🌱 AI RoI and Adoption Measures

In early 2026, the proven ROI of AI implementation remains elusive. In a survey cited by Conflicting signals: AI investments vs. ROI doubts, 74% of CIOs say they regret at least one major AI vendor or platform decision in the last 18 months, and 29% have been repeatedly asked to justifiy AI outcomes that they cannot fully explain.

The article advises focusing on AI implement to solve problems the organisation already faces, not to pursue abstract benefits that cannot be clearly defined.

What measures can be used for RoI of AI? The article Why Utah Uses More AI Than California? makes some attempt (unsuccessfully, I would argue) to identify some ways of measuring it:

Enterprise ROI
– Claim: 95% of organisations see zero measurable ROI from GenAI despite $30–40B investment.
– Source: MIT report, The GenAI Divide: State of AI in Business 2025.

Consumer monetisation
– Claim: Only ~3% of consumer AI users pay; for ChatGPT specifically 2–5%.
– Source: OpenAI usage data (Sept 2025) and Menlo Ventures survey (2025).

Work v personal use
– Claim: ChatGPT usage has moved from ~50/50 work–personal in June 2024 to ~70% personal in Sept 2025.
– Source: OpenAI usage report (2025).

Augmentation vs automation
– Claim: Wealthier countries lean toward augmentation over automation.
– Source: Anthropic AI Usage Index (2025).
– Interpretation (author’s): this shows “delegation failed.”

"Productive use" vs “comfort use”
– Claim: Growth area is “expressing” (emotional/sounding board use) vs “asking” or “doing.”
– Source: OpenAI usage report (2025).
– Interpretation (author’s): this is non-productive use.

My thoughts on this article:

Blurring consumer and enterprise evidence
– The article collapses two distinct issues: enterprise ROI (MIT) and consumer willingness-to-pay (OpenAI, Menlo). They are different dynamics and should be analysed separately.

Misattributed motivation
– Author wrongly applies organisational motivation (economic output) to individuals. Employees adopt AI for ease of use, relief from effort, or job interest; organisations adopt AI for measurable output gains.

Expressing as productivity
– Article dismisses expressive use (e.g. "I am feeling overwhelmed with my work tasks, help me order my thoughts") as “comfort use” rather than "productive use". I would argue that augmented cognition (clarifying thinking, reducing overwhelm) is a legitimate productivity gain, though not yet captured in ROI frameworks.

Narrative vs data
– The “delegation failed” line is not from Anthropic, but the author’s gloss. Other explanations exist (labour costs, skills, incentives in different economies).

Lack of a framework
– None of the sources provide a coherent framework for measuring AI ROI. The article inherits this weakness, critiquing without offering structured alternatives.