JPMorgan has raised its global AI capital expenditure forecast through 2030 to $5.5 trillion.
IBM research shows 79% of executives expect AI to meaningfully boost revenue only by 2030.
McKinsey projects global data center construction will reach $7 trillion between 2025 and 2030.
Chief executives are no longer asking whether to invest in technology. The real question is one of sequence, scale, and timing. Board discussions now center on AI infrastructure, workforce automation, and cybersecurity resilience. These are not routine budget lines anymore.
Companies that delay these decisions face a shrinking window. Analysts covering enterprise technology see 2030 less as a distant deadline and more as a checkpoint. Early movers are already pulling ahead. The choices CEOs approve over the next few years will shape competitive standing for a full decade.
Enterprise technology budgets have shifted firmly toward AI infrastructure and automation. JPMorgan's midyear research raised its global AI capital expenditure estimate through 2030 to $5.5 trillion.
The bank points to expanded data center capacity and growing reliance on debt financing. Goldman Sachs offers a higher figure. It projects that cumulative AI infrastructure spending between 2026 and 2031 could reach $7.6 trillion.
McKinsey expects data center construction worldwide to total roughly $7 trillion between 2025 and 2030. Gartner puts global IT spending above $6.31 trillion for 2026 alone. These figures cover infrastructure only. They exclude software licensing, talent acquisition, and integration costs that CEOs must also plan for.
| Investment Area | Reported or Projected Spend | Strategic Focus | Source |
|---|---|---|---|
| Global AI capital expenditure | $5.5 trillion by 2030 | Data center capacity and compute scaling | JPMorgan |
| Cumulative AI infrastructure build-out | $7.6 trillion, 2026 to 2031 | Chips, power delivery, and cooling systems | Goldman Sachs |
| Global data center construction | $7 trillion, 2025 to 2030 | Compute capacity for enterprise AI | McKinsey |
| Global IT spending | Over $6.31 trillion in 2026 | Infrastructure, software, and AI devices | Gartner |
| Hyperscaler capital expenditure | $660 to $785 billion in 2026 | Cloud and AI infrastructure expansion | Futurum |
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The table keeps infrastructure spending separate from software and workforce costs. Infrastructure numbers describe the physical base of enterprise AI. Software and talent budgets show how well companies use that base.
IBM's Institute for Business Value surveyed 2,000 C-suite executives for its Enterprise in 2030 report. The findings show a clear gap between expectation and confidence. Nearly 79% of executives expect AI to significantly boost revenue by 2030. Only 40% say it delivers that impact today.
Productivity gains draw stronger belief from leadership teams. IBM's research shows executives expect AI to lift productivity by 42% within four years. Two-thirds expect most of those gains to arrive by 2030. Leadership roles will shift as well. 74% of executives expect AI to redefine how enterprises structure management.
CEOs are directing capital toward these areas ahead of 2030:
Cloud modernization to support AI workloads at scale
Cybersecurity systems built for autonomous threats
Workforce automation paired with reskilling programs
Data infrastructure built for real-time decision-making
Energy and power partnerships tied to data center growth
Confidence gaps between CEOs and technical leadership complicate these choices further. Research from Protiviti found that 61% of CIOs feel confident that AI drives revenue growth. Fewer than one-third of CEOs and board members share that confidence. Alignment across the C-suite matters more than technology selection alone.
What This Means for Enterprise StrategyA widening gap between CEO confidence and CIO confidence shows that technology alone will not deliver returns. Leadership alignment is becoming the real deciding factor for AI success through 2030.
Enterprises with mature data systems already show stronger outcomes. More than four in five organizations with well-organized data report positive ROI on AI investments. Fewer than half of companies with fragmented systems can say the same. Data readiness separates enterprises earning returns from those still experimenting.
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The path to 2030 will not reward companies for spending the most. It will reward companies that sequence their investments with discipline. Infrastructure, talent, and leadership need to move together toward one shared goal. Capital alone has never guaranteed transformation, and the enterprises pulling ahead already understand this.
CEOs who treat technology investment as a coordinated, multi-year commitment will enter the next decade with a real advantage. Waiting for certainty before committing carries its own risk. The window for meaningful positioning may already be narrowing. The numbers point to one conclusion: 2030 will belong to leaders who invested early, aligned their organizations, and stayed the course.
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Why should CEOs invest heavily in AI infrastructure before 2030
Global AI capital expenditure could reach $5.5 trillion by 2030, according to JPMorgan. Companies that build infrastructure early secure compute capacity and specialized talent before demand outpaces available supply across the industry.
What is the biggest barrier to AI investment returns?
Data immaturity remains the leading barrier for most enterprises today. Companies with organized, well-structured data systems report significantly higher ROI on AI investments than firms still managing fragmented, inconsistent systems.
Do CEOs and CIOs agree on AI's value?
Not entirely, based on recent research. Protiviti found 61% of CIOs feel confident AI drives revenue growth, compared with fewer than one-third of CEOs and board members surveyed.
What technologies matter most for enterprises heading into 2030
Cloud modernization, cybersecurity built for autonomous threats, workforce automation, and real-time data infrastructure rank among top priorities. Enterprise leaders are prioritizing these areas as AI adoption accelerates across nearly every major industry sector.