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A Forbes Tech Council column by Farsight AI's Samir Dutta argues that raw model intelligence is becoming a commodity, and asks enterprise buyers to judge AI on the share of its output that reaches the final deliverable.
Reality
INVID's Alberto Lugo argues in Forbes that developers will be judged on how accurately they solve problems, and his case rests on a Gartner adoption forecast and a McKinsey productivity finding, both cited secondhand.
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Fraud models learn from the transactions they approve and almost nothing from the ones they turn away, and Visa's April cut to its acquirer monitoring threshold raises the payoff for declining more often.
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A Forbes Tech Council column argues that an institution's control over what its agents may do decides sovereignty, and the European rules it cites were written for cloud vendors.
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Claudeforce lets a seller work the pipeline without opening Salesforce. Vivek Acharya argues in Forbes that the packaging unit is now the skill, and that per-seat licensing stops cohering once agents are the users.
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A governance, risk and compliance executive at Manulife argues that assessments taken at intervals cannot describe systems that change between them. The evidence he offers is his own experience with enterprise projects and one cited research paper.
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Greg Ombach, a senior vice president at Airbus, writes that vision-language-action models force a choice between capability and edge deployment, and that industrial buyers should validate one operating domain before expanding.
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When software starts creating accounts and calling applications on a person's behalf, the identity system has to name it. Elena Koryakina wrote that this means task-scoped permissions and an audit trail.
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Son Nguyen says his team ran the same prompt and the same use case through different models, found the difference surprisingly small, and rebuilt the preprocessing, context and validation around the model instead. He calls that surrounding system the agent harness.
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Rob Versaw of Dynatrace argues that capable product managers look weak inside systems they did not choose. The Pendo estimate behind the argument is an industry-scale figure. It sizes the category; it cannot audit one company.
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Daniel Jabaraj's Forbes column pairs Gartner's abandonment forecast with MIT's zero-return finding to argue that AI programs fail on business framing. One study counts projects and the other counts companies.
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The Gartner numbers behind the argument count how many candidates use generative AI. The one-in-four-fake-profiles figure is a forecast for 2028, and the wasted senior-engineer hours arrive without a count.
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Jay Hawkinson, writing for the Forbes Tech Council, says his pre-deal AI governance reviews keep turning up models with no named decision owner, and he puts figures on what changed once companies assigned one.
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A distinguished analyst at Info-Tech Research Group argues that choosing work is the real bottleneck in IT portfolios. The same argument admits approval creates no capacity, so a person still has to pause funded projects.
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Richard Clough says enterprise data rules were written on the assumption that a human makes the final call, and the survey number he brings to size the problem counts people who let AI shop and bank for them.
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Christophe Kolb argues that the first memo and the first reconciliation shipped work and taught judgment at once. The payroll number he cites measures hiring among 22- to 25-year-olds, and nothing more.
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A software CEO's case that AI multiplies misalignment is thin on measurement and sound on mechanism. Token leaderboards and unreviewed agent objectives both pay for activity. The pilot figure he cites is the bill.
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Deloitte's figures, as cited by a Persistent Systems executive, compare a two-year expectation with a capability held today, which makes the gap less a verdict on neglect than a measure of the governance build now due.
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Chris Burchett of Blue Yonder argues in Forbes that AI projects fail on strategy rather than technology, a diagnosis that would move the fix from the data science team to the executive suite. What he offers is experience, not measurement.
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David Talby of John Snow Labs reports 98.6% recall against hand-labeled ground truth and no measurable demographic gap across 24 subgroups. The available account stops before the three-month red team reports back.
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