the production process. The Melville et al. model predicts exactly the variance we observe in practice: two organizations making similar IT investments produce very different performance outcomes depending on their complementary organizational resources.
McKinsey's State of AI 2025 report is a good illustration of this mechanism at scale (https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai). It found that 88% of organizations use AI in some form, but only 7% have fully scaled it across their operations. AI investments are growing at more than 35% year over year, according to Gartner. The spending curve is steep. The scaling curve is nearly flat. The gap between those two curves is the complementary capability problem made visible. Organizations are buying the technology. They are not building the organizational infrastructure to extract value from it at scale.
As an IS researcher, what surprises me is how little this lesson seems to transfer across technology waves. The productivity paradox was first articulated in the early 1990s. The ERP implementation failures of the late 1990s and early 2000s were well documented and produced a substantial IS literature on why large IT projects underperform. The cloud adoption literature of the 2010s revisited similar themes. Now we are watching the same dynamic unfold with AI, at a larger scale and at faster deployment speed, and I am not sure the organizational community is reading the earlier research before making the same choices.
The mismanagement explanation is the most actionable of Brynjolfsson's four, and I think it is the one that IS research is best positioned to address. Mismanagement of IT investment is not random. It follows patterns. Organizations that lack absorptive capacity, to use Cohen and Levinthal's (1990) term, struggle to recognize, assimilate, and apply new technical capabilities regardless of how much they spend. Organizations with strong IT governance convert similar investment levels into better performance outcomes. Understanding the governance and capability conditions that predict value capture is work with enormous practical relevance at the scale of $5.43 trillion.
What raises a research question for me is whether IS research is being heard in the rooms where these decisions are being made. The Gartner forecasts move boardrooms. The McKinsey reports move CEOs. The IS literature, with its careful empirical analysis of what actually produces IT value, reaches the journals that practitioners rarely read. There is a translation gap between what IS research knows and what the organizations spending $5.43 trillion per year are doing with their budgets. Closing that gap is, I think, one of the most important unsolved problems in the field. At this scale, even a marginal improvement in how well organizations convert IT investment into value is worth an enormous amount.
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