The Cost of Tech Ambition in 2027

November 17, 2026 • 3 Min Read

The technology agenda for 2027 is getting bigger. AI is moving into everyday operations, cybersecurity threats are becoming more complex, and businesses are under pressure to modernize the systems and data on which both depend. Yet the budgets available to CIOs are growing far more slowly than the demands being placed on them. 

Gartner forecasts worldwide IT spending will reach $7.35 trillion in 2027, a 15% increase from 2026. But its 2027 CIO survey found that individual IT budgets are expected to increase by an average of just 3.7%. Funding for agentic AI alone is projected to rise nearly 32%. 

These figures measure different things, but together they illustrate an increasingly difficult planning environment. Technology spending is expanding across the market while individual organizations face difficult choices about which investments they can support. 

And those choices are becoming more consequential. AI is no longer simply another technology initiative competing for resources. Its adoption brings new requirements for data, infrastructure, cybersecurity, governance and workforce capabilities. Companies must fund the technology while also finding the capacity to make it productive. 

The question for 2027 is how to make more disciplined decisions about which investments will create lasting business value.

AI’s Growing Appetite for Investment

AI will dominate technology planning in 2027, but the economics are becoming more complicated. 

According to Gartner’s 2027 CIO research, 37% of surveyed organizations have already deployed AI agents, with another 34% planning deployments within the next 12 months. This expansion is expected to bring significant new spending on AI infrastructure, software and supporting capabilities. 

The anticipated returns remain less certain. Gartner found that only 13% of CIO respondents reported significant value from AI tools so far. Meanwhile, 58% face increasing pressure to deliver AI-related cost savings, even as 51% expect AI to increase technology’s total cost of ownership. 

The tension is understandable. An AI application may reduce the time required to complete a task while introducing additional costs for software licenses, computing capacity, integration, oversight and maintenance. Savings in one department may be offset by expenses elsewhere. And increased employee productivity does not automatically translate into measurable financial returns. 

For CFOs and CIOs, the challenge is to evaluate AI as a business investment rather than an isolated technology purchase. 

That means understanding the full cost of deploying and operating an AI capability, identifying the outcomes it is expected to improve and establishing who is accountable for realizing those benefits. 

It also means being willing to discontinue initiatives that are not delivering sufficient value. As experimentation gives way to enterprise deployment, funding every promising use case will become increasingly difficult to justify.

The Priorities That Cannot Wait

AI may be attracting the most attention, but it is competing with technology investments that organizations cannot simply postpone. 

Cybersecurity is one of them. As AI becomes more deeply embedded in enterprise operations, organizations face new questions about access, oversight and accountability for actions taken by autonomous systems. 

Gartner’s 2027 strategic technology trends reflect this concern, emphasizing AI agent identity, autonomous vulnerability remediation and stronger controls over machine-generated software. 

These capabilities will require investment alongside established security priorities. 

Data infrastructure presents another challenge. AI applications depend on reliable information, yet many organizations still operate with fragmented data environments, inconsistent definitions and aging systems. Investments in AI can expose these limitations, increasing demand for integration, data quality and modernization work that may not have been included in initial budgets. 

Technical debt adds further pressure. Maintaining outdated applications absorbs money and talent that could otherwise support new capabilities. Yet deferring maintenance or modernization to finance AI initiatives can increase operational risk and future costs. 

For executives, these expenditures should not be viewed simply as competing technology categories. They are often interdependent. Funding an advanced AI capability without addressing the data and security requirements it depends on can put the entire investment at risk.

The Budget Problem Is Also an Ownership Problem

The growing role of business functions in technology development is changing where spending originates, but financial accountability has not necessarily followed. 

Gartner reports that an average of 15.3% of technology solutions are now developed outside centralized IT. Eighty percent of technology executives expect AI to increase business-developed solutions by 2030. Yet 73% of surveyed enterprises have no plans to establish ownership rules for technology costs and solutions. 

This presents a significant budgeting challenge. 

A business function may introduce an AI application to improve a local process, while IT becomes responsible for integration, security and ongoing support. Finance may see an initial business case that does not account for future operating expenses or dependencies on enterprise infrastructure. 

As these investments multiply, companies can lose sight of what they are spending and whether individual improvements are contributing to broader business performance. 

RGP’s 2026 CFO Perspectives research offers a useful perspective. Eighty-nine percent of surveyed CFOs said organizational misalignment slows execution at least sometimes. The findings suggest that coordinating technology investments across the enterprise is as important as evaluating their individual financial merits. 

For 2027, budgeting needs to become a more collaborative exercise. Finance, technology and business leaders should agree on the value an investment is expected to deliver, how its full costs will be allocated and who will be responsible for the result. 

Without that discipline, the proliferation of business-led technology could make spending harder to control just as demands for demonstrable returns intensify.

What Should Come Off the Budget?

Perhaps the hardest 2027 planning conversation will concern what organizations should stop doing. 

Technology budgets often grow through accumulation. Existing applications remain in place, new tools are introduced, and capabilities that have outlived their usefulness continue consuming resources because retiring them requires difficult operational decisions. 

AI creates an opportunity to reconsider that pattern. 

If a new capability replaces an established activity, leaders should identify which applications, processes and expenses can be eliminated. If a modern platform reduces the need for custom integrations, the business case should include the cost of retiring those connections. If automation changes how work is performed, the budget should account for the organizational adjustments needed to realize the benefit. 

This requires examining technology spending as a portfolio rather than evaluating each investment separately. 

Organizations should distinguish between spending required to maintain safe and reliable operations, investment in capabilities essential to future performance, and discretionary initiatives whose returns remain uncertain. 

That assessment should extend beyond IT. Business leaders need to participate in decisions about which processes can change and which systems are no longer necessary. 

The objective is not simply to reduce spending. It is to release resources tied up in arrangements that no longer contribute enough value. 

Four Decisions for the 2027 Planning Cycle

For senior executives, a more disciplined technology budget begins with several practical decisions. 

Establish a complete view of AI spending. Bring together technology and business-function investments, including software, infrastructure, data, integration, governance and ongoing operating costs. Require a common method for evaluating returns so that funding decisions are made on comparable terms. 

Protect the capabilities on which new investments depend. Identify critical data, cybersecurity and infrastructure requirements before committing additional resources to AI. A project should not be considered fully funded if the organization lacks the foundations needed to make it reliable and productive. 

Create room by retiring what no longer serves the business. Review overlapping applications, underused licenses, outdated processes and projects with limited evidence of value. Assign responsibility for retiring systems and track whether the anticipated savings actually materialize. 

Fund changes in how work gets done. Technology investments should include the cost of preparing employees, redesigning responsibilities and helping functions work together effectively. Measure benefits after implementation, when the organization has had an opportunity to demonstrate whether the investment changed performance. 

These decisions require CFOs, CIOs and business leaders to share responsibility for technology’s financial and operational outcomes.

A Different Standard for Technology Investment

The opportunity in 2027 is considerable. AI is creating possibilities for improving business performance that were difficult to achieve with previous generations of technology. But the growing cost and complexity of pursuing those possibilities demand greater discipline. 

The most effective technology budgets may not be those that accommodate the most ambitious investment plans. They may be those that make the clearest choices about what the organization needs, what it can afford to support and what it is prepared to change. 

That is also where cohesion becomes relevant across the enterprise. When strategy, technology investment and operational decisions are made with a shared understanding of business priorities, organizations are better equipped to coordinate resources and act on opportunities as they emerge. 

For 2027, the challenge is to move beyond funding technologies in isolation and begin investing in the ability of the enterprise to put them to work. The competitive advantage will come from how deliberately companies turn spending into results.