Enterprise AI Budgets Face 2027 Reality Check as Compute Costs Surge
A Forrester forecast projects AI will account for roughly 20% of technology spending in 2027, forcing enterprise leaders to manage unpredictable usage costs, hidden operational taxes, and long-term return on investment.
Rising Infrastructure Costs and Unpredictable Pricing Models
According to an Oct. 21 report by market research firm Forrester, artificial intelligence is forecasted to account for about 20% of overall business technology spending in 2027. While the figure includes infrastructure investments made by technology vendors rather than indicating that every average enterprise will allocate a fifth of its own budget to AI, it underscores a major financial shift across the sector.
Speaking on the budget pressures facing chief information officers (CIOs), Forrester analyst Brian Hopkins noted that almost all of this spending growth is driven by infrastructure and computing capacity—including servers, chips, cloud services, foundational model access, and software subscriptions.
Generative AI is also transforming enterprise software purchasing. Traditionally, organizations could forecast annual application costs and budget accordingly. Generative AI and autonomous agents have disrupted this predictability by introducing token-based consumption, usage-based fees, and credits. The running cost of an AI agent now fluctuates based on task complexity and the raw computing power required to execute it.
Where Companies Are Sourcing Funds—and Where They Shouldn't Cut
To accommodate rising AI expenditures at a time when enterprise software spending is growing slower than inflation, organizations are seeking savings across their IT operations. Companies are demanding lower pricing on external IT service contracts, arguing that service providers should pass down efficiency gains derived from AI. Additionally, enterprises are pulling software development, support, and customer service functions back in-house to reduce third-party costs.
However, Hopkins warned against trimming spending in critical areas:
- Cybersecurity: Delayed security investments have historically occurred because security is treated as a cost center. However, risks are escalating rapidly as malicious actors use AI agents to scan for and exploit vulnerabilities at unprecedented scale.
- Legacy Core Infrastructure: Decisions to modernize longstanding, high-cost enterprise resource planning (ERP) platforms and systems such as Salesforce have frequently been delayed. These unresolved infrastructure needs are now colliding with mounting AI costs.
Navigating the "Trust Tax" and Redefining ROI
Enterprise adoption of agentic AI comes with hidden operational costs that Forrester calls the "trust tax." Deploying autonomous agents is rarely as simple as automating a task. Organizations must also budget for testing and red-teaming, building guardrails, implementing continuous monitoring, maintaining a centralized registry with governance reviews, and verifying agent identities when interacting with external partners or customers.
Hopkins also highlighted that enterprise leaders often miscalculate the financial returns of AI by focusing primarily on short-term productivity gains. Forrester organizes AI financial outcomes across three time horizons:
- Productivity (Short-Term): Measuring time saved on routine tasks, such as writing customer emails or resolving support cases. While easy to track and commonly reported, simple productivity gains rank significantly lower in their impact on bottom-line business results.
- Engagement (Medium-Term): Improving customer and employee interactions to make transactions easier, raising customer satisfaction, and boosting interaction frequency.
- Transformation (Long-Term): Rethinking business processes, accelerating product delivery, creating new revenue streams, or entering markets ahead of competitors to secure a lasting competitive advantage.
Prioritizing Investments for the 2028–2029 Divide
To capture long-term value, Forrester recommends that CIOs focus on three core investment priorities: security, data, and the AI platform. Rather than delaying adoption until all three pillars are perfected, organizations should establish an AI strategy tied directly to clear business metrics—such as an energy firm linking AI deployment to performance targets like cost per barrel, production efficiency, and workplace safety.
As enterprises head into a challenging 2027, companies will need robust governance, financial operations (FinOps) disciplines, and explicit controls over AI agent access. Hopkins projects a widening split: organizations that view AI merely as a basic productivity tool are likely to pull back as costs grow, whereas companies that strategically target engagement and business transformation will secure ongoing funding. The full financial impact of this market divide is expected to become clearly visible between 2028 and 2029.
