7 CIO Mistakes That Drive AI Budgets Over Plan

CIOs seeking to keep AI spending within budget need to manage both the cost and performance of agents deployed in production. CIO.com contributor Isaac Sacolick warns that AI debt and gaps in AI financial operations can build as companies buy enterprise model licenses, support employee experimentation, encourage AI code generation and vibe coding, and expand AI features into production. Total spending can rise even as token prices fall if more employees use AI and agents take on harder, more expensive work. He lists seven mistakes that can push IT leaders over budget.

The first mistake is setting strategy without measurable business outcomes. CIOs and chief AI officers may identify areas where AI can create lasting value but fail to quantify targets, leaving adoption and usage metrics as employees’ goals. Rob Scudiere, CTO of Verint, says token spending on pilots without proven value is an unrecoverable cost and opportunity loss, reflecting a governance failure more than a technology failure. Agents and workflows should earn funding by meeting outcome targets; otherwise, their budgets should be reclaimed. Chris Bivers, CEO of NetBox Labs, advises checking whether existing business metrics improve faster in departments adopting AI and reconsidering spending if rising usage does not bring better results. Financial targets may be hard to set during experimentation, but should be defined before work moves into development and deployment.

The second mistake is experimenting without governance. When pilot agents multiply into thousands and run in production, it can become difficult to assign owners, set data-access rules, define outcomes, and establish accuracy standards. IT leaders also need to train teams on cost drivers such as model and platform standardization. Guy Burgos of Concentrix says delayed governance creates budget risks as well as compliance risks: the longer controls and standards are postponed, the more money goes to inefficiency instead of progress. Varun Goswami of Newgen warns that scattered pilots, duplicate system connections, and agents running without proven results waste money. He recommends a governed shared platform, reusable templates and connectors, and choosing a model suited to each task instead of sending every request to the most expensive foundation model. In a Collibra and Harris Poll survey, 96% of IT leaders said project failures almost always trace back to poor data foundations, while 51% said employees spend substantial time manually correcting autonomous agents’ output. CIOs should build a trusted AI context layer with data-quality metrics and data products, and update and communicate governance policies monthly as costs, capabilities, regulations, and security conditions change.

The third mistake is tracking token costs without measuring the business value they produce. The FinOps Foundation defines tokenomics as the study of how tokens are produced, distributed, and consumed to create organizational performance and AI value. Organizations using multiple models, software-as-a-service products with AI agents, and in-house agents should work with their CFO to measure the full impact: direct token costs, employees’ time and effort, ongoing inference costs, and value created. UKG CIO Prakash Kota proposes starting with outcomes and asking three questions: whether specialized expertise is needed, whether existing tools can solve the problem, and whether using tokens creates enough value to justify the investment. Set budgets by employee, department, and workflow, then review and adjust them regularly based on results. Experimentation can be encouraged at first, with spending controls applied once agents enter production. Tom Shea of OneStream says the same tokens might help an experienced engineer launch a product sooner or help a non-specialist create 10 versions of a presentation; governance should help experts increase their output.

The fourth mistake is building agents without standards. In Stack Overflow’s 2026 developer survey, 73% of developers said they use AI coding assistants and agents daily, but when choosing models and writing prompts, 75% prioritized quality over cost (38%). CIOs should set guidance for balancing cost and quality and require code analysis in deployment pipelines. A Sonar survey found that 96% of developers did not fully trust AI-generated code, and 48% always checked AI-assisted code before committing it. Sonar CTO Andrea Malagodi says guardrails and engineering standards can reduce rework costs, while real-time data on cost per pull request can support governance. CIOs should keep tracking the cost and quality of code generation.

The fifth mistake is using frontier models for every task. Craig Wiley, vice president at Databricks, says 77% of companies use multiple model families. The best model can vary by task, and using several models makes it harder to manage price changes and a distributed set of vendors, as well as to see where budgets

CIOs seeking to keep AI spending within budget need to manage both the cost and performance of agents deployed in production. CIO.com contributor Isaac Sacolick warns that AI debt and gaps in AI financial operations can build as companies buy enterprise model licenses, support employee experimentation, encourage AI code generation and vibe coding, and expand AI features into production. Total spending can rise even as token prices fall if more employees use AI and agents take on harder, more expensive work. He lists seven mistakes that can push IT leaders over budget.

The first mistake is setting strategy without measurable business outcomes. CIOs and chief AI officers may identify areas where AI can create lasting value but fail to quantify targets, leaving adoption and usage metrics as employees’ goals. Rob Scudiere, CTO of Verint, says token spending on pilots without proven value is an unrecoverable cost and opportunity loss, reflecting a governance failure more than a technology failure. Agents and workflows should earn funding by meeting outcome targets; otherwise, their budgets should be reclaimed. Chris Bivers, CEO of NetBox Labs, advises checking whether existing business metrics improve faster in departments adopting AI and reconsidering spending if rising usage does not bring better results. Financial targets may be hard to set during experimentation, but should be defined before work moves into development and deployment.

The second mistake is experimenting without governance. When pilot agents multiply into thousands and run in production, it can become difficult to assign owners, set data-access rules, define outcomes, and establish accuracy standards. IT leaders also need to train teams on cost drivers such as model and platform standardization. Guy Burgos of Concentrix says delayed governance creates budget risks as well as compliance risks: the longer controls and standards are postponed, the more money goes to inefficiency instead of progress. Varun Goswami of Newgen warns that scattered pilots, duplicate system connections, and agents running without proven results waste money. He recommends a governed shared platform, reusable templates and connectors, and choosing a model suited to each task instead of sending every request to the most expensive foundation model. In a Collibra and Harris Poll survey, 96% of IT leaders said project failures almost always trace back to poor data foundations, while 51% said employees spend substantial time manually correcting autonomous agents’ output. CIOs should build a trusted AI context layer with data-quality metrics and data products, and update and communicate governance policies monthly as costs, capabilities, regulations, and security conditions change.

The third mistake is tracking token costs without measuring the business value they produce. The FinOps Foundation defines tokenomics as the study of how tokens are produced, distributed, and consumed to create organizational performance and AI value. Organizations using multiple models, software-as-a-service products with AI agents, and in-house agents should work with their CFO to measure the full impact: direct token costs, employees’ time and effort, ongoing inference costs, and value created. UKG CIO Prakash Kota proposes starting with outcomes and asking three questions: whether specialized expertise is needed, whether existing tools can solve the problem, and whether using tokens creates enough value to justify the investment. Set budgets by employee, department, and workflow, then review and adjust them regularly based on results. Experimentation can be encouraged at first, with spending controls applied once agents enter production. Tom Shea of OneStream says the same tokens might help an experienced engineer launch a product sooner or help a non-specialist create 10 versions of a presentation; governance should help experts increase their output.

The fourth mistake is building agents without standards. In Stack Overflow’s 2026 developer survey, 73% of developers said they use AI coding assistants and agents daily, but when choosing models and writing prompts, 75% prioritized quality over cost (38%). CIOs should set guidance for balancing cost and quality and require code analysis in deployment pipelines. A Sonar survey found that 96% of developers did not fully trust AI-generated code, and 48% always checked AI-assisted code before committing it. Sonar CTO Andrea Malagodi says guardrails and engineering standards can reduce rework costs, while real-time data on cost per pull request can support governance. CIOs should keep tracking the cost and quality of code generation.

The fifth mistake is using frontier models for every task. Craig Wiley, vice president at Databricks, says 77% of companies use multiple model families. The best model can vary by task, and using several models makes it harder to manage price changes and a distributed set of vendors, as well as to see where budgets