December 2025. You’re sitting in a conference room, facing a spreadsheet with the proposed IT budget for the coming year. The CFO just asked why technology spending should increase by another 12% when last year’s AI investments “haven’t paid off yet.” The CEO adds that the competition just announced a breakthrough product built on generative AI. The board expects answers: where exactly will this money go and what results will it deliver?
This scenario will play out in thousands of companies worldwide in the coming weeks. According to Gartner’s latest forecasts, global IT spending will exceed $6 trillion in 2026—growth driven primarily by GenAI features embedded in existing software. Meanwhile, an Info-Tech Research Group report reveals a troubling statistic: 78% of organizations have implemented generative AI tools, but only 22% report lasting business impact.
This article is a navigation map for CIOs facing the toughest budget decisions of the past decade. We’ll analyze five priorities that, according to research and ARDURA Consulting’s experience, separate organizations building competitive advantage from those merely reacting to market pressure.
Why does IT budgeting in 2026 require a fundamentally new approach?
The traditional IT budget planning model was based on simple logic: estimate the costs of maintaining current infrastructure, add a margin for new projects, and negotiate with finance. This model has stopped working for three reasons.
First, software costs are rising regardless of purchasing decisions. Gartner forecasts that GenAI features are now “ubiquitous in software already owned and used by enterprises, and these features cost more.” Microsoft, Salesforce, SAP—virtually every major vendor is introducing AI add-ons that automatically raise subscription prices. A CIO who doesn’t account for this in the budget will end the year with unplanned overspending.
Second, pressure to demonstrate ROI from technology has never been stronger. West Monroe research shows that over 4 in 5 companies increased IT spending in the last 12 months, and 85% expect further increases. At the same time, boards increasingly demand concrete return metrics—the era of “strategic investments” without measurable results is ending.
Third, geopolitical uncertainty complicates long-term planning. The CIO Dive report indicates that “economic, technological, and geopolitical uncertainty has muted hiring activity” and forces IT leaders to take a more cautious approach to budgeting. Decisions about infrastructure location, cloud vendor selection, or team work models must account for scenarios that seemed abstract just two years ago.
At ARDURA Consulting, we observe this shift in conversations with clients. Companies that were still planning budgets in “business as usual” mode in 2024 now ask for help building flexible IT financing models. The key is transitioning from annual budgeting to quarterly priority reviews with rapid resource reallocation mechanisms.
What does the global IT spending landscape look like according to the latest forecasts?
Before we move to specific priorities, it’s worth understanding the broader context. Gartner data from late 2025 paints a picture of a market in transformation.
Global IT spending will reach over $6 trillion in 2026. That’s approximately 9% year-over-year growth—significantly higher than the historical average. The main driver of this growth is the software segment, where vendors are massively introducing AI functionality and adjusting price lists accordingly.
The AI-as-a-Service (AIaaS) infrastructure category is growing particularly dynamically. Gartner predicts enterprises will spend over $37 billion on it in 2026. This reflects the shift from AI experiments in development environments to production deployments requiring dedicated computing infrastructure.
At the same time, the Flexera 2026 IT Priorities report reveals that cost and risk remain the main challenges. Uncontrolled growth of applications purchased outside IT’s knowledge—a phenomenon known as “SaaS sprawl”—worsens year after year. The average enterprise organization spends approximately $49 million annually on SaaS subscriptions, often without full visibility into how these tools are used.
At the European level, there’s an additional factor: regulations related to digital sovereignty. CIOs in EU-operating companies must account for requirements regarding data localization, AI algorithm auditability, and AI Act compliance. This means additional budget items that didn’t exist just two years ago.
Does AI really need to be priority number one in the 2026 budget?
Short answer: yes, but not in the way press headlines suggest. The longer answer requires distinguishing between three categories of AI spending.
The first category is “imposed AI”—generative AI features embedded by vendors in existing software. Microsoft 365 Copilot, Salesforce Einstein, SAP Joule—these add-ons appear in enterprise packages regardless of whether the company planned to use them. According to Gartner, this category accounts for the largest portion of software cost increases in 2026. The CIO has no choice here: they must budget for higher licensing fees or negotiate opting out of AI features (which is increasingly impossible).
The second category is “strategic AI”—deliberate investments in artificial intelligence solutions supporting key business processes. Here the situation is more complicated. The Info-Tech Research Group report indicates that 78% of organizations have deployed GenAI tools enterprise-wide, but only 22% report lasting business impact. The gap between deployment and value most often stems from data quality issues, lack of integration with existing processes, and insufficient user training.
The third category is “infrastructural AI”—investments in computing power, MLOps platforms, and AI system security. This category is often overlooked in initial budgeting, then forces costly mid-year corrections. Organizations that launched AI pilots on shared cloud infrastructure in 2025 are now discovering that scaling to production requires dedicated GPU resources and specialized model management tools.
In practice, ARDURA recommends clients allocate 15-20% of the IT budget to AI-related initiatives, with about half serving as a reserve for unplanned licensing cost increases. It’s also crucial to link every AI investment to a specific business metric—not “we’ll implement a chatbot,” but “we’ll reduce customer query handling time by 40%.”
Why does data quality determine the success of all other priorities?
The CIO Priorities 2026 report from Info-Tech Research Group places data management in second position among priorities, with 65% of leaders citing poor data quality as a barrier to AI ROI. This is no coincidence—data is the fuel without which even the best algorithms generate worthless results.
The data quality problem in the AI context has several dimensions. First is completeness: machine learning models require historical data that many organizations simply weren’t collecting in the appropriate form. Second is consistency: data scattered in departmental silos often uses different definitions of the same concepts (customer, transaction, product). Third is timeliness: AI models trained on pre-pandemic data may generate incorrect predictions in a changed market reality.
Budgeting data quality initiatives requires a long-term approach. According to ARDURA’s experience, a typical data unification project in a mid-sized organization takes 12-18 months and requires involvement not only from IT but also from business owners of individual data domains. Costs include not just tools (MDM platforms, data profiling tools) but primarily people’s time needed to define business rules and validate results.
Practical recommendation: before approving any AI investment, conduct a data readiness audit. Check whether the data needed to train and operate the model is available, complete, and reliable. If not—start with a data remediation project and postpone AI until the foundation is solid.
How is cybersecurity evolving from a cost center to a competitive advantage factor?
IT security has occupied top spots on CIO priority lists for years, but the nature of this priority is fundamentally changing. According to the Forrester Budget Planning Guide 2026, 43% of technology decision-makers plan to increase IT security spending above inflation levels. That’s more than any other category.
The qualitative change involves transitioning from reactive protection to proactive risk management. Three factors drive this transformation:
First, AI-related threats. Generative artificial intelligence lowers the barrier to entry for attackers—LLM-generated phishing is harder to detect, and deepfakes complicate identity verification. At the same time, organizations deploying their own AI systems create new attack vectors: prompt injection, training data poisoning, leaks through model hallucinations.
Second, quantum threats are no longer science fiction. The Unisys report indicates that 71% of leaders believe their current security won’t withstand attacks using quantum cryptography. While practical quantum computers capable of breaking modern ciphers are still probably several years away, preparation for “Q-day” requires action now—inventorying systems using vulnerable algorithms and planning migration to post-quantum cryptography.
Third, regulations multiply compliance requirements. NIS2 in Europe, expanding SEC requirements in the US regarding incident reporting, sector-specific regulations for finance and healthcare—security teams spend increasingly more time on documentation and audits rather than actual protection. The budget must account for not just tools but also personnel to handle compliance processes.
At ARDURA, we observe that organizations managing security most effectively treat it as a competitive advantage element, not a cost. Certifications, transparency in security communication, incident response speed—these are factors influencing B2B customer purchasing decisions. Investment in security is an investment in trust, and trust translates to revenue.
Can infrastructure modernization wait another year?
Info-Tech Research Group data indicates that 45% of CIOs plan infrastructure modernization to support AI workloads. This is no coincidence—machine learning models have radically different requirements than traditional business applications.
Infrastructure technical debt is accumulating faster than ever. According to McKinsey research, 60% of organizations report that technical debt has significantly increased in the last three years. The causes are complex: rapid deployments during the pandemic without time for architectural considerations, accumulation of “temporary” integrations that became permanent, postponing modernization to the next budget year.
Financial consequences are measurable. Enterprises lose an average of $370 million annually through legacy systems and technical debt-related burdens. This includes maintenance costs, failed modernization attempts, and operational friction resulting from legacy constraints.
Data infrastructure is particularly critical. Traditional data warehouses designed for batch reporting cannot handle real-time analytics and AI requirements. Migration to modern platforms (lakehouse, streaming) is a multi-month project requiring budget not just for tools but also for ETL process re-architecture and team reskilling.
Practical recommendation: instead of the “big bang modernization” approach, which often ends in budget and timeline overruns, consider an incremental strategy. Identify systems with the highest maintenance costs or greatest impact on strategic initiatives and modernize them first. At ARDURA, we use the “strangler fig pattern” methodology—gradually replacing legacy components with new solutions without the risk of a major migration.
How does cloud cost optimization differ from simple spending cuts?
FinOps—the discipline of cloud financial management—has matured from a niche practice to mainstream. According to research, organizations applying advanced FinOps practices achieve 20-30% cloud cost reduction without negative impact on performance or availability.
The challenge is that traditional IT cost optimization approaches don’t work in the cloud model. In the on-premise world, savings required contract renegotiations or personnel reductions—lengthy and painful actions. In cloud, costs are variable and granular: every VM instance, every GB of transfer, every API call generates a charge. This means optimization must be a continuous process, not a one-time project.
The most common sources of waste in cloud environments include: overprovisioned resources (instances larger than needed), zombie resources (started and forgotten), inefficient architectures (data transfer between regions), lack of discount utilization (Reserved Instances, Savings Plans). Each of these categories requires different tools and processes for identification and remediation.
AI paradoxically both increases cloud costs and helps optimize them. On one hand, ML/AI workloads require expensive GPU resources and generate significant data transfer. On the other, AIOps tools can predict usage patterns and automatically adjust resources, achieving optimization levels impossible with manual management.
In the 2026 budget, we recommend allocating a dedicated line item for FinOps tools and personnel. The typical ratio is 2-3% of cloud spending allocated to optimization—an investment that pays for itself multiple times in avoided costs.
Why does the IT skills gap require budget prioritization?
According to ManpowerGroup’s Talent Shortage Survey 2024, 74% of employers globally report difficulty finding talent—the highest level in 17 years. In IT, the situation is even more difficult, particularly in AI/ML, cybersecurity, and modern cloud architecture areas.
The paradox is that global developer supply is growing, but demand for specialized skills grows faster. McKinsey forecasts that by 2026, organizations will need one million more developers proficient in AI-driven tools. Meanwhile, the share of AI/ML job postings increased from 10% to 50% of all tech postings between 2023 and 2025.
Budget implications are threefold. First, recruitment costs are rising—the average time to fill a technical position is 52-88 days, and every day of project delay generates costs. Second, wage pressure forces compensation grid revisions, especially for AI-related roles. Third, retention requires investment in development and career paths—the best specialists leave not for money but for more interesting projects.
An alternative to expensive recruitment is staff augmentation—flexibly strengthening teams with external experts. This model allows rapid capability scaling without long-term salary commitments and is particularly effective for time-limited projects or those requiring niche competencies.
At ARDURA, we observe growing demand for a hybrid model: core competencies built internally, supplemented by flexible external teams for peak loads and specialized tasks. The budget should account for both paths, with clear delineation of which roles are strategically critical (internal recruitment) versus operationally necessary but not unique (augmentation).
How do compliance and regulations affect IT budget structure?
2026 brings unprecedented regulatory requirement accumulation for IT. The EU AI Act comes into full force, NIS2 expands cybersecurity obligations, the Digital Operational Resilience Act (DORA) imposes new requirements on the financial sector, and wage transparency and ESG reporting require changes to HR and reporting systems.
Each of these regulations means specific budget items. The AI Act requires documentation of AI systems, risk assessment, human oversight mechanisms, and algorithmic decision auditability. For organizations using AI in decision-making processes (credit scoring, recruitment, pricing), these are multi-month projects requiring IT, legal, and business collaboration.
NIS2 expands the list of sectors covered by cybersecurity requirements and introduces personal liability for management in case of incidents. This means investments not just in tools but also in reporting processes, business continuity plans, and regular resilience testing.
DORA for the financial sector introduces detailed ICT risk management requirements, including mandatory penetration testing, IT vendor management, and incident reporting within 24 hours. Financial institutions must budget not only for their own activities but also for technology vendor audits.
Practical recommendation: treat compliance not as a cost but as a transformation driver. Many regulatory requirements (process documentation, system inventory, identity management) overlap with IT best practices. A compliance project can be an opportunity to address backlogs and build foundations for future initiatives.
Does sustainable IT deserve a separate budget line item?
Sustainable IT has gone from a niche CSR initiative to a strategic priority. According to recent research, 94% of IT leaders report growing importance of sustainability in their organizations, driven by regulatory pressure and customer expectations.
The regulatory dimension includes emissions reporting (scope 1, 2, and 3), where IT is a significant contributor through data center energy consumption and hardware supply chain. The Corporate Sustainability Reporting Directive (CSRD) in the EU requires large companies to provide detailed environmental impact reporting, including IT infrastructure.
The business dimension is the growing importance of sustainability in B2B purchasing decisions. Corporate customers increasingly require environmental reports from suppliers and include carbon footprint in selection criteria. For IT companies, this means both a challenge (optimizing their own emissions) and an opportunity (products and services supporting customer sustainability).
Specific budget items include: tools for monitoring energy consumption and emissions, data center energy efficiency optimization, migration to cloud providers with renewable energy commitments, equipment lifecycle management (extending lifespan, responsible recycling). In the cloud model, choosing regions powered by renewable energy is particularly important—differences in carbon intensity between regions of the same provider can be significant.
How do you build an IT budget resilient to unforeseen changes?
The traditional annual budgeting model with quarterly reviews cannot keep pace with technology change. Organizations best handling uncertainty use a “rolling forecast” approach with rapid resource reallocation mechanisms.
Key elements of a flexible IT budget include:
Division into fixed and variable categories. Fixed costs (enterprise licenses, basic infrastructure, core team salaries) planned annually with quarterly reviews. Variable costs (cloud consumption, development projects, team augmentation) planned quarterly with monthly monitoring.
Innovation reserve. 10-15% of budget without allocation to specific projects, available for rapid launch of initiatives arising from market or technology changes. Better to have unused reserves than to block innovation due to lack of funds.
Budget scenarios. Preparing three budget versions (baseline, optimistic, pessimistic) enables rapid response to changing business conditions without having to build the budget from scratch.
Value metrics, not just costs. Every significant budget item should have an assigned business metric. Not “AI spending: 2M PLN” but “AI in customer service: 2M PLN, goal: 30% reduction in handling time.” This enables evidence-based reallocation decisions, not intuition.
Strategic table: IT Budgeting Maturity Model
| Area | Level 1: Reactive | Level 2: Managed | Level 3: Strategic | Level 4: Optimizing |
|---|---|---|---|---|
| Planning cycle | Annual, rigid | Annual with quarterly reviews | Rolling forecast 4 quarters | Continuous, event-driven |
| AI allocation | None or ad-hoc | Dedicated project budget | Integrated with all initiatives | AI-first with ROI metrics |
| Data management | Reactive fixes | Basic governance | Data quality program | DataOps with automation |
| Cybersecurity | Compliance-driven | Risk-based | Proactive with threat intelligence | Security as competitive advantage |
| Modernization | When system fails | Planned, multi-year | Continuous, incremental | Automated with AI |
| Cloud FinOps | None | Basic reports | Dedicated team | Automatic optimization |
| Talent management | Recruit when needed | Candidate pipeline | Strategic partnerships | Ecosystem thinking |
| Compliance | Reactive | Checklist-based | Integrated with processes | Compliance by design |
| Sustainability | None | Reporting | Reduction targets | Circular IT |
| Flexibility | No reserve | 5% reserve | 10-15% innovation reserve | Dynamic resource allocation |
Interpretation: Most organizations are at level 1-2 in most areas. The goal for 2026 should be achieving level 3 in areas strategically critical to the given organization. Level 4 is an aspiration for digital transformation leaders.
How does ARDURA Consulting support CIOs in achieving budget priorities?
At ARDURA, we have been supporting IT leaders in technology transformation for over a decade. Our experience includes projects for 32+ organizations in Europe, the Middle East, and the USA, from mid-sized companies to global enterprises.
In the context of 2026 budget priorities, we offer support in three areas:
Staff Augmentation — flexibly strengthening teams with specialists whose competencies are hard to find in the market. The Try & Hire model minimizes recruitment risk, and ARDURA’s global expert network ensures access to talent regardless of local labor market constraints.
Software Asset Management — licensing cost optimization and audit preparation. In the face of increasingly aggressive enforcement by vendors (Microsoft, Oracle, SAP), professional license management is not a cost but a saving. Our Flexera One implementations typically identify 20-30% potential savings in license portfolios.
Software Development — executing modernization and AI projects with quality assurance. The Time & Materials model provides flexibility, and the Discovery Workshop methodology enables precise scope and budget estimation before work begins.
Summary: 5 actions to take before approving the budget
IT budget planning for 2026 requires balancing pressure for innovation with financial discipline. Five priorities—AI, data, security, modernization, talent—don’t work in isolation; success in one area depends on progress in the others.
Action 1: Conduct an audit of “hidden” AI costs—check how much you’re actually paying for GenAI features embedded in existing software and budget for expected increases.
Action 2: Define data quality metrics for key AI use cases—without solid data, AI investments won’t deliver ROI.
Action 3: Assess readiness for AI-native and quantum threats—traditional safeguards may be insufficient.
Action 4: Identify 3-5 systems with the highest maintenance costs or greatest impact on strategy—these are candidates for priority modernization.
Action 5: Build a hybrid talent acquisition model—determine which roles to build internally and which to flexibly supplement through staff augmentation.
If you’re facing the challenge of building an IT budget for 2026 and looking for a partner to help translate priorities into concrete actions—contact us. Our experts will help identify areas with the highest return potential and plan their implementation within the available budget.