How Does AI Fit into Your Technology Budget

Key Takeaways

• AI is eating up a third of enterprise change budgets while simultaneously increasing run costs, creating a compounding pressure that most organizations are not yet proactively managing.
• The organizations extracting the most value from technology are deliberate modernizers, keep run costs at least 20% lower than peers, and direct at least a third of their budget toward change activities.
• Spending more on AI without retiring legacy systems does not create value. It creates complexity, increases technical debt, and inflates the cost of running everything underneath.
• Two-thirds of top-performing technology organizations have technology leaders very involved in enterprise strategy, compared with just over half of other companies.
• The winning move is not spending more. It is spending differently, making deliberate choices about what to simplify, retire, and stop so that modern capabilities can take hold.

Take your technology budget and divide it into two piles. One covers everything it takes to keep existing systems running. The other funds building something new.

For most enterprises, the first pile is much larger than the second. It pays for maintenance on aging infrastructure and legacy applications that everyone depends on, but nobody wants to modify.

The innovation pile is what’s left. And now AI is eating up much of that pile.

Global AI spending is expected to surpass $2 trillion in 2026, according to Gartner. McKinsey's March 2026 research on technology spending at global companies shows that AI is consuming up to a third of enterprise change budgets while also adding to run costs. Every new AI deployment brings models to maintain, platforms to govern, and controls to manage, without reducing the legacy footprint underneath.

The organizations that figure out how to break this cycle will pull ahead. The ones that do not will find that every AI investment reinforces the very complexity they were trying to escape.

Why Is the AI Technology Budget Problem Getting Harder to Solve?

Every technology leader knows this tension well. There is a budget that keeps existing systems operational and covers infrastructure, cybersecurity, compliance, and cloud platforms. There’s a separate budget that funds new capabilities, like modernization, application development, data and analytics, and AI.

AI investments often stack on top of existing spending. Legacy systems keep running. New AI platforms get deployed alongside them. The total cost of the technology infrastructure grows, but the share available for genuine innovation stays flat or shrinks.

How Are Companies Allocating Their Technology Budgets?

McKinsey's research identified four archetypes based on how organizations balance run spending against change investment. Where an organization falls on this map has direct implications for its ability to compete in an AI-driven market.

Deliberate modernizers allocate at least a third of their technology budget to change activities and keep run costs at least 20% lower than peers. How? By deploying standardized platforms, designing services for reuse, and ensuring new capabilities replace legacy systems rather than accumulate on top of them.

Technical debt stays manageable, run costs decline over time, and the freed-up budget flows back into change initiatives, including AI. These organizations invest nearly twice as much in data and analytics as their peers, laying the foundation for AI to scale without driving proportional cost increases.

Strained transformers are heading in the right direction, but making a costly mistake. They’re funding change and AI investment, but building on top of existing systems rather than replacing them. The result is a triple cost burden: maintaining legacy platforms, operating new applications on top of them, and now governing AI on top of that. Returns are flattened because gains from new investments are continuously offset by rising run costs underneath.

Lean operators keep both run and change investments small. This worked for organizations with stable operations and tight cost controls, but AI is changing the economics of even traditional industries. The business case that justified low technology investment in the past is being rewritten, and lean operators who do not respond will find themselves behind competitors who moved earlier.

Heavy IT sustainers commit most of their budget to running and little to changing. Some face genuine structural constraints from long-standing system complexity. Others have completed a major modernization effort and are maintaining the result. The risk for both groups is that AI investment, when it comes, adds to run costs rather than replacing them because the infrastructure was never designed to absorb new capabilities without introducing new fragmentation.

Understanding which archetype describes your organization is the starting point for recalibrating the budget. It is also the lens through which every AI investment decision should be evaluated before it is made.

Why Does Adding AI Without Retiring Legacy Systems Backfire?

The intuition behind AI investment is straightforward: automate work, reduce costs, and free up capacity for higher-value activity. That instinct is correct. But the path from investment to savings is not automatic, and most organizations are learning this the hard way.

AI deployments require infrastructure to run, models to maintain, governance frameworks to manage, and data pipelines to operate continuously. When these are built alongside existing systems rather than replacing them, they add to the run burden rather than reducing it. The organization ends up managing more technology than before, and the efficiency gains from AI are offset by the cost of the expanded estate.

Companies that add AI on top of existing complexity without making deliberate choices about what to retire are not modernizing. They are accumulating. And accumulation is precisely the condition that keeps run costs high, change budgets constrained, and AI ROI out of reach.

The organizations getting this right treat AI as a catalyst for simplification, identifying and retiring redundant systems as AI capabilities take over. This requires a level of decision-making discipline that traditional technology governance often lacks, making the development of that discipline just as critical as the AI investment itself.

Learn More: Rethinking Enterprise Architecture for the Agentic Era

What Do the Best-Performing Technology Organizations Do Differently?

Deliberate modernizers do not simply spend less on run and more on change. They make specific decisions that produce that outcome consistently. Three practices distinguish them from every other archetype.

They modernize the full technology stack in parallel, not piecemeal. Rather than updating one layer at a time, they spread change investments consistently across all major technology domains. This creates a coherent foundation rather than a partially modernized estate where AI sits on top of infrastructure that was never designed to support it.

They build data and analytics foundations first. Organizations that underinvest in data consistently find their AI deployments hit a ceiling. The models work, but the data quality, accessibility, and governance required to operate them reliably at scale is simply not there. Deliberate modernizers build this foundation early, which is why they can scale AI without proportional cost increases.

They use internal teams to drive change, not external vendors. Deliberate modernizers allocate 16% of their overall technology budgets to internal staff working on change activities, up to four times more than other organizations. Internal teams maintain continuity, build institutional knowledge, and align technology decisions with business goals in ways that vendor relationships structurally cannot replicate. Technology investment without the internal talent to apply it does not pay back.

The broader organizational discipline behind these choices involves strategic governance and investment sequencing. These foundational decisions are what separate programs that generate measurable returns from those that remain stalled in the pilot phase.

Learn More: Seizing the Agentic AI Advantage

What Should CIOs Do Differently When Building Their AI Budget?

Three moves separate the organizations that get AI budgeting right from those that simply spend more.

Decide explicitly what to remove from run. Without these decisions, every AI investment adds to the operating burden rather than reducing it. It doesn’t require wholesale replacement. Often, organizations can reduce usage of certain services or switch to lower-cost alternatives without affecting performance. But it requires the kind of deliberate governance decision that most technology portfolios are not currently structured to make.

Direct change spending toward capabilities that reduce future costs. Rather than optimizing for near-term delivery alone, CIOs should direct change spending toward shared platforms, standardized services, and data foundations that lower the marginal cost of future innovation. This is the logic that allows agentic AI to scale without driving proportional increases in run costs.

Use AI to simplify, not to multiply. The most forward-thinking technology leaders are embedding AI into streamlined processes so that AI eventually replaces work and systems rather than running alongside them indefinitely. This is the only path to a technology estate that genuinely gets leaner as AI investment grows.

The Budget Decision That Determines Everything Else

AI costs are rising. Legacy run costs are not falling without deliberate action. And the compounding effect of inaction grows with every budget cycle.

The organizations that look back on this period as a turning point are the ones making clear choices now about what to stop funding and what to fund instead. Those choices free up the budget capacity that AI investment requires to generate returns. The ones that simply add AI on top of what already exists will find themselves with a more expensive, more complex technology estate and fewer resources to do anything meaningful with it.

Recalibrating is not a technology decision. It is a prioritization decision. And it is the one that matters most right now.

Contact Taazaa today to discover how our engineering teams help organizations modernize their technology infrastructure, reduce run costs, and lay the foundations for AI investments to generate lasting returns.

Frequently Asked Questions

Q: What is the difference between run and change spending in a technology budget?

Run spending covers everything required to keep existing systems operational: infrastructure maintenance, cybersecurity, regulatory compliance, cloud platform costs, and application support. Change spending funds new capabilities that create business value: modernization, application development, data and analytics, and AI. The balance between these two categories determines how much of a technology budget is available for innovation versus maintenance.

Q: Why does AI investment increase run costs rather than reduce them?

AI deployments require ongoing infrastructure to operate, models to maintain, governance frameworks to manage, and data pipelines to run. When these are built alongside existing systems rather than in place of them, they add a new layer of operating cost without reducing the legacy footprint below. The organizations avoiding this pattern are those that use AI to replace existing systems and processes rather than simply augment them.

Q: What makes a deliberate modernizer different from other organizations?

Deliberate modernizers allocate at least a third of their technology budget to change activities and keep run costs at least 20% lower than peers. They achieve this through standardized platforms, services designed for reuse, and a consistent discipline of retiring legacy capabilities as new ones are introduced. They also invest nearly twice as much in data and analytics as other organizations.

Q: Where should a CIO start when recalibrating their technology budget for AI?

Start with an honest accounting of what is in the run budget and whether each item is essential, reducible, or removable. This audit reveals deferred decisions, legacy systems nobody wants to own, the retirement of platforms that could be consolidated, and services paid for at levels that do not reflect actual usage. Making those decisions explicitly, rather than defaulting to the status quo, creates the budget capacity to fund AI initiatives without simply adding cost to an already constrained baseline.