Don't count the savings until you know what the AI actually costs

For the past two years, discussions around AI have primarily centered on job displacement and automation. However, enterprise customers are now raising a different concern: the hidden costs associated with AI implementation. Boards, executive teams, and business units have been spurred to adopt AI tools rapidly, but they are now facing mounting bills that they didn't anticipate.

Organizations are discovering they have limited visibility into AI consumption, leading to budget overruns, runaway token usage, and unexpectedly large invoices.

AI's integration into daily business applications, such as Microsoft Copilot, Google Gemini, SAP, Workday, and LinkedIn, is exacerbating this issue. Every AI decision and action incurs a cost, from reasoning and retrials to planning, triggering calls, leveraging data and compute. AI is embedded in various teams across an organization, including marketing, sales, product, and development. With agentic AI expected to increase token consumption by 24-fold by 2030, AI spend is becoming more widespread.

Without visibility into AI costs, organizations lack the foundation for governance, control, and accountability. When making workforce decisions based on projected AI savings, it is crucial to consider all cost components, including token consumption, infrastructure, data platforms, cloud resources, failed attempts, retries, and the human oversight required to validate outputs and manage exceptions.

AI costs can move around, spreading across different systems, teams, and budgets, making them difficult to measure accurately.

The answer is not to halt AI innovation or abandon AI initiatives. Instead, organizations must understand where AI creates value, its operating costs at scale, and how to make informed decisions about AI adoption. This requires visibility into which AI applications, agents, and models are being used, token consumption, cost accumulation, and usage mapped to business units, products, and outcomes.

By gaining this visibility, organizations can make informed decisions about optimization, such as selecting different models, identifying unnecessary consumption, and discovering when lower-cost alternatives can achieve the same outcome. Ultimately, understanding AI's economics is essential before making financial decisions.