7 hiba, ami miatt elszállhat a céges AI-büdzsé

CIOs have always felt pressure to deploy emerging technologies rapidly. Implementation brings challenges, including calculating and measuring upfront investment costs. Deploy too quickly, and when the hype fades, many IT departments are left with a costly architecture to support, security gaps, and operational issues. The same trend is happening with AI. Many CIOs procured enterprise-wide AI model licenses and empowered employees to experiment with language models and develop AI agents. Others asked DevOps teams to accelerate AI code generation and vibe coding. The shift from rapid experimentation to deploying more AI capabilities to production can leave CIOs with mounting AI debt and AI FinOps gaps. CIOs’ near-term concern is managing budgets amid consumption-based AI pricing models. Even as AI token costs decrease, more employees are using AI, and agents are given more challenging, expensive assignments. Here are seven common mistakes IT leaders make that result in blown AI budgets. Most CIOs and CAIOs recognize the need to establish AI strategies focused on areas where AI can provide lasting business value. But many fail to quantify targeted business outcomes, leaving employees to use adoption metrics as the primary objective. “Every token spent on a pilot that never proves its value is money leaders can’t get back and lost opportunity cost, which is a governance failure more than a technology one,” says Rob Scudiere, CTO at Verint. “Every agent and workflow should have to earn its budget by showing results that achieve the desired business outcome, or lose it.” Kris Beevers, CEO and cofounder of Netbox Labs, says to use existing business metrics to see whether the areas adopting AI are improving them faster. “If adoption is climbing but the outcomes aren’t moving with it, that’s a pretty good signal to rethink where you’re spending,” Beevers says. Recommendation: Financial impacts may be hard to target during experimental phases, but employees should set financial objectives before moving AI agents into development and deployment phases. CIOs may find they have a real mess on their hands when a few pilot AI agents quickly scale to thousands running in production. Much of AI governance focuses on identifying owners, setting data-access rules, defining target business outcomes, and establishing accuracy guidelines. CIOs must also educate employees on underlying cost factors, including model selection and platform standardization. “Delayed AI governance gets framed as a compliance risk, but it’s just as much a budgetary concern,” says Guy Bourgault, head of agentic systems at Concentrix. “The longer organizations wait to define ownership, standards, and controls, the more they end up funding inefficiency, rather than progress.” Varun Goswami, global head of product and AI at Newgen, says AI budgets are wasted on scattered pilots, duplicated integrations, and agents that run quietly without ever proving value. “Build on one governed platform rather than a dozen parallel stacks, reuse templates and connectors instead of custom-building each use case, and match the model to the job rather than routing everything to the priciest foundation model,” Goswami recommends. Data quality is a third concern, according to Collibra and The Harris Poll, with 96% of IT leaders saying project failure almost always traces back to poor data foundations, and 51% reporting significant staff hours spent manually correcting autonomous AI agent outputs. CIOs should set data quality metrics and establish data products to build a trustworthy AI context layer. Recommendation: CIOs should update and communicate their AI governance policies monthly, as AI cost, capabilities, compliance, and security considerations evolve rapidly. Much of the discussion around AI token economics, or tokenomics, focuses on the costs of using different AI models. But the FinOps Foundation defines tokenomics as “the study of how the production, distribution, and c