1. IT budgets are increasing, but spending remains difficult to control
Tech budgets are growing—great. But here’s the problem: a bigger budget doesn’t mean better spending. Many companies don’t actually know where their IT dollars are going. The reality? Over half of IT decision-makers admit they lack the data needed to track and optimize spending.
The issue is a mix of unpredictable cloud bills, outdated software licenses still draining money, and departments buying their own tech outside IT’s oversight. The result? Financial blind spots. Companies are pouring money into innovation while struggling to maintain control over what they already have. If they don’t fix this, that innovation won’t be sustainable.
The future belongs to businesses that treat IT spending as an investment, not a cost. That means real-time tracking, data-driven decisions, and a mindset shift—money should go where it has the highest impact, not wherever the invoices pile up.
2. AI adoption is driving up IT costs and consuming much of the increased budget
AI is everywhere now—baked into nearly every software tool. John-David Lovelock at Gartner put it well: “GenAI is like salt on the dinner table—you can sprinkle it on everything.” And businesses are. But here’s the catch: sprinkling AI on everything is expensive.
The promise of AI is that it drives efficiency, automates work, and cuts costs. But that’s not happening yet. Instead, companies are seeing rising cloud costs, higher computing demands, and more complex software licensing. AI isn’t free, and instead needs massive computing power, constant updates, and specialized talent to make it work.
Gartner estimates global tech spending will jump 10% in 2025 to $5.6 trillion, and AI is a big part of that. Companies racing to integrate AI need to be just as focused on managing costs as they are on deploying the technology. Without a clear financial strategy, AI will be a runaway expense.
3. Cloud cost unpredictability and shadow IT create budget inefficiencies
Cloud pricing is a black box. One month, your bill makes sense. The next? It’s doubled. Why? Because cloud services operate on pay-as-you-go pricing. If usage spikes, costs skyrocket. If teams overprovision, you’re paying for unused capacity. Add shadow IT (where departments buy their own software outside official budgets) and things get even messier.
Apptio found that these hidden costs are killing IT financial oversight. Finance teams can’t manage what they can’t see. When non-IT departments buy software directly, they often duplicate existing tools, fail to optimize usage, or sign up for overpriced contracts. It’s like running a company where every department orders their own office supplies without telling procurement—chaos.
“Smart companies are treating cloud costs and shadow IT like supply chain problems—tracking every dollar, eliminating waste, and consolidating tools. The best tech investment is one you actually control.”
4. Companies are reallocating funds to support AI initiatives, despite limited dedicated budgets
AI is expensive, and most companies don’t have a dedicated budget for it. That’s a problem. Half of IT leaders say they’re pulling money from other departments to fund AI projects. Another 40% are hoping that AI-driven efficiency will eventually pay for itself. That’s risky thinking.
AI doesn’t deliver instant savings. It takes time—months, even years—to see ROI. Relying on cost reductions that haven’t materialized yet is like spending your future paycheck before it arrives. And shifting funds from other areas? That’s often a short-term fix that creates long-term problems, like underfunding security, infrastructure, or core business functions.
The companies that win with AI will be the ones treating it as a strategic investment, not an experimental side project. That means creating dedicated budgets, setting clear financial goals, and tracking real results.
5. AI cost reductions are emerging, but budget pressures persist
AI costs are coming down, but they’re not cheap yet. Open-source models like DeepSeek-R1 are making AI more accessible, and Accenture’s Michael Abbott sees prices dropping. That’s good news. But enterprise AI spending is still massive. Even with cheaper models, businesses still need cloud computing, data storage, and specialized teams to make AI work.
It’s also about the infrastructure behind these initiatives. Training AI models requires expensive GPUs, high-power cloud computing, and continuous monitoring to keep models relevant. Even open-source AI isn’t free when you factor in operational costs.
Companies need to separate AI hype from financial reality. Cost savings will come, but until then, businesses need a clear strategy: optimize cloud spending, reduce unnecessary AI experiments, and invest only in AI that delivers measurable business value.
6. AI cost management will become a focus of FinOps strategies in 2025
AI costs are about to get real scrutiny. Until now, most companies have treated AI spending like an experimental budget. That’s changing. FinOps (a practice focused on tracking and optimizing IT costs) is expanding to include AI. And that’s a smart move.
Cloud costs and AI costs are merging. AI needs massive cloud resources, so it makes sense to manage them under the same financial strategy. Companies that succeed with AI won’t be the ones spending the most; they’ll be the ones spending the smartest.
Jay Litkey, SVP of cloud and FinOps at Flexera, put it simply: AI financial governance is now as important as managing cloud and data center costs. The future of AI will be about making AI a sustainable part of the business. And that means treating every AI dollar like it matters, because it does.
Key takeaways for leaders
- AI is driving unpredictable IT costs: As AI solutions proliferate, they are rapidly escalating IT expenses, especially with increased cloud consumption and infrastructure demands. Leaders should implement robust cost tracking systems to ensure that AI adoption aligns with budget expectations.
- Shadow IT and cloud spend are major budget drains: Unapproved software purchases and unpredictable cloud billing are creating budget inefficiencies. Executives must centralize IT procurement and enhance visibility into cloud usage to reduce unnecessary expenditures.
- AI budgets are largely ad-hoc: Most companies lack dedicated AI budgets, forcing them to reallocate funds from other areas. Decision-makers should prioritize establishing clear AI financial frameworks to ensure sustainable investment and avoid resource strain in other critical areas.
- FinOps will become essential for managing AI costs: As AI expenses grow, integrating AI cost tracking into existing FinOps practices will be key. Companies should build AI cost management into their broader IT financial strategies to maximize ROI and prevent overspending.
