Growing investment in AI is challenging enterprises to rethink IT budgets. Forrester forecasts that AI will account for about 20% of business technology spending in 2027. The estimate, included in an Oct. 21 report, includes infrastructure spending by technology vendors; it doesn't mean the average enterprise will devote that share of its own IT budget to AI.
In this Q&A, Brian Hopkins, an analyst at Forrester, discusses the pressures this is putting on enterprise budgets, how CIOs can prioritize spending, and how to judge whether AI investments deliver lasting value.
What's driving the increase in AI spending?
Brian Hopkins: Almost all of the growth comes from infrastructure and computing capacity — servers, chips, cloud services, AI model access and software subscriptions.
AI is also changing how companies pay for software. Traditionally, organizations could estimate an application's annual cost and budget accordingly. With generative AI, costs are harder to predict. Vendors are introducing token-based consumption, usage charges and credits. And agent costs vary with task complexity and the computing power required.
Where can CIOs find money for AI investments?
Hopkins: Enterprise software spending is rising more slowly than inflation, eroding purchasing power.
To compensate, companies are pushing for lower prices on IT services contracts, arguing providers should pass on AI-driven efficiency gains, and are bringing software development, support, and customer service in-house to cut external costs.
Where should enterprises avoid cutting technology spending?
Hopkins: Security is the obvious one. Organizations have delayed security investments because they view security as a cost center. But risks are becoming more urgent as attackers use AI agents to find and exploit vulnerabilities at a scale we haven't seen before.
Companies also haven't modernized longstanding, expensive ERP infrastructure and platforms such as Salesforce. CIOs have been asking for investment in these areas for years, but businesses have often delayed those decisions.
These longstanding issues are converging just as rising AI costs put additional pressure on IT budgets.
What are companies getting wrong about the ROI of AI?
Hopkins: We see three financial outcomes — revenue growth, efficiency/cost reduction, and better risk management — organized into three time horizons: productivity (short term), engagement (medium term), and transformation (long term).
Productivity is easiest to measure. Companies can track whether AI reduces the time employees spend writing customer emails or speeds up customer service case resolution. Productivity is the most commonly reported AI benefit, yet its bottom-line impact ranks much lower.
Engagement involves improving how customers and employees interact with a business. For example, helping customers get answers and complete transactions more easily can increase transaction frequency and improve satisfaction.
Transformation involves rethinking business processes, accelerating product delivery, creating new products or entering markets ahead of competitors. These investments carry more risk but can create a lasting competitive advantage.
Companies that focus only on productivity risk missing the larger opportunities in engagement and transformation. Capturing those benefits also requires coordination across departments, with shared priorities and agreed-upon outcomes.
How are companies accounting for AI's less visible costs?
Hopkins: We call those costs the "trust tax," and agentic AI is bringing them to the forefront.
Companies might expect an agent to simply automate a process but often need to also test and red-team it, add guardrails and monitoring, maintain a centralized registry with governance review, and verify its identity if it interacts with customers or partners.
These requirements create additional overhead, and the industry is still trying to determine how much organizations should expect to spend.
Where should CIOs prioritize AI investments?
Hopkins: There are three main investment priorities: security, data and the AI platform. But CIOs can't delay AI adoption until all three areas are fully addressed, especially in industries undergoing rapid disruption.
Companies should start with an AI strategy that identifies their major business challenges, competitive advantages and desired outcomes. They should translate that strategy into measurable targets and create an investment portfolio, with a business executive accountable for results and a portfolio manager responsible for funding.
For example, an oil company might measure performance through cost per barrel, production and safety. AI investments should be tied to improving those metrics. Once a company understands how an investment will affect a business metric, it can identify the foundational work required to achieve that outcome.
Will companies continue to increase AI budgets without clear returns?
Hopkins: Organizations need to invest in security, responsible AI, governance, FinOps and controls over agent access and consumption in what is likely to be a challenging 2027.
Some companies will recognize the value AI can deliver through engagement and transformation. They'll be able to build business cases and secure funding. Companies that remain focused on AI as just another productivity tool are more likely to struggle.
Many companies may pull back, while a smaller group pushes forward with the right strategy — a divide whose full financial impact likely won't be visible until 2028–2029.
Editor’s note: This interview has been edited for clarity and conciseness.
Kinza Yasar covers AI and emerging technology for TechTarget, with a focus on ethics, enterprise adoption, governance and business strategy. Before moving into journalism, she worked in IT and network support roles, giving her a systems-level perspective on how enterprise technologies are built, deployed and managed.
