AI is driving significant increases in cloud and IT infrastructure spending
Enterprises are entering a new phase. The kind that demands more than iterative improvements. What we’re seeing is a shift, driven primarily by AI, that’s forcing considerable cloud and infrastructure rethinking. Hyperscalers like AWS, Microsoft, and Google are pumping capital into infrastructure upgrades to catch up with compute demands rising from generative AI. That compute demand isn’t plateauing. It’s accelerating.
This isn’t just about buying more servers. It’s about building the foundation for a future of intelligent tools operating at enterprise scale. Businesses understand this, which is why we’re seeing IT budgets expand even before revenue gains surface. AI capabilities are getting embedded across standard tools, CRM platforms, productivity software, analytics dashboards, making them smarter, faster, and more predictive.
The smart companies aren’t waiting for the payoff to start investing. They see the direction and are allocating accordingly. Whether or not AI delivers immediate ROI is the wrong measure. It’s a question of capability positioning. When the real inflection comes, those already built for scale and speed will win.
According to Gartner, global IT spend is projected to exceed $6 trillion by 2026. That’s not theoretical. It’s grounded in actual purchasing behavior being shaped by AI infrastructure demand and device adoption.
Software itself is becoming more powerful, and more expensive. The reason is generative AI. According to John-David Lovelock, Research Vice President at Gartner, “The cost of software is going up, and both the cost of features and functionality is going up as well thanks to GenAI.” That’s a direct consequence of moving from traditional code to systems that can understand, learn, and automate at a high level.
So here’s the simple takeaway: AI is no longer a research lab toy. It’s changing the economics of enterprise tech, starting with how we spend and what we build to support it. C-suite leaders who understand this aren’t obsessing over short-term margins. They’re building systems capable of long-term dominance.
AI-related returns on investment are currently limited but expected to mature by 2030
We’re still early. If you’re looking for quick profits from AI, you’re going to be frustrated. The tools are impressive. The applications are growing. But the big revenue impact? Not quite here, yet.
Most executives get this. According to a study from the IBM Institute for Business Value, nearly 80% of leaders don’t expect AI to significantly drive enterprise revenue until 2030. That level of long-term thinking is rare, but necessary. What it shows is that companies aren’t confusing hype with results. They’re betting on the trajectory, where the technology is going, not just what it does today.
This isn’t a tech problem. It’s an integration timeline. Enterprise systems are complex. Replacing workflows, retraining staff, overhauling data pipelines, it all takes time. You don’t just flip a switch and expect artificial intelligence to produce top-line growth in six months. What matters is that every investment made now is compounding capability. That’s how momentum builds.
Executives who understand that patience and scale matter will lead here. They’re not slowing down deployment plans simply because returns take longer. They’re watching adoption spread across teams and infrastructure. That type of rollout may be slower, but it’s durable.
Look at where the momentum is heading. Over the next four years, AI is expected to drive enterprise growth even if the dollars tied directly to AI are still small. In that sense, the ROI is already taking shape: broader automation, smarter tooling, and faster decision-making across operations.
You won’t measure its impact with a single report or forecast. You’ll see it in how companies start outperforming competitors that stayed on the sidelines. The investment payoff isn’t a matter of if, it’s a question of alignment and time.
Daily enterprise AI usage is growing, reinforcing companies’ commitment to further AI spending
AI adoption doesn’t live in pilot programs anymore. It’s part of the daily workflow now. Executives aren’t just approving experiments, they’re using AI tools across their operations, consistently and at scale.
More than two-thirds of executives report using AI tools every day, according to a report from Accenture. That’s not theoretical usage. It reflects real behavioral change across organizations. Decision-makers, analysts, support teams, they’re leaning on AI to parse data, generate content, optimize logistics, and surface opportunities faster than before.
This level of usage changes the conversation around ROI. Daily tool integration becomes an indicator of foundational utility. Executives are not only spending on AI, they’re relying on it. They’re building processes around it. That’s why AI investment continues to rise, even when direct revenue isn’t immediately measurable.
What’s happening now is strategic entrenchment. Once AI tools are part of how teams work, it justifies further investment, better models, stronger infrastructure, deeper integration with enterprise systems. This feedback loop boosts operational efficiency and encourages more expansive AI rollouts.
For business leaders, this matters. It’s not about waiting until the technology is “perfect.” It’s about recognizing that widescale usage signals organizational readiness. These companies aren’t experimenting anymore, they’re scaling. And those that haven’t crossed that threshold are already a step behind.
Software pricing is rising due to the integration of advanced AI features and functionalities
The software market is evolving, fast. Prices are going up. Not due to inflation or licensing games, but because the software itself is becoming significantly more capable. Generative AI is reshaping the product landscape, and that transformation has real cost implications.
As vendors integrate GenAI capabilities into their platforms, you’re not just paying for access, you’re paying for horsepower. These aren’t simple upgrades. They’re embedded AI features that learn, automate, and execute complex tasks that used to require high-level human input. That additional functionality adds measurable value, but it also pushes up development and operational costs, which flow into pricing.
John-David Lovelock, Research Vice President and Distinguished VP Analyst at Gartner, explained it clearly: “The cost of software is going up, and both the cost of features and functionality is going up as well thanks to GenAI.” That reality is already visible across enterprise tools, everything from CRM systems to internal productivity suites.
For C-suite executives, the message is this: cost is rising because expectations and capability are rising. AI-enabled software is evolving into a core part of the operating system for modern business. It runs processes. It reduces manual input. It delivers output that used to take hours in seconds.
Budgeting for software now requires understanding feature scope and long-term strategic fit, not just short-term pricing comparisons. The tools that demand more investment today are positioning companies for a leaner, smarter operating model tomorrow. If you’re evaluating cost without factoring in AI integration, you’re missing the value proposition entirely.
Key takeaways for leaders
- AI infrastructure is scaling cloud investment: Enterprises and hyperscalers are expanding cloud infrastructure to support GenAI demands, pushing global IT spending toward $6 trillion by 2026. Leaders should align budgets with future AI scalability rather than short-term returns.
- Returns are slow, but enterprise AI growth is steady: Most executives don’t expect significant AI-driven revenue until 2030, yet the technology is widely seen as critical to long-term growth. Decision-makers should stay focused on integration and capability-building over immediate gains.
- Daily AI usage is fueling sustained investment: Over two-thirds of executives are now using AI tools daily, signaling embedded reliance across business operations. Leaders should deepen AI deployments to build operational agility and long-term competitive advantage.
- AI-enabled software is driving up enterprise costs: GenAI is increasing the cost of software and functionality, reflecting elevated performance and integration complexity. Budget owners should plan for rising licensing costs and prioritize high-impact AI features that drive efficiency.
