March 10, 2026

Get IT budget predictions right with help from Total FinOps

Most organizations struggle with IT budget forecasting. They analyze last month's actuals, apply assumptions, add a buffer for uncertainty and submit predictions. Three months later, they're 30% over budget with no clear explanation of how that happened.

This isn't an isolated problem. The uncomfortable truth is that most organizations are weak at forecasting IT spend, and it's costing them — a lot.

When it comes to IT forecasting, the 2025 State of FinOps survey shows that the capabilities businesses actually need to implement, such as automated behavioral modelling or using predictive analytics to improve delays in cost data, remain largely unimplemented.

Why IT forecasting is uniquely challenging

Unlike traditional expenditures, technology spending exhibits characteristics that make forecasting exceptionally difficult:

• Variable consumption patterns. Cloud resources scale dynamically based on demand, for example, a successful product launch can triple compute costs overnight. Traditional linear forecasting models break down when facing this volatility.
• Delayed cost visibility. Cloud providers don't bill in real-time. There is often a lag of days or weeks between resource consumption and cost data appearing in systems. By the time the spike appears, the money has already been spent.
• Behavioral complexity. IT spending reflects hundreds or thousands of engineers, developers, and business users making decisions independently. Without understanding these behavioral patterns, forecasts are just educated guesses.
• Tool fragmentation. When spending data is scattered across AWS dashboards, Azure portals, on-premises infrastructure reports, and SaaS management tools, building an accurate forecast requires manually reconciling disparate sources.

Organizations can't forecast value creation when they're still struggling to forecast basic costs.  

The strategic cost of inaccurate forecasting

Poor forecasting capability doesn't just create budget variance. It creates strategic problems that limit business agility:

• Missed investment opportunities. When organizations can't confidently predict spending, they build in excessive buffers. That conservative approach leaves resources on the table that could have funded innovation, AI initiatives, or competitive responses.
• Reactive decision-making. Without predictive insight, problems are discovered after they've occurred, rather than being proactively addressed.
• Eroded credibility. When IT forecasts consistently miss the mark, finance teams stop trusting technology leaders' projections. This makes future budget requests harder to justify and strategic initiatives harder to fund.
• Competitive disadvantage. While some organizations are reconciling last month's actuals, competitors with mature forecasting capabilities are making faster, more confident investment decisions.

Forecasting is where cost optimization moves from tactical to strategic. It's where the shift happens from managing what happened to shaping what happens next.

What predictive forecasting actually requires

Moving beyond manual adjustments demands several foundational capabilities:

• Unified data visibility consolidates spending across cloud environments, on-premises infrastructure, SaaS applications, and data centers. Only then can patterns that span the full technology estate be seen. 
• Behavioral analytics track deployment patterns, identify seasonal variations, and recognize the signatures of different workload types.
• Real-time adjustments continuously incorporate new data, adjusting projections as actual consumption deviates from expectations.
• KPI integration connects technology spending to business outcomes.

"You can optimize better if you can see all the data in one place," says Glen Ralph, global head, Cloud Advisory, at DXC. Machine learning excels at identifying patterns in vast datasets and generating predictions that improve over time. But the key is pairing algorithmic sophistication with business context that only human expertise provides.

From reactive to predictive: The DXC advantage

DXC's Total FinOps solution addresses the forecasting gap through comprehensive capabilities, supported by IBM Apptio and Flexera to embed a foundational capability and deliver comprehensive capabilities spanning:

• Enterprise-grade visibility consolidates spending data across all environments, eliminating the fragmentation that makes accurate forecasting impossible. When entire technology estates can be seen holistically, patterns emerge that remain hidden in siloed views.
• Advanced analytics leverage AI and machine learning to process vast amounts of data, identifying trends and generating predictions that manual approaches can't match.
• Expert interpretation ensures algorithmic predictions translate into strategic insights. Certified cloud architects and experienced FinOps practitioners evaluate forecasts through the lens of business context, compliance requirements, and strategic priorities.

Because DXC can handle every workload category at scale, this allows you to spend less time accounting for IT costs and more time optimizing them.

That shift, from accounting to optimization, from reconciliation to prediction, represents the forecasting maturity that separates leaders from followers.

Go deeper

• Read more about Total FinOps: Transform your IT estate from cost center to business accelerator 
• Watch the webinar: Total FinOps: A Strategic Growth Engine
• View the infographic