It is Monday, the third week of November. Krzysztof, CIO of a large manufacturing company employing over 2,500 people, sits in his office before three spreadsheets laid out on his desk. Outside the window, the gray autumn landscape of Warsaw, but Krzysztof pays no attention to it. His thoughts revolve around one of the most challenging tasks of his entire professional career. On one spreadsheet lies a list of urgent infrastructure modernization projects - ERP and CRM systems that were created in the previous decade increasingly freeze up, and every integration with new tools requires weeks of team effort. On the second spreadsheet are projects related to artificial intelligence - the board expects the company to “ride the AI wave,” and competitors are already implementing solutions that automate customer service and optimize the supply chain. On the third spreadsheet is a list of security incidents from the last quarter - two serious phishing attacks, a ransomware attempt stopped at the last moment, and an auditor’s report indicating security vulnerabilities.
Krzysztof has an IT budget for 2026 that increased by 8% compared to the previous year. That is the good news. The bad news is that the total cost of all “urgent” and “strategic” projects exceeds this budget threefold. How to decide what is truly a priority? How to explain to the board that it is not possible to simultaneously be an AI innovation leader, have an impregnable cybersecurity fortress, and modernize the entire legacy infrastructure within a single year? And above all - how to make these decisions strategically rather than reactively, so that a year from now it does not turn out that investments were made in the wrong areas?
This situation is not unique. According to the latest Gartner forecasts, global IT spending will reach a record level of $5.75 trillion in 2026, representing a 9.3% year-over-year increase. Enterprise software spending is growing even faster - at 14.2% annually. At the same time, 67% of artificial intelligence budgets are being allocated to embedding AI in key business operations, rather than experimental proof-of-concept projects. This signifies a fundamental shift in thinking about technology - from a cost center to a growth engine. This article is a practical guide for IT leaders facing dilemmas similar to Krzysztof’s. We will show how to build a decision-making framework for IT budget allocation that enables strategic balancing among three key areas: artificial intelligence, cybersecurity, and legacy system modernization.
Why Does IT Budget Planning for 2026 Require a Fundamentally New Approach?
“Global corporate investment in AI reached $189.6 billion in 2023, with generative AI funding alone surging to $25.2 billion.”
— Stanford University HAI, AI Index Report 2024 | Source
The traditional approach to IT budgeting was based on simple logic: take last year’s budget, add inflation, include a few new projects approved by the board, and you are done. This model has stopped working. The pace of technological change, competitive pressure, and growing cybersecurity threats mean that the IT budget must be treated as a strategic tool for business transformation, not as a cost line to be minimized.
The first fundamental factor forcing change is the explosion of possibilities related to artificial intelligence. Just two years ago, generative AI was a technological curiosity. Today, it is a technology that can fundamentally transform almost every business process - from customer service, through data analysis, to automation of manufacturing processes. Companies that do not invest in AI in 2026 risk falling behind competitors who will make these investments. But at the same time, thoughtless, chaotic AI spending without a clear business strategy is a direct path to budget waste.
The second factor is the dramatically changing cybersecurity threat landscape. Ransomware attacks have become more sophisticated and targeted. The average cost of a data security breach for a large company now exceeds $4 million, not counting reputational losses and regulatory fines resulting from GDPR or NIS2. In this context, cybersecurity ceases to be “insurance” - it becomes a fundamental requirement for conducting business operations.
The third factor is the accumulating technical debt in the form of legacy systems. According to Gartner research, the average large organization currently allocates 60-70% of its IT budget to maintaining existing systems, leaving only 30-40% for innovation and development. This is a proportion that must change if the company is to remain competitive. But legacy modernization involves multi-year projects requiring significant investments and carrying substantial operational risk.
These three forces - AI, cybersecurity, and legacy modernization - compete for the same limited budget resources. Moreover, they are closely interconnected. AI cannot be effectively implemented without solid data infrastructure, which is often blocked by legacy systems. Cybersecurity cannot be ignored when implementing AI, which introduces new attack vectors. Legacy systems cannot be modernized without considering security requirements and integration capabilities with modern AI solutions. This is why a holistic framework is needed that allows for a balanced approach to budget allocation.
What Does the Structure of Global IT Spending Look Like and What Does It Mean for Your Organization?
Before we begin planning our own budget, it is worth understanding the broader market context. Global trends in IT spending provide valuable guidance on investment directions and allow for benchmarking of our own plans against market practices.
According to Gartner’s forecasts for 2026, the structure of global IT spending is as follows. Enterprise software spending will reach $1.24 trillion, growing by 14.2% year-over-year - this is the fastest-growing category. IT services spending (consulting, outsourcing, support) will reach $1.58 trillion with 9.1% growth. Hardware spending (end-user devices, data centers) will amount to $1.02 trillion with 6.7% growth. Telecommunications services spending will reach $1.56 trillion with 3.8% growth.
These data show a clear trend: organizations are shifting spending from physical infrastructure toward software and services. This reflects a broader transformation toward cloud-first models, SaaS, and platform as a service. For an IT leader planning a budget, this means the need to reconsider the proportions between capital expenditure (CAPEX) and operational expenditure (OPEX).
Particularly significant are the data regarding artificial intelligence spending. Global AI spending in enterprise environments will exceed $300 billion in 2026. Crucially, 67% of this spending is directed toward embedding AI in key business operations - meaning projects with direct impact on revenue, costs, or customer experience. Only 33% is spending on experimental proof-of-concept projects and building foundations (data infrastructure, competencies). This means the market has moved from an experimentation phase to a phase of scaling business value from AI.
In the cybersecurity area, global spending will exceed $200 billion, growing by approximately 12% year-over-year. The fastest-growing segments are: cloud security (growth over 20%), identity and access management (IAM), and managed security services (MSSP). These trends reflect a shift from the traditional “perimeter protection” model to a Zero Trust model and data-centric security.
How do these global trends translate to planning a specific organization’s budget? The first principle is to avoid simply copying market averages. Budget allocation proportions should reflect industry specifics, organization size, level of technological maturity, and strategic business priorities. A manufacturing company with extensive legacy infrastructure will have different proportions than a technology startup operating natively in the cloud. A bank subject to strict regulations will allocate cybersecurity resources differently than a retail company.
How to Build a Framework for Strategic IT Budget Allocation Between AI, Cybersecurity, and Legacy?
Effective IT budget planning requires a systematic approach that allows for objective evaluation and prioritization of competing investment areas. I propose a framework based on three dimensions: business value, risk, and urgency.
The business value dimension answers the question: what specific, measurable impact on business results will a given investment bring? This is not about general statements like “efficiency improvement,” but about specific metrics: revenue increase by X%, operational cost reduction by Y%, order fulfillment time shortened by Z days. Investments with the highest business value should receive priority in budget allocation, but only if they are feasible at the organization’s current maturity level.
The risk dimension encompasses two aspects. First, the risk of not making the investment - what are the consequences if we do not execute a given project? In the case of cybersecurity, this may be the risk of attack and associated financial and reputational losses. In the case of legacy modernization, this may be the risk of critical system failure or inability to meet regulatory requirements. Second, the risk of executing the investment - how high is the probability of project failure and what are the potential losses in case of failure?
The urgency dimension determines how quickly a given investment must be realized to deliver the intended value or to avoid a specific risk. Some investments have a “window of opportunity” character - delaying them means losing competitive advantage. Others have a “ticking bomb” character - the longer we wait, the greater the risk.
Based on these three dimensions, a prioritization matrix can be created to help with budget allocation. Projects with high business value, high risk of not investing, and high urgency receive the highest priority. Projects with low value across all dimensions should be eliminated or postponed. Projects falling between these extremes require deeper analysis and conscious decisions about trade-offs.
Practical application of this framework requires involvement not only of the IT department, but also representatives from business, finance, and risk management. Only then will the assessment of business value and risk reflect the organization’s actual priorities, not just the technological perspective.
How to Evaluate and Plan Artificial Intelligence Investments in the 2026 Budget?
Artificial intelligence is an area where pressure to invest is currently greatest, but at the same time, the risk of budget waste is highest. According to research, up to 85% of AI projects fail to deliver expected business value. The main causes are: lack of clear business strategy, insufficient data quality, inadequate competencies in the organization, and attempts to solve problems that do not require AI.
When planning AI investments for 2026, one should start with the fundamental question: what specific business problems do we want to solve using AI and why is AI the best tool to solve them? The list of potential AI applications is almost endless, but the budget is limited. It is crucial to identify use cases with the highest potential ROI and lowest implementation risk.
When evaluating AI use cases, the following criteria should be applied. First, data availability and quality - does the organization possess the data necessary to train or power the AI model? Is the data clean, complete, and available in the appropriate format? Many AI projects fail at the data preparation stage, which turns out to be much more labor-intensive and costly than anticipated. Second, clarity of success metrics - can it be unambiguously determined how implementation success will be measured? AI projects without clear KPIs quickly become “perpetual pilots” with no clear endpoint. Third, organizational readiness - are end users ready to work with AI solutions? Are there change management processes that will ensure adoption? The best technological solution is worthless if it is not accepted by the organization.
The structure of AI spending in the 2026 budget should include three categories. The first category is foundations: data infrastructure, ML/AI platforms, team competencies. These investments are necessary regardless of specific use cases and should constitute approximately 25-30% of the AI budget. The second category is scaling projects: production deployments of AI solutions that have passed the pilot phase and proven business value. This category should constitute approximately 50-55% of the AI budget. The third category is exploration: proof-of-concept projects testing new use cases. This category should constitute approximately 15-20% of the AI budget.
The proportion of 67% of budget for embedding AI in key operations (categories one and two) versus 33% for exploration aligns with market trends and reflects the maturing AI market. Organizations that still allocate most of their AI budget to experimental projects should reconsider their strategy - perhaps the time has come to scale business value from AI.
How to Determine the Appropriate Level of Cybersecurity Investment?
Cybersecurity is an area where the consequences of underinvestment can be catastrophic, but at the same time, it is difficult to determine the “right” level of spending. How much is enough? How to justify spending on something that - if functioning properly - is invisible?
The traditional approach to cybersecurity budgeting was based on industry benchmarks - companies allocated a certain percentage of IT budget to security (typically 5-10%) and considered this sufficient. This approach is fundamentally flawed because it does not account for the specific risk profile of a given organization, the value of protected assets, or the current threat landscape.
A modern approach to cybersecurity budgeting should be based on risk analysis. The starting point is identification of the organization’s key digital assets and estimation of potential losses in case of their compromise. Next, the probability of various attack scenarios should be assessed in the context of industry, organization size, and its public profile. Based on this, Expected Loss can be calculated and compared with the cost of countermeasures.
In practice, the cybersecurity budget for 2026 should include the following categories. The first category is baseline protection: next-generation firewalls, intrusion detection and prevention systems, endpoint protection, vulnerability management. These investments are a necessity for every organization and should be treated as the “cost of doing business.” The second category is identity and access management (IAM): multi-factor authentication solutions, privileged access management (PAM), single sign-on (SSO). In the era of hybrid work and a growing number of SaaS applications, identity has become the new security perimeter. The third category is cloud security: CSPM (Cloud Security Posture Management) tools, CASB (Cloud Access Security Broker), cloud workload protection. As migration to the cloud progresses, traditional security tools become insufficient. The fourth category is detection and response: SIEM/SOAR solutions, SOC services (internal or managed), incident response plans. The assumption that all attacks can be prevented is unrealistic - the key is the ability to quickly detect and neutralize an incident. The fifth category is training and awareness: educational programs for employees, phishing simulations, social engineering tests. Humans remain the weakest link in the security chain.
Special attention in the 2026 budget should be given to preparations for NIS2 directive requirements, which came into force in October 2024 and will be fully enforced in 2025-2026. NIS2 significantly expands the scope of entities covered by cybersecurity regulations and introduces strict requirements for risk management, incident reporting, and board responsibility. Organizations that do not account for these requirements in their budgets expose themselves to significant financial penalties.
How to Prioritize Legacy System Modernization Projects?
Legacy systems are one of the most complex areas to manage in the context of IT budget. On one hand, maintaining outdated systems consumes enormous resources - both financial and human. On the other hand, legacy modernization involves multi-year projects that are costly and carry high risk. How to make rational budget decisions in this situation?
The first step is to create an inventory and assessment of all legacy systems in the organization. For each system, the following should be determined: age and technical condition, maintenance costs (licenses, infrastructure, support), level of operational risk (how often failures occur, how difficult they are to resolve), level of security risk, degree of connection to key business processes, and availability of competencies to maintain (are there specialists in the market who know this technology).
Based on this assessment, legacy systems can be divided into four categories. The first category is systems for urgent modernization: critical for business, high risk, high maintenance costs. These systems should be a priority in the 2026 budget. The second category is systems for planned modernization: important for business, but stable and with acceptable maintenance costs. Modernization can be spread over 2-3 years. The third category is systems for decommissioning: low business value, possible replacement with SaaS solutions or consolidation with other systems. Decommissioning these systems will free up budget for other priorities. The fourth category is systems for maintenance: stable, low cost, specific functionality difficult to replace. Modernization is not economically justified.
The choice of modernization strategy for each system should consider the 6R model: Re-host (migration to cloud without changes), Re-platform (migration with minor optimizations), Re-factor (code refactoring), Re-architect (architecture redesign), Re-build (building from scratch), Replace (replacement with ready-made solution). Each of these strategies has a different profile of costs, risks, and implementation time.
The 2026 budget should also include costs of parallel operation of old and new systems during the transition period, data migration costs, integration costs with remaining systems, and user training costs. These “hidden” costs are often underestimated and represent one of the main causes of budget overruns in modernization projects.
How to Balance Maintenance Versus Innovation Spending?
One of the fundamental challenges in IT budget planning is finding the right proportion between spending on maintaining existing systems (run the business) and spending on innovation and development (change the business). As mentioned earlier, in many organizations this proportion is 70:30 or even 80:20 in favor of maintenance, leaving little room for innovation.
The strategic goal should be to reverse this proportion over several years. The benchmark for organizations with high digital maturity is a proportion of 40:60 or even 30:70 in favor of innovation. Achieving this goal, however, requires systematic action, not revolutionary changes overnight.
In the 2026 budget, the following approach is worth adopting. First, identify and eliminate waste in maintenance spending. Typical areas include: unused software licenses (according to research, 25-30% of licenses in organizations are unused), redundant infrastructure, duplicated systems performing similar functions, expensive service contracts that can be renegotiated. A maintenance spending optimization program can free up 10-15% of budget without negative impact on system operation.
Second, strategically migrate systems to the cloud. Migration to a cloud/SaaS model allows for transformation of capital expenditure (CAPEX) into operational expenditure (OPEX) and often for reduction of total cost of ownership (TCO). More importantly, it frees IT team resources from infrastructure maintenance tasks toward higher value-added projects.
Third, implement IT operations automation (AIOps). Tools for automating monitoring, incident management, and ticket handling can significantly reduce maintenance workload and allow resources to be shifted toward innovative projects.
Fourth, consider staff augmentation or managed services models for maintenance tasks. Collaboration with an external partner, such as ARDURA Consulting, allows for flexible scaling of resources for maintenance tasks, while concentrating the internal team on strategic projects.
How to Account for Hidden Costs and Dependencies Between Investment Areas?
One of the most common mistakes in IT budget planning is treating individual investment areas as independent silos. In reality, AI, cybersecurity, and legacy modernization are closely interconnected, and ignoring these dependencies leads to cost underestimation and delays in project execution.
The dependency between AI and data quality and infrastructure is fundamental. Implementing AI solutions requires access to high-quality, integrated data. In many organizations, this data is scattered across legacy systems, in incompatible formats, with inconsistent definitions and numerous errors. Before an organization can effectively leverage AI, it must first carry out data integration and cleansing projects. These projects are often “invisible” in AI budgets, yet their costs are significant.
The 2026 budget should include realistic investments in data preparation for AI projects. A practical rule states that 60-80% of effort in a typical AI project is data work, with only 20-40% being actual modeling and deployment. Organizations that plan budgets only for the “sexy” part of AI (models, algorithms, interfaces) doom their projects to failure.
The dependency between AI and cybersecurity is becoming increasingly important. AI deployment introduces new attack vectors and requires new security competencies. AI can be used both for defense (anomaly detection, incident response automation) and for attack (deepfakes, automation of social engineering attacks). The cybersecurity budget should include both protection of AI systems and use of AI for defensive purposes.
The dependency between legacy modernization and cybersecurity is often underestimated. Old systems are harder to secure - they may not support modern encryption protocols, may not integrate with IAM systems, may not log events in a way that allows their analysis by SIEM. Every modernization project should include security requirements from the very beginning (security by design), which increases its cost but reduces risk.
The dependency between legacy modernization and AI implementation capabilities closes the circle. Legacy systems often constitute a barrier to AI deployment - they do not expose data through APIs, do not scale, do not integrate with modern ML platforms. The modernization plan should account for future requirements of AI projects.
How to Create a Realistic Schedule and Expenditure Distribution Throughout the Year?
IT budget planning is not just about determining the total amount for the year, but also about distributing expenditures over time. Too many projects launched simultaneously leads to team and organizational overload. Too slow a pace of launching projects means loss of business value and competitive advantage.
In practical 2026 budget planning, the following principles are worth adopting. First, design quarterly rather than annually. Instead of planning all projects upfront for the entire year, it is better to plan the first quarter in detail, and for subsequent quarters, define priorities and indicative budgets with the possibility of adjustment during the year. Such flexibility is particularly important in the AI area, where the pace of technological change is very high.
Second, account for seasonality and business cycles. In many organizations, there are “freeze” periods (e.g., the holiday season for retail companies) during which deploying changes is prohibited or limited. The budget should account for these constraints and plan more intensive work during periods of lower load.
Third, build a budget reserve for unplanned expenditures. In the cybersecurity area, this may be an urgent need to respond to a newly discovered vulnerability or incident. In the AI area, this may be the emergence of breakthrough technology worth testing quickly. A reserve of approximately 10-15% of budget for unplanned expenditures is a reasonable practice.
Fourth, synchronize with license and contract renewal cycles. Many organizations have software and service agreements that renew at specific dates. Budget planning should consider these dates as opportunities to renegotiate terms or change vendors.
How to Build a Mechanism for Budget Monitoring and Adaptation Throughout the Year?
The best-prepared budget becomes outdated at the moment of first contact with reality. Therefore, it is crucial to create mechanisms for monitoring budget execution and adapting to changing circumstances.
An effective IT budget monitoring system should include the following elements. Regular budget execution reviews - at least quarterly, and monthly for key projects. Reviews should include not only financial execution (how much was spent vs. plan), but also business value realization (what results were achieved). Early warning metrics - leading indicators that allow early detection of problems. For IT projects, typical metrics include: schedule deviation, number of open defects, project team turnover, scope changes.
The budget change management process defines who has authority to approve transfers between budget lines and within what limits. Too rigid a process blocks flexibility, too loose leads to chaos. Portfolio reviews serve as periodic assessments of whether projects in execution are still priorities in light of changing business circumstances. Projects that have lost business justification should be stopped, and freed resources redirected to higher priorities.
In the context of the 2026 budget, monitoring AI projects is particularly important. This area is characterized by high uncertainty - projects that looked promising at the concept stage may prove unfeasible or less valuable than assumed. At the same time, new opportunities may emerge from technological progress. Flexibility in AI budget management is key.
How to Effectively Communicate and Justify IT Budget Before the Board?
Even the best-prepared budget is worthless if not approved by the board. Effective communication of IT budget requires translating technological language into business value language that is understandable to the CEO, CFO, and other board members.
The first principle is to speak the language of business, not technology. Instead of: “We need $2 million for middleware platform modernization,” better: “A $2 million investment will shorten time to market for new products by 40%, which according to the sales department will translate to an additional $8 million in annual revenue.” Each budget item should have a clear connection to the organization’s business goals.
The second principle is presenting alternatives and trade-offs. The board wants to make decisions, not just approve proposals. Instead of presenting one budget variant, it is worth presenting 2-3 scenarios with different investment levels and clearly described consequences of each. “With budget X, we can achieve goals A, B, C. With budget Y, only goals A and B. With budget Z, we risk consequences D and E.”
The third principle is risk quantification. Especially in the areas of cybersecurity and legacy modernization, where expenditures are often perceived as “costs” rather than “investments,” it is crucial to show the cost of the alternative - what happens if we do not invest. “The cost of an average security breach in our industry is $4 million. The proposed $500,000 investment reduces the probability of such an incident by 60%.”
The fourth principle is presenting the path to ROI. For each major investment, it should be shown when and how it will pay off. For AI projects, this may be direct revenue growth or cost reduction. For legacy modernization, this may be reduction in maintenance costs and freeing resources for innovation. For cybersecurity, this may be avoiding losses and regulatory penalties.
What IT Budget Allocation Model Is Recommended for 2026?
Based on the above analysis, I propose the following IT budget allocation framework for 2026 for a typical medium or large organization in the digital transformation phase. This model is a starting point for customization to a specific organization’s specifics.
The first category is Run the Business, meaning maintenance of existing systems and infrastructure. The recommended allocation is 45-50% of total IT budget. This category includes: infrastructure maintenance (datacenter, network, storage), software licenses and subscriptions, support and service, IT operations (helpdesk, incident management). Goal for 2026: reduction by 5 percentage points compared to 2025 through optimization and automation.
The second category is Grow the Business, meaning innovation and development projects. The recommended allocation is 35-40% of total IT budget. This category includes: AI and automation projects (15-20% of total budget), legacy system modernization (10-15% of total budget), new digital products and services (5-10% of total budget). Goal for 2026: increase by 5 percentage points compared to 2025.
The third category is Protect the Business, meaning cybersecurity. The recommended allocation is 10-15% of total IT budget. This category includes: security tools and technologies (5-7% of total budget), security services (SOC, penetration testing, audits) constituting 3-5% of total budget, training and awareness (1-2% of total budget). Goal for 2026: achieve NIS2 compliance and reduce risk to acceptable level.
The fourth category is strategic reserve for unplanned expenditures and new opportunities. The recommended allocation is 5-10% of total IT budget. Goal for 2026: maintain flexibility in responding to changes.
The table below presents the recommended IT budget allocation model for 2026 along with key priorities for each category.
| Category | Allocation | Key Priorities 2026 | Success Metrics |
|---|---|---|---|
| Run the Business | 45-50% | License optimization (SAM), operations automation (AIOps), infrastructure consolidation | TCO reduction by 10%, automation increase by 20% |
| AI and automation | 15-20% | Embedding AI in key operations, scaling successful pilots, data platform development | ROI from AI projects > 150%, 3+ production deployments |
| Legacy modernization | 10-15% | Priority modernization of 2-3 critical systems, cloud migration | Technical debt reduction by 20%, 30% systems in cloud |
| New digital products | 5-10% | Digital customer service channels, B2B process automation | Digital revenue growth by 25% |
| Cybersecurity | 10-15% | NIS2 compliance, Zero Trust, AI protection | Zero serious incidents, 100% NIS2 compliance |
| Strategic reserve | 5-10% | Rapid response to new opportunities and threats | Reserve utilization < 80% |
These proportions should be adjusted to the organization’s specifics. A company in the financial industry may need higher allocation to cybersecurity. A technology company may allow higher allocation to innovation at the expense of maintenance. A company with significant technical debt may temporarily need higher allocation to legacy modernization.
How Does ARDURA Consulting Support Organizations in Strategic IT Budget Planning and Execution?
Planning and executing an IT budget in such a complex technological and business environment is a challenge that requires not only internal competencies, but often also external perspective and specialized expertise. ARDURA Consulting has been supporting organizations in making strategic technology decisions and executing ambitious transformation programs for over a decade.
Our support in IT budget planning and execution encompasses several key dimensions. In strategic advisory, we help build budget allocation frameworks, prioritize projects, and justify investments before the board. Our external perspective, based on experiences from dozens of organizations across various industries, enables objective assessment of priorities and identification of optimization areas that may be invisible from within the organization.
In the artificial intelligence area, we support organizations at every stage of the journey - from identifying use cases with the highest ROI, through building data strategy, to implementing and scaling AI solutions. Our approach is based on the “business value first” principle - we focus on projects that deliver measurable results, not on technology for technology’s sake.
In the cybersecurity area, we offer comprehensive support - from audits and risk assessment, through security architecture design, to implementation support and preparation for compliance with regulations such as NIS2. We help organizations build a security level adequate to their risk profile, neither overloaded nor underinvested.
In the legacy modernization area, we have extensive experience in guiding organizations through complex transformation projects. We apply proven methodologies (6R model) and offer flexible collaboration models - from strategic advisory, through Staff Augmentation allowing supplementation of internal teams with missing competencies, to comprehensive execution of modernization projects in a Software Development model.
Our Trusted Advisor collaboration model means we do not sell solutions - we help make the right decisions. Sometimes this means recommending a smaller project scope than initially planned. Sometimes it means indicating that a given investment is not a priority at this moment. It always means honest, experience-based advice that serves the client’s long-term interests.
If you face the challenge of planning your IT budget for 2026 and are looking for a partner to help you make strategic decisions based on data and experience, contact ARDURA Consulting. We will be happy to share our perspective and jointly develop an approach tailored to your organization’s specifics.
Planning an IT project? Learn about our Software Development services.