56% of CEOs worldwide say they've seen no significant financial benefit from AI so far
Only 12% of the CEOs PwC asked report both lower costs and higher revenue from AI, and they are two to three times as likely to say they embed it extensively.
News and insights
Getting AI right, with your people
Research, practical guides and product news on AI governance and adoption. Written for the people who have to make it work.
October 2026
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What's new in The GAiGE 2.0
With Standard, you get AI governance, a page for everyone and three pulses. With Pro, you also get what each tool is worth and what to do next.
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Why version 2 of The GAiGE starts with AI governance
Most mid-sized organisations told us their AI governance came first, and Australia's corporate regulator says directors' duties already extend to AI.
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Australia's AI Value Gap Is a Measurement Problem First
More than a third of Australian firms are spending on AI without proof it works, and the firms pulling ahead share one habit: they measure continuously.
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Most firms still cannot prove their AI is paying off
In McKinsey's 2026 survey, 80% of respondents say AI improved their own productivity, but only 37% attribute any earnings impact to it.
September 2026
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Depth, not breadth: what the new PYMNTS data tells you to measure
Firms with AI embedded in three or more functions use it on no more tasks than firms with one or two, yet 93% report returns against 55% (PYMNTS).
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The enterprise AI ROI gap: why felt productivity is not proof
In ModelOp's 2026 survey of 100 senior AI leaders, more than two thirds of enterprises still judge AI's return on estimates, not measured financial results.
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Money wasted on AI is climbing because almost no one is measuring it
In Flexera's 2026 survey, only 31% of organisations had accurate visibility into their AI software, and 59% said money wasted on AI had grown.
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Organisations estimate a quarter of their AI costs are wasted, and nobody owns the bill
Harness found organisations estimate about 26% of their AI costs are wasted, and only 13% have basic visibility into what they spend on AI.
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Firms tripled what they spend on AI. Most still can't prove it worked.
Enterprise spending on generative AI tripled to about US$37 billion in 2025, yet fewer than four in ten organisations can attribute any financial impact to it.
August 2026
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57% of senior AI leaders say their AI returns still don't outpace the costs: what to measure instead
Senior AI leaders report two years of flat returns while the models keep improving. Where the gap sits, between the models and everyday work, and how to measure it.
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Only 6% of leaders have a framework that tracks AI returns consistently: what one looks like
Battery Ventures found 62% of enterprises already report AI ROI to the board, yet only 53% can point to a clear return.
July 2026
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Measure what AI earns, not just how much it's used
In Writer's 2026 study, 59% of organisations spend US$1 million or more a year on AI, and 29% report significant returns.
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Rising AI spending, elusive returns: what KPMG's data says about measurement
In KPMG's data, leaders who can see their AI costs clearly are five times as likely to report established ROI, 15% against 3%. About a third can.
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The trust deficit is the missing layer beneath every AI ROI dashboard
AI tools can give different answers to the same question, and leaders who expect ordinary software are often disappointed by the returns. Why trust in the output belongs beside every ROI figure.
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Microsoft is spending US$190 billion on AI infrastructure and cutting jobs: what to measure in your own AI costs
The job cuts came alongside a 30% fall in Microsoft's share price, about US$1.2 trillion of market value in nine months, as spending outran visible return.
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Microsoft's US$2.5 billion bet on deployment, and how to see whether yours pays off
Microsoft is embedding 6,000 employees with its customers to deploy AI, two days after Amazon put US$1 billion behind the same idea.
June 2026
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What's new: redesigned reports, three sharper pulses, and Microsoft Edge
Every report now leads with at-a-glance gauges tailored to what each pulse measures, the three core pulses each have a clearer job, every pulse has its own colour, and the extension is now on Microsoft Edge.
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What's new: editorial refresh, sample reports, PDF downloads, and more
A roundup of what shipped in the weeks before 1 June 2026: a design refresh, a public sample report, downloadable PDF reports and a cleaner sign-up.
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Why AI vendor impact surveys can't replace independent measurement
OpenAI is shipping Impact Surveys in ChatGPT, and other vendors may follow. What a vendor's own survey answers well, and what a view across all your tools adds.
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How to compare AI tools like-for-like: a practical framework
Every AI vendor measures impact its own way, so the numbers don't compare. Here's a five-minute framework to fix that, built around four dimensions any CFO can defend.
May 2026
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What's the best way to measure AI? Five options compared
Annual surveys, vendor dashboards, SaaS spend management tools, consultant audits and short pulses on a regular cadence: five approaches, what each is good for, and how to pick the right combination.
April 2026
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Why we built The GAiGE, and what most companies get wrong about measuring AI ROI
Three years ago a customer asked us how to measure whether their AI tools were working. We didn't have a good answer, but here's how we ended up with one.
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Anonymous or identifiable: what your organisation sees, and where the limit is
Every pulse is anonymous or identifiable, and says which before it starts. When a pulse is anonymous, your name isn't shown with your answers to anyone, admins included, and every figure needs answers from at least five people. Here's what your organisation sees, and why we tell people that a small group or a comment can still point to them.
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Why we normalise every metric to 0-100: the case for health scores over raw numbers
A 3.5× ROI and a 72% satisfaction score are both numbers, but not the same kind of number. Here's why every headline metric in The GAiGE gets mapped onto a common 0-100 scale before it hits a gauge.
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Why asking your team works better than usage data alone for AI ROI
Usage data answers one question completely: who used each tool. Asking your team adds whether it helped. Here's why the two together beat either one alone.
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Seven questions every AI tool vendor should answer before you renew
A practical checklist for your next vendor review: the questions that separate "the team loves it" from "the team is paying for it and quietly ignoring it."
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The 2.5× rule: how we keep ROI estimates in line with the evidence
How The GAiGE calculates ROI defensibly: the 2.5× extrapolation cap, the use-your-own hourly rate, aggregation thresholds, and where you should push back on our methodology.
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