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NewsMaterial4 min readDisconfirmAI Editorial

OnlyCFO Author: AI May Redefine ARR and Cost Presentation; Annual Plans May Not Hold for a Full Year

An author writing for the OnlyCFO newsletter argues that AI products may force companies to expand their definition of Annual Recurring Revenue (ARR) to include consumption-based pricing, while AI expenditures are becoming or will soon become the largest cost category requiring detailed breakdowns for boards. Additionally, the rapid pace of AI change may render the underlying assumptions of annual plans invalid within the year.

2026-11-03 20:39 GMT+8

The core function of annual planning is not merely the three financial statements, but rather establishing strategic consensus among the company’s internal teams and its board regarding the most important priorities for the coming year. The author of the OnlyCFO newsletter raised this point on November 3, 2026, noting that due to continuous disruption and evolving impacts brought by AI, the importance of such alignment has never been greater. His specific questions were: how the financial framework presented by CFOs to boards needs to adjust as AI changes revenue definitions and cost structures, and how long an annual plan itself can realistically remain valid.

At the level of revenue metrics, the author believes that changes driven by AI may require updating how ARR is presented. He assesses that if a company’s AI products are entirely based on consumption-based pricing, and future growth bets are placed there, then this revenue stream must be separated out because gross margin profiles associated with different pricing models can vary significantly. He recommends splitting revenue streams by pricing model type and notes that if the definition of ARR changes, this must be clearly marked in the appendix. The author’s exact words were that the ARR definition will likely need to change or expand, rather than asserting that all companies must do so.

On the cost side, the author argues that AI spend is currently or will very soon become the single largest expense for a company. He suggests breaking it down for the board into two categories: the COGS portion affecting gross margins, and the Opex portion dispersed across Sales & Marketing, R&D, and G&A. He mentions that R&D is typically the largest category of AI operating expenses, including costs for AI tools, usage volumes, and personnel building AI products; AI costs related to free trials and proof-of-concepts (POCs) can grow rapidly. He also suggests showing total Claude and ChatGPT usage by department as a metric for AI penetration, but emphasizes that this must be immediately followed by an explanation of how much time and resources these usages actually save, rather than just reporting volume.

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These changes point to the author’s core judgment: AI is changing too fast, and strictly adhering to a single annual plan for 12 months may no longer be realistic. He believes every company must be willing to completely tear up the plan when underlying assumptions are broken. Meanwhile, the plan should set guardrails—how costs should be adjusted if actual performance deviates from the plan—rather than blindly executing it. He acknowledges that every financial plan is wrong on day one; the difference lies only in how wrong it is.

The author also mentions several operational details supporting this judgment. Board members sit on multiple boards simultaneously and do not live inside the financial data like the executive team does; therefore, reporting should start with goals, first stating what is to be achieved over the next 12 to 18 months, and then describing tailwinds and headwinds. Boards truly care about the handful of key assumptions driving more than 80% of the model, not hundreds of minor ones. In headcount planning, if more than 70% of hiring is concentrated in the first half of the year, the board will get nervous—because if sales underperform, the company’s ability to adjust cash burn is very limited. These details indicate that when AI simultaneously changes revenue definitions and cost structures, the board needs to understand not a static number, but a set of assumptions that may become invalid within the year.

It should be noted that the author did not disclose his company’s specific size, industry, or AI revenue share. The premise of his described practices is that AI product revenue already constitutes a significant part of the company’s revenue structure, and AI spend is large enough to require separate breakdowns for the board. He himself mentioned that this calendar generally applies to larger, more predictable companies; smaller companies or those with lower predictability may need to compress timelines or revise multiple times. For traditional enterprises where AI investment is still in the experimental stage and contributes minimally to revenue, the applicability of these recommendations needs to be discounted.

The author also mentions an operational aspect: topics such as AI gross margins, total AI spend, and where funds are directed can all trigger strong disagreements on the board. He describes his own practice: if there have been past disputes or potential sensitivities in the plan, he communicates separately with board members before the formal meeting to gauge their positions; however, he explicitly states he does not always do this. This is not a universal recommendation, but a conditional practice based on the author’s personal experience.

Sources

  • onlycfo.io↗
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