Audience, decisions, and rollout for the state of AI-assisted data visualization

Status: audience and distribution strategy, 16 August 2026.

Evidence cut: 2026-08-16. Community routes and event dates should be checked again immediately before any outreach. This document recommends a rollout; it does not authorize publication, community posting, submissions, or direct outreach.

Companions: editorial architecture, executive summary, research field guide, and practitioner and reader experience.

Recommendation

Do not launch this as one large report for one imagined audience. Launch one canonical evidence hub with several decision-shaped entry points.

The underlying research has at least six credible reader groups. They share an interest in AI and visualization but arrive at different moments, use different language, and need to make different decisions. The comprehensive report is the evidence spine. The useful public units are shorter guides, scorecards, figures, methods, and source tables that help one group decide something now.

The first four audiences should drive the package:

1. visualization and data practitioners deciding where AI belongs in a real workflow;
2. BI and analytics leaders deciding what to enable, buy, govern, or stop;
3. visualization, HCI, and AI researchers choosing studies, benchmarks, and claims; and
4. data journalists, graphics editors, and newsroom developers deciding what is safe to use and publish.

Product builders and educators are important secondary audiences. General executives and broad AI readers should receive a concise brief, not set the structure of the research.

The positioning should be literal:

What AI chart and dashboard systems can do in 2026, where they fail, and how to evaluate them for real work.

Use AI-assisted data visualization as the umbrella. Use the audience’s own more specific terms in route titles and descriptions: AI data visualization tools, Power BI Copilot, Tableau Agent, conversational analytics, agentic analytics, agentic visualization, LLM visualization generation, coding agents for data analysis, semantic models, and reader trust. Do not force one vocabulary across every surface.

What is observed, inferred, and still unknown

This strategy combines three evidence classes:

• Observed: the current report files and rendered artifacts; official descriptions of community access, editorial terms, conference programs, search-measurement and citation mechanics; and bounded public language samples. No site-specific query or route-use data entered this synthesis.
• Inferred: audience priority, decision moments, suitable artifacts, and the sequence in which those artifacts should travel through the observed routes.
• Unknown until tested: how many people will read, refer, cite, subscribe, invite, or change a decision because of this work.

The audience map is therefore a testable model, not an adoption claim. Community membership counts do not establish interest in this report. A visible page does not establish use. A citation does not establish a changed decision.

The source trail for the audience scan is preserved in a private evidence ledger, while this public-facing strategy links to sources that can be checked by a reader. No private captures or copyrighted source text should travel into the public package.

The actual product

The phrase “report rollout” can hide the wrong unit of work. A reader rarely has the job “read a state-of-the-field report.” They have a question such as:

• Can this tool be trusted with our semantic model?
• Which parts of my workflow should I delegate, and which must remain mine?
• What evidence would show that an agent made the work better rather than merely faster?
• Which benchmark or study would close a real gap?
• What can a newsroom permit without weakening source, editorial, or reader accountability?
• What should a student still learn when a model can produce the first chart?

The public offering is therefore a family of evidence-backed decision guides built from one maintained evidence base. Internally, decision products is a clearer name for this family than “automated consulting.” “Automated consulting” foregrounds the production mechanism and invites a comparison with consulting labor. “Decision product” names the object and its use. For public language, “evidence-backed decision guide” or “maintained decision guide” is more legible and less grandiose.

One validity result, seven audience questions

The latest configuration audit has one canonical answer: no benchmark result or prepared protocol currently authorizes a Vizier configuration winner. The translation changes the decision language, not the truth:

Every shorter layer should point to the complete threat audit and carry the evidence date and reopen condition. This is audience translation, not observed audience demand or proof that any route has helped a reader.

One system-anatomy result, seven audience questions

The complete field-guide account has one canonical answer: six of six audited authoring or analysis systems separate generation, execution, and some critique route; four expose meaningful human control; zero reaches accepted delivery, intended-reader outcome, or maintenance. The translation changes the next decision, not those counts:

Every layer points to the same eleven-stage account and keeps critic authority, acceptance, delivery, reader evidence, and maintenance separate. This remains a modeled route design, not observed audience use.

One acquisition result, seven audience questions

The complete field-guide account has one canonical answer: twelve named skill listings carry public install signals, but zero held row exposes real-task invocation, repeat use, organizational acceptance, or outcome. The twelve rows collapse to ten parent repositories and eleven documented lineages. The translation changes the next decision, not those counts:

Every layer points back to the same provenance-aware answer. These are intended routes, not evidence that any audience has used or accepted the layer.

One governance result, seven audience questions

The complete practitioner account has one canonical answer: three provider families expose meaningful controls, two provider stories report named-feature organizational use, two studies add adjacent governance-process evidence, and zero of seven held rows joins a complete governed deployment. The translation changes what each audience does next, not those counts:

Every layer should link back to the same nine receipts and retain the evidence cut. This is a designed route for identified audiences, not evidence that those audiences have yet used or accepted it.

One capability-to-practice result, seven audience questions

The complete practitioner account has one canonical answer: three of ten audited primary cases form controlled partial bridges from technical evidence to human use, correction, or reader harm; zero reaches exact-version accepted delivery or a complete episode. HAIChart is the broader reinforcement-learning recommender case, interactive task decomposition supplies the correction case, and ChartAttack supplies the reader-harm case. The translation changes the next decision, not those counts:

Every layer should link back to the comprehensive answer and retain its reopen condition: carry the same artifacts, tasks, configuration, and version through a frozen acceptance rule, real delivery, representative use, a later event, and whole cost. This is translation for intended audiences, not evidence that an audience has used or accepted the public layer.

One reader-outcome result, seven audience questions

The canonical answer is: one of ten held rows directly carries selected AI-generated charts into an independent controlled-reader effect; five rows measure adjacent human outcomes under different AI roles; zero reaches exact accepted delivery and later same-lineage reader recheck. The translation must name the AI role before naming the outcome:

These are audience-facing decisions layered over one verified evidence ledger, not seven new interpretations of the underlying result.

One development-decision and recheck result, seven audience questions

The canonical answer is: Lexara adds one two-week longitudinal development- decision near-miss; adding the later same-feature InfoViP operational line brings the audit to 15 named reader, production, and evaluation cases, while zero reaches the complete exact-artifact decision/recheck chain. Six CVA developers ran 38 experiments over 57 uniquely authored cases, ten models, and six prompts using their own data and prompts. The study records real model and prompt selections, rationale, disagreements, and confidence. It does not bind an immutable study build and exact configuration to accepted downstream delivery, a consequential audience decision or calibrated trust, whole cost, and later post-release same-lineage recheck:

Every layer leads back to the same twelve receipts. The intended route changes the decision product, never the zero or the distinction between development selection, contract acceptance, audience consequence, confidence calibration, and post-release return.

One artifact-custody result, seven audience questions

The canonical answer is: a bounded title-cue screen over A208’s 122 authoritative stable-key studies surfaces one explicit GPT signal, one pin-able supplemental-output bundle, and zero audience-delivery or later-recheck chains. Beyond Generating Code evaluates 91 quiz questions and nine homework assignments. Its one-commit public supplement preserves prompts, results, generated code, and grader-ready bundles across 1,271 blobs. It does not preserve an immutable provider model snapshot or complete configuration, expose a versioned release or later commit, directly evaluate the final project, or deliver artifacts to the government, UN, or primary-school audiences named in prompts:

Every route begins from the same comprehensive answer and changes only the decision layer. The first valid upgrade is one frozen artifact and configuration carried through acceptance, intended-audience delivery, measured outcome, a material later event, and a same-lineage recheck. Designed translations are still not evidence that any audience has used them.

One mature-platform result, seven audience questions

The canonical answer is: exact-DOI discovery covers 114 authoritative A208 keys and 87 available abstracts; it adds no hidden GenAI candidate, but it surfaces one mature non-AI visualization afterlife and zero complete AI- assisted lifecycles. AIDSVu reports ten years of public delivery, 501,527 unique users in 2019, named planning applications, recurring governance, and explicit maintenance limits. An official 2026 data release and current FAQ add a later event, annual-update contract, and careful unavailable/unreleased/suppressed/lagged/estimated/ corrected data states. They do not add an AI visualization role, immutable build, version-bound measured audience outcome, or affected-audience recheck:

Every route keeps the mature platform receipts because they are genuinely useful. Every route also keeps the missing AI and audience-outcome joins. The translation changes the next decision product, not the evidence class.

One primary-evidence result, seven audience questions

The canonical answer is: five exact full texts recovered below the 27 no-abstract DOI rows add four empirical human-evidence cases, but no accepted deployment. B17 and B128 measure controlled tasks; B120 records HHS co-design, a demo, and bounded use; B90 records an enterprise demonstrator and production intent. None joins accepted field release to later affected-audience recheck. All five explicit GenAI cue screens are empty. That pass left 22 DOI rows unassessed; the subsequent high- signal recovery adds eight abstract or official-project surfaces and leaves 14 DOI rows without substantive primary content:

The translation changes the next product—study card, approval gate, methods register, publication language, release ledger, access protocol, or executive answer—while preserving one sentence: co-design, a demo, a human task study, and production intent are four different receipts—not deployment.

One field-use result, seven audience questions

The canonical answer is: the eight stable non-DOI keys yield three DOI repairs, one non-AI embedded-use comparator, and zero complete AI lifecycles. The cancer diary reports increasing system-log use; an independent systematic review recovers an 11-clinician QUIS median of 4.38. Four full texts, two primary abstracts, one issue excerpt, and one gated primary text remain distinct:

Every route begins from the same repaired authority ledger and preserves the same missing receipts. The useful translation changes; the evidence does not.

One prototype-evaluation result, seven audience questions

The canonical answer is: nine high-signal DOI rows yield eight substantive primary abstract or official-project surfaces, seven explicit human evaluations, one production-intent near-miss, and zero complete lifecycles. InfoViP is the nearest delivery case: seven FDA safety evaluators supplied requirements and evaluated the prototype, suggestions were addressed, and an official FDA page says an enhanced NLP and unsupervised-learning version will be installed in production. Installation, acceptance, routine use, regulatory outcome, later change, and evaluator return are not confirmed. Fourteen DOI rows still lack substantive primary content:

Every route preserves the same grammatical and lifecycle boundary. The translation changes the decision product; it does not turn will be installed into was deployed.

One residual-recovery result, seven audience questions

The historical answer at that evidence cut was: all 14 residual DOI rows were investigated; eleven gained substantive primary content in that pass, including one full chapter, and three were then metadata-only. The strongest additions were evaluation-to-redesign and practice context, not AI deployment. The full chapter connects three UX experts and 25 problems to a third design version. The corridor case and university- network case involve stakeholders and managers but expose no accepted build, ordinary-use denominator, consequential outcome, or later return:

Every layer derives from the same comprehensive answer. The translation changes vocabulary, depth, and decision utility—not the evidence state.

One public-delivery afterlife result, seven audience questions

The historical answer at that pass was: B110 had gained a substantive official abstract, immutable author-linked framework and application source, and a same-named DiscoverWater interface reachable at KU in August 2026. B92 and B91 then remained content-unassessed. This was a bounded non-AI public-delivery afterlife, not an exact-build use or outcome lifecycle. The official abstract names stakeholders, policy makers, scientists, educators, resource managers, and users. The pinned framework, pinned DiscoverWater application, and live interface preserve different receipt stages:

Every route starts from one maintained evidence state. The live check earns the word reachable; it does not earn continuously available, used, accepted, effective, or rechecked.

One source-lineage answer, seven audience decisions

The canonical answer is: the dated live DiscoverWater page is not reproduced by either held public framework state, while selected data assets establish partial shared lineage. One of three sampled pairs matches bytes, two match canonically, and one differs materially. No exact build or audience outcome is held. The source audit keeps a useful partial answer without promoting a repository link, prototype demonstration, analytics hook, or intended audience into deployment or use:

These are translations of one comprehensive answer. They change vocabulary and decision utility, never the evidence state.

One publisher-abstract recovery and one stop rule, seven audience decisions

The current canonical answer is: B92 now has an exact publisher abstract describing a multicriteria hydrogen-pipeline risk model, Monte Carlo simulation, Kendall’s tau rank comparison, and graphs for ranking sections and targeting mitigation. It names no human sample or AI visualization role and supplies no release, delivery, use, outcome, recheck, or whole cost. B91 remains content-unassessed, while generic retrieval over six named surfaces is paused after three bounded lawful passes. The 27-row ledger stays 6 full / 20 primary abstract or official / 1 unassessed / 0 complete AI lifecycles; the separate access state is 1 access-blocked and unassessed / 0 active generic B91 targets. These are abstract-bounded findings because the full B92 paper is not held:

Every route derives from the same comprehensive answer. Translation changes depth, vocabulary, and next action; it does not add participants, delivery, or outcomes that the source does not report.

One field-use, cost, and governance answer, seven audience decisions

The canonical answer is: A deduplication component of FDA’s InfoViP was used by 20 unique reviewers in a six-month internal real-work evaluation and is later reported operating across 29 million historic plus daily incoming FAERS reports; three related FDA awards total $2.40 million obligated and $2.86 million estimated, while the current inventory still records implementation N/A and no associated AI-system ATO. Admit one counted field-evaluation and operating-pipeline near-miss with a bounded award envelope; do not convert it into accepted whole-platform deployment, current routine use, impact, whole cost, ROI, or a complete episode:

These are seven decision layers over one record. None may erase the field-use denominator, performance variation, award-envelope boundary, or current governance counterevidence.

One production-component and QA answer, seven audience decisions

The canonical answer is: A final 2025 CIOMS use-case report says FDA’s InfoViP deduplication component was approved for historical and live processing, installed in AWS, integrated with AERS, and processing more than 30 million historical plus about 8,000 daily submissions; it also says a solid QA plan was not yet in place, routine-use roles were incomplete, and downstream signal effects had not been investigated, while the current federal inventory still lists implementation N/A and no AI-system ATO. A 2026 FDA fellowship record proposes Elsa GenAI API and UI work; it is a change trigger, not an observed release.

One date-and-relationship repair now travels through every route. An FDA page that appears 2026-dated in search explicitly says its content is current to 3 April 2024, so its future-tense InfoViP language predates the final report. A May 2026 biography names Oanh Dang as serving project lead without assigning operations or maintenance authority. FDA’s Elsa 4.0/HALO release does not name InfoViP, so the application-specific integration remains unobserved. For BI and sponsors, the resulting decision rule is: require the unit, event date, exact role, and named application join before accepting a broader platform or GenAI status claim.

The accountability translation is equally literal. The live InfoViP–Elsa opportunity is now closed and names Joshua Xu, Leihong Wu, and Oanh Dang as research mentors. Their official profiles support R2R/advanced-AI integration, bioinformatics research, and InfoViP project leadership; none names an InfoViP operator, maintainer, QA executor, release approver, or authorization authority. A fellow would be a nonemployee barred from inherently governmental functions. The useful BI layer is therefore three research contacts, zero named operating authorities—not “the closed fellowship shipped.”

Every layer points back to the same full account. None may turn a same-project production statement into independent audit, the current inventory conflict into a resolved authorization, or future-tense fellowship aims into deployment.

One review-lineage result, seven audience questions

The canonical answer is: a 2025 systematic review maps 127 studies of data visualization in broadly defined AI-assisted decision-making, but it does not construct the denominator for complete same-artifact GenAI lifecycles. It retains 118 empirical papers and nine reviews; it explicitly does not focus on GenAI, leaves GenAI trust and interpretability for future investigation, and calls longitudinal decision-behavior evidence future work. The qualifying count is unknown, not zero of 127. Its publisher supplement also separates reported count from auditable identity: six domain totals sum to 127, while the “full list” names 126 citations and leaves one Education entry as a literal ?. A source-internal repair now identifies that row as Hernández-Calderón et al. (2023): A208 says the domains were assigned after selection, and this is the sole one of its five Education examples absent from the twelve named entries. The publisher file remains unchanged. The complete six-domain crosswalk now maps 122 of the 127 supplement positions to distinct A208 review keys. Five supplement-only labels have exact-looking external DOI candidates—Jiao (2022), Burt et al. (2017), Theis et al. (2018), M. Lu (2020), and Islam et al. (2022)—but no review-controlled artifact joins them. One admitted key also carries a printed PMID that resolves to another paper. The frame is 122 authoritative keys, five authority gaps, and one quarantined identifier conflict. Lifecycle screening remains unstarted and the count remains null:

Each layer leads back to the same review and eight-field audit. The vocabulary and decision change; the null denominator and reopen conditions do not.

One accepted-cost result, seven audience questions

The cost audit has one canonical answer: zero of twelve held fragments reports both equivalent observed per-arm route-wide cost and a frozen accepted- artifact denominator. The peer-reviewed Selective TTS visual-insights study is the strongest fixed-budget near miss: it matches declared LLM calls and, separately, output tokens inside one pipeline. Its released accounting omits repair and failed- worker cost, and its final reports are proxy-judge-scored candidates rather than contract-accepted artifacts. Keep three receipts separate: matched declared partial budget, observed route-wide use, and cost per accepted artifact. The translation changes the decision language, not the evidence:

Every layer should retain rejected, abandoned, quarantined, and no-output work in the numerator; count only artifacts that passed the frozen contract; and report eligible, attempted, candidate, accepted, delivered, and reader- successful ratios separately. This is a protocol result, not evidence that either route is cheaper.

One retry-topology result, seven audience questions

The complete specialist account has one canonical answer: over 888 tasks, VisCoder2-32B and GPT-4.1 both finish at 732 execution passes after 584 versus 714 conditional revisions; the held records do not support a cost winner. The endpoint, attempt topology, resource receipt, and accepted-work denominator remain separate. The translation changes the next action, not the evidence:

These are intended decision layers over one custody record. They are not evidence that any audience has used, preferred, or accepted the translation.

One longitudinal co-design result, seven audience questions

The Graphy audit has one canonical answer: the same three blind co-designers completed 12 sessions across four workshops and eight months while selected interaction changes were implemented, but no workshop-version binding, formal efficacy result, versioned release, or representative release recheck is in custody. The public repository has seven commits and no tags or releases. The translation changes the next decision, not the evidence class:

Every layer should point back to the comprehensive practitioner answer and keep the same reopen condition: bind a workshop to immutable code and model state, or formally recheck a versioned release with representative users after a material change. This is translation for intended audiences, not evidence that any audience has used the layer.

One six-receipt null, seven audience questions

The exact-join audit has one canonical answer: zero of seven leading cases in the named surfaces through 15 August 2026 joins repeated representative use, immutable tested build, exact exposure model, versioned same-lineage release, later material event, and representative or actor-separated post-change recheck. Every receipt appears somewhere, but none stays attached to one artifact and user lineage. The audience layer changes the next action, not the zero:

The strongest next routes are specific: bind Graphy workshops to code and model state; identify MAIDR’s participant-tested version and bring users back after maintenance; obtain the Prism affected-operator recheck; or carry the OpenClaw proposal through merge, corrected release, and reporter reconciliation. These are research targets, not evidence that an audience has adopted the public layer.

One production-recovery result, seven audience questions

The production audit has one canonical answer: three held AI-assisted dashboard afterlives, two maintainer-verified restorations, zero affected-actor recoveries, zero transferred maintenance authority, and zero complete twelve- state rows. Its full public account keeps issue closure, maintainer recheck, contribution, and authority separate. The translation changes the audience’s decision, not the counts:

The first valid upgrades are also audience-specific: a return-user receipt on the corrected route, or a named second person exercising release or incident authority. Another project-level AI disclosure, pull-request count, issue close, or maintainer-only demo does not move either missing state.

One lifecycle result, seven audience questions

The latest practitioner audit has one canonical answer: four promising public joins still produce zero new same-artifact lifecycle episodes. MAIDR now contributes a dedicated legacy AI-study surface, a v2.10.0 version floor, abstract-level findings for eight blind and low-vision participants, and real later maintenance, but no tested-build receipt or representative recheck. Its current TypeScript rewrite is a separate validation target. A public- health copilot paper describes a 16-person human method but says the experiment was removed and reports no human result. The translation changes what each audience does next:

Every layer should point back to the comprehensive public answer, preserve the bounded-search language, and carry the exact-version reopen condition. “Zero admitted” does not mean “no case exists anywhere,” and a translation is not evidence that an audience has used it.

Priority audience map

Priority reflects fit with the current evidence, reachability through observed routes, and likelihood of a concrete decision. It is not a market-size estimate.

Audiences are not amplifiers

Editors, community hosts, newsletter writers, podcast hosts, conference organizers, instructors, and vendors may carry the work to readers. They are not automatically the people whose decision the work should serve. Each outreach pitch should distinguish:

• reader: who uses the material;
• decision: what they can do differently;
• carrier: who already has permission to reach them; and
• proof object: the figure, result, protocol, or dataset that makes carrying it worthwhile.

This prevents “distribution” from becoming a list of places to paste a link.

Audience dossiers

1. Visualization and data practitioners

Who this includes. Independent visualization designers, data analysts, analytics engineers who also communicate results, BI authors, newsroom graphics practitioners, and developers who build custom visual interfaces. They vary in code fluency, but all own some part of the path from question to delivered artifact.

Decision moments. The blank page; a request outside the creator’s normal stack; a recurring transformation; a generated chart that is almost right; a repair that breaks something else; a handoff or maintenance obligation.

Jobs. Get to a useful first representation, understand unfamiliar code or data, compare alternatives, make precise corrections, prove the numbers and interactions, deliver in the real environment, and preserve a record another person can maintain.

Questions in their language. “Can it make the chart I mean?” “Can I edit the result?” “Where did that number come from?” “Does it work with live data?” “How many prompts will this take?” “What did it take to make this route usable?” “What did we plan, and what did each task actually use?” “Which phase made it slow or expensive, and was the route cold or warm?” “Was the affected route independently rechecked after the repair?” “Which parts still need me?” “Will I spend the saved time checking it?”

What to give them. A surface-by-job map; the possible/accomplished/experienced threshold; a checklist for data meaning, transformation, interaction, accessibility, and handoff; a clock that begins with route preparation and ends at accepted delivery; a planned budget beside the observed resource receipt; a phase receipt that separates cold start, discovery, planning, tool/result transfer, context update, and synthesis; a two-receipt accepted-cost card that keeps every attempt and failure in the route numerator and only contract-passing artifacts in the denominator; a five-receipt incident chain from report through independent recheck; and the four-state handoff ladder—accepted change, repeat contribution, maintenance authority, independent recovery; and worked examples where assistance both helped and failed. Lead with the job, not model names.

Where they gather. The Data Visualization Society describes its community as a place for practitioners and enthusiasts to connect and offers Slack, events, and interest or local groups through membership. Its 2024 State of the Industry survey was disseminated through email, Nightingale, Slack, LinkedIn, X, and local meetups, showing a real multi-route field network rather than a single feed. The survey had 980 starts and 763 completions; the existing practitioner report uses the 825 respondents who answered its AI question and keeps that denominator visible. DVS membership, DVS State of the Industry 2024.

Access strategy. Earn editorial treatment first. Nightingale accepts original member submissions in a roughly 500–3,000 word range and describes promotion through the DVS newsletter, Slack, and LinkedIn. Pitch a self-contained finding or practical protocol, not a summary of everything. Respect its exclusivity terms and do not duplicate the submitted essay during the stated window. Nightingale submission guidance.

2. BI and analytics leaders

Who this includes. Heads of analytics, BI platform owners, analytics engineering leaders, data governance leads, report developers, and technically engaged business sponsors. Authors, platform owners, and budget owners are not the same persona; the asset should make their distinct responsibilities visible.

Decision moments. A vendor adds AI to an existing license; leadership asks for a conversational interface; a pilot reaches production data; an assistant returns one wrong executive number; or an agent appears capable of rebuilding a dashboard end to end.

Jobs. Separate demo capability from acceptable organizational behavior; determine whether semantic models and metadata are ready; choose pilot scope; define refusal, review, logging, escalation, and actor-separated recovery; and estimate total human and machine cost.

Questions in their language. “Does it respect the semantic model?” “Can it use certified metrics?” “What can it read beyond the visible report?” “Which permission and capacity state is actually in force?” “Can we reproduce and correct the wrong answer?” “Who owns it?” “What is fixed, periodic, and per attempt—and over what volume?” “Is this conversational analytics or report authoring?” “Who verified the corrected release?” “Do planned and observed resources reconcile?” “Do we still need dashboards?” “How do we govern agents using MCP?” “Which host, client, model, cache, streaming, concurrency, and retry state produced this total?”

What to give them. A two-page decision brief, a pilot scorecard, a semantic- readiness checklist, an incident scenario, and an eleven-lane total-cost ledger. Keep preparation, ownership, marginal attempts, failures, and downstream delivery separate; declare volume, useful life, and amortization; and recompute every claimed denominator before procurement. Put the planned cap beside observed calls and retries, tokens or image units, evaluator and tool work, accelerator use, latency and concurrency, charges, and human minutes. Attach a topology card, phase receipt, and accepted-artifact denominator; keep hidden internal work explicitly missing rather than inferring it from the user-visible response. The report’s central distinction between possible output and accepted work is more useful than a tool ranking.

Context-envelope and correction receipt. Before a pilot result counts, declare and retain five controls:

Two captured Fabric Community sources motivate these fields. One unresolved practitioner issue reports model-grounded responses under a specific shared- capacity, View-only/no-Build setup; it is not a confirmed bug or security finding. One Microsoft-authored customer account links incorrect or inconsistent answers to model structure, filter context, inclusion, naming and synonyms, then reports repairs without disclosing its test denominator. These are source- wording and preparation-pattern evidence, not a platform verdict or efficacy comparison. Fabric permission-scope issue, Microsoft-authored implementation account.

Where they gather. The official Tableau community exposes user groups, forums, Tableau Public, and Slack; its Slack page names channels including AI-specific discussion and visualization feedback. An official AI and Tableau user group runs recurring virtual sessions around best practices and governance. The dbt community offers Slack and local or virtual meetups for analytics practitioners. These are separate communities with different norms, so a useful BI-specific artifact should be discussed in one appropriate venue at a time, not sprayed across all of them. Tableau community Slack, AI and Tableau user group, dbt community.

Access strategy. Recruit pilot reviewers from author, platform-owner, and governance roles before public launch. After revision, offer a concrete session: “How to tell whether an AI dashboard pilot is ready for governed work.” Use one worked scenario and let the scorecard be the handout.

3. Visualization, HCI, and AI researchers

Who this includes. Visualization and visual-analytics researchers, HCI researchers, NLP and agent researchers working on visualization, benchmark and evaluation authors, and graduate students entering the literature.

Decision moments. Scoping a literature review; positioning a paper; choosing a benchmark; deciding whether a product demonstration supports a research claim; or selecting the next study.

Jobs. Find the primary literature, compare unlike evaluation regimes without flattening them, identify contradictions and nulls, reuse taxonomies, and formulate a study whose outcome would change what the field believes.

Questions in their language. “What is the task definition?” “What is the baseline?” “Is this text-to-vis, chart-to-code, visual analytics, or agentic visualization?” “What does the benchmark actually measure?” “Was there a human study?” “Does author success transfer to reader outcome?” “What is the ecological validity?” “Are resources actually equal, or did the arms merely match a round or candidate count?” “Which claim is direct, which is a bridge, and which later evidence lane is still missing?”

What to give them. Stable claim IDs or section anchors, a benchmark crosswalk, machine-readable study cards, a negative-findings table, an explicit gap ledger, methods, an equal-resource receipt, event-level phase traces with effective run manifests and observability state, and a frozen citable release. Researchers should be able to reuse a table or dispute a claim without reverse-engineering a designed page.

Where they gather. The official IEEE VIS 2026 workshop program includes VISxGenAI (“GenAI, Agents, and Future of VIS”), BELIV, AccessViz, EduVis, VISxAI, and related venues. That program is direct evidence that the topic crosses generation, evaluation, accessibility, education, and visual analytics. It does not prove that every attendee wants this report. The 2026 submission deadlines have passed; the responsible current route is discussion with organizers and attendees where invited, followed by a 2027 submission plan. IEEE VIS 2026 workshops.

Access strategy. Release a frozen research edition with citation metadata. Deposit an approved snapshot in a repository that can assign a persistent DOI; Zenodo describes a DOI as a persistent identifier that supports citation and discovery. Add a root CITATION.cff if the public repository is meant to be cited; GitHub then exposes a “Cite this repository” surface and can point to the preferred report citation. These are publication choices and require owner approval. Zenodo DOI guidance, GitHub citation files.

4. Data journalists, graphics editors, and newsroom developers

Who this includes. Reporters who analyze data, graphics and visual editors, news application developers, data editors, investigative teams, and the people who set newsroom policy or training.

Decision moments. An analysis deadline; a coding agent used on source data; a generated graphic proposed for publication; a new newsroom policy; or a need to explain how a result was produced.

Jobs. Accelerate exploration or implementation without surrendering source custody, reproducibility, editorial judgment, disclosure, accessibility, or the reader test. Know which evidence belongs in the published explanation.

Questions in their language. “Can we reproduce it?” “Can an editor inspect the transformation?” “What did the agent invent?” “What needs disclosure?” “Is the source material allowed to leave our environment?” “Will a reader understand the claim on a phone?”

What to give them. A newsroom use protocol, a provenance checklist, a short benchmarking guide, and a worked example that begins with source custody and ends with the delivered reader surface. Keep product promotion out of it.

Where they gather. NICAR 2026 included sessions framed as “Using coding agents for data analysis,” “Build your own AI benchmark,” and practical AI in the newsroom. Those titles are useful language evidence: the audience is looking for bounded methods and tests, not only “AI visualization.” NICAR 2026 has passed; the next formal opportunity should be checked through IRE rather than implied to be open. OpenNews/Source explicitly connects developers, designers, and data analysts working in journalism through code. NICAR 2026, OpenNews/Source community.

Access strategy. Seek two newsroom reviews before packaging the protocol. Then propose a training or conference session whose promise is operational: “A reproducible acceptance test for AI-assisted data analysis and graphics.” The complete report is follow-through evidence, not the session abstract.

5. Product, engineering, and tool teams

Decision moments. Adding an agent to a product, choosing an intermediate representation, deciding between a specialist model and a general agent, interpreting a benchmark win, or responding to a failure that the current eval did not catch.

Jobs. Choose where model reasoning adds value; bind it to authoritative context; expose inspectable state; create recovery paths with named verifier identity; and test the complete delivered artifact rather than a screenshot, successful execution, closed issue, or maintainer-only recheck.

Language. “Agentic workflow,” “MCP,” “tool calling,” “semantic layer,” “structured representation,” “critic,” “browser evaluation,” “repair loop,” “ablation,” “phase trace,” “effective run manifest,” “human-in-the-loop,” and “end-to-end dashboard.”

Assets and route. Give this audience the system anatomy, technique matrix, evaluation ladder, and reproducible ablation protocol. A repository-ready methods page should include topology-bound event identity, cold/warm and cache state, retries, phase timing and usage, charges or accelerator receipts, stop reasons, and explicit missing internal phases. Compact diagrams can travel through GitHub, Observable, and engineering blogs; only original results should be submitted as “news.”

6. Educators, literacy, and accessibility specialists

Decision moments. Revising a curriculum, deciding what assistance is permitted, assessing whether students learned or merely completed a task, or reviewing a generated interface for disabled readers.

Jobs. Separate assisted performance from durable learning; preserve critique and data/domain reasoning; evaluate transfer without the tool; begin access with co-creation rather than automated remediation; let users inspect, challenge, or take an alternative path around generated descriptions; and test the delivered interaction with representative readers and assistive technology.

Language. “Visualization literacy,” “learning outcome,” “unassisted transfer,” “retention,” “cognitive offloading,” “accessibility from the start,” “co-creation,” “verification,” “user agency,” “alternative pathways,” “screen reader,” “low vision,” “mobile,” and “reader comprehension.”

Assets and route. Give this audience the human-skills model, a classroom or training protocol, and an accessibility acceptance sheet that covers participation, multimodality, disclosure, verification, agency, maintenance, and sustained use. VIS workshops such as EduVis and AccessViz provide obvious intellectual homes; DVS education and accessibility groups may provide practice-facing routes. The AccessViz 2025 outcome report treats adoption as a socio-technical process and AI-mediated access as a trust and power question, not merely an alt-text feature. Its participant denominator is not stated, so it supplies design requirements rather than prevalence evidence. AccessViz 2025 outcome report.

Jobs and decisions: the product matrix

The same chapter can support several readers, but the decision artifact should be explicit about who holds authority and what counts as an answer.

A reusable decision-product specification

Every derived asset should carry these fields:

1. For whom: role, environment, existing practice, and authority.
2. At what moment: the observable trigger that makes the material timely.
3. Decision: the actual fork, including a legitimate “do not use AI” branch.
4. Inputs: what local context the reader must supply.
5. Evidence: which report claims and limitations support the guidance.
6. Output: the record the reader leaves with, not merely information read.
7. Acceptance: how to know the decision path worked.
8. Reopen condition: what product, model, benchmark, or field evidence would require revisiting it.

This is the useful inheritance from the adjacent automated-consulting work: one maintained corpus can produce several moment-shaped artifacts, but each artifact must still have a named human user, decision, and acceptance test.

The language map

Use one canonical term and several audience routes

Canonical umbrella: AI-assisted data visualization.

It accurately covers human-led work that uses AI without claiming autonomy. It also keeps research, practice, products, and reader outcomes in one field.

Public subtitle: What AI chart and dashboard systems can do in 2026, where they fail, and how to evaluate them for real work.

“State of” signals comprehensiveness but not the reader’s job. The subtitle and route titles should answer that deficiency.

A dated search and community-language sweep found that exact “AI-assisted data visualization” phrasing is less common in broad tool discovery than “AI data visualization” or product-specific terms. “Agentic visualization” is already useful in research and system-design contexts, while vendors and BI communities use “conversational analytics” and “agentic analytics.” The correct response is an intentional vocabulary bridge, not keyword substitution throughout the report. Agentic Visualization research paper, Tableau’s agentic-analytics framing.

Language evidence is not demand

The 15 August source pass added four useful boundaries to the language map:

• an independent visualization podcast asks how people actually use dashboards, where generative AI enters visualization work, whether it is effective, and what would make it safer; it is an interview and research preview, not a prevalence estimate (PolicyViz with Melanie Tory);
• a Spanish public-data exercise frames the work as converting natural-language questions into analysis through explicit dataset context, executable code, retries, corrections, and visible results; it is one implementation account, not evidence for a general Spanish-speaking market (datos.gob.es implementation exercise);
• the AccessViz workshop makes co-creation, durable deployment, verification, alternative pathways, and user agency native language for AI-mediated accessibility; the report does not state a participant denominator; and
• official search documentation shows how the site’s own query evidence could later be interpreted, while also documenting anonymized queries, row truncation, normalized Trends scores, filtering, and low-volume noise (Search Console query dimensions, Google Trends methodology).
• a practitioner-authored Fabric issue uses the concrete language of semantic- model-grounded responses, shared versus Fabric-backed capacity, Viewer versus Build permission, and control over access to existing models; the issue is a single unresolved PoC report, not confirmed product behavior (Fabric permission-scope issue); and
• a Microsoft-authored community account describes incorrect or inconsistent answers, context and binding diagnosis, semantic-model preparation, and before/after validation in one customer case; its test denominator is absent, so the reported success and ROI claims are not used as measured evidence (implementation account).

Use a four-rung evidence ladder:

These passes strengthen rung 1 and define how to measure rung 2. Rungs 2–4 are still unknown. Broad Google Trends interest sits outside the ladder: it is context, not site demand. The formerly record-only Fabric issue is now in private custody through the platform’s first-party item RSS, so its bounded wording can shape the BI scorecard. The remaining audience-language custody gate is privacy-approved, site-specific query/referral evidence—not more public search snippets.

What should AI not optimize away?

The language map also needs a retained-human-value check. Independent editorial and practitioner sources do not just ask whether AI makes visualization faster. They ask whether it preserves the work through which people decide what a chart is for, learn with other people, notice and repair errors, and carry a result through feedback into use. The May 2026 Visualising Data Newsletter frames current AI discussion through tensions around text interfaces, creative friction, and the questions an assistant should ask before proposing a chart. A single-practitioner Viz Responsibly reflection adds collaboration, whiteboards, teaching, audience research, joy, and inspectable correction. A 2026 Portuguese-language University of Sao Paulo course puts purpose, audience, co-design, requirements, usability testing, feedback, and delivery alongside Python, generative AI, and Power BI.

These are bounded signals: one curated newsletter issue, one named reflection with an informal show of hands and no denominator, and one official 21-hour course record with 55 places. They do not establish prevalence, effectiveness, or demand. They do identify four values that every audience translation should make visible:

For an executive audience, this can be compressed to one decision question: what valuable work are we paying to remove, and what must remain human-owned? The full evidence and its limits remain in research custody; shorter audience versions should translate that answer without silently discarding it.

Questions worth answering verbatim

These are stronger discovery doors than generic chapter labels:

• What can AI actually do in a data-visualization workflow in 2026?
• Can AI-generated charts and dashboards be trusted?
• How should a team evaluate Power BI Copilot, Tableau Agent, or conversational analytics?
• Which parts of data visualization should remain human-controlled?
• Does an AI visualization tool save time after checking and repair?
• What is agentic visualization, and how is it evaluated?
• Which benchmarks test chart generation, dashboards, interaction, or reader understanding?
• How should journalists disclose and validate AI-assisted analysis and graphics?
• What data-visualization skills still matter when AI can produce a first draft?
• How do accessibility and mobile testing change the verdict?

Each should have a direct two- or three-sentence answer, a visible evidence date, and a route into the deeper report.

Public artifact architecture

One canonical hub, not sibling islands

The current rendered artifacts already have substantial strengths: crawlable text, literal titles and descriptions, responsive viewport declarations, real heading structure, section anchors, skip links, and visible evidence dates. The research and practitioner sites are usable long-form reading surfaces.

The content map adds a local prototype of the routing layer: it shows which draft owns which question, emphasizes four reader paths, compares coverage against the comprehensive-report promise, and keeps planned bridges visually separate from existing work. It does not yet supply durable public URLs, canonical links, source navigation, or publication state.

The current draft package is still a set of sibling artifacts. Inspection on 14 August found no canonical-link declaration, Open Graph or social-card metadata, author metadata, article structured data, alternate feed, or shared citation surface in the AI-visualization artifact heads. The artifacts also do not yet provide one audience router, public change log, or shared source and download index. These are packaging gaps, not research gaps.

The public package should have this shape:

1. Canonical hub. One durable URL, one-sentence thesis, evidence date, scope, audience router, major findings, and links to every rendering.
2. Audience routes. “I make visualizations,” “I run analytics,” “I research visualization,” “I publish data journalism,” “I build AI systems,” and “I teach or evaluate access.” Each begins with decisions, not biography.
3. Executive brief. HTML first; a well-made PDF as a printable companion.
4. Comprehensive report. The connective argument and complete evidence accounting.
5. Standalone essays. Self-contained capability, workflow, reader-outcome, methods, and contested-claim entries with canonical links back to the hub.
6. Decision tools. The evaluator, BI scorecard, research crosswalk, newsroom protocol, and learning/access protocol.
7. Reusable evidence. Public-safe figures, tables, CSV or JSON exports, definitions, and claim/source index. Never publish the private source corpus.
8. Methods and limits. Search dates, inclusion rules, evidence ladder, known absences, conflicts, corrections, and disclosure of assisted methods.
9. Citation and versions. Preferred citation, authors or accountable owners, date published and modified, frozen release, and change log.

The route page contract

Every audience route should fit this sequence above the fold:

• Moment: “You are deciding whether to…”
• Short answer: the strongest conclusion the evidence supports.
• Decision tool: a checklist, scorecard, or crosswalk.
• Three findings: each linked to its evidence and limitation.
• Worked case: a realistic path through the decision.
• Go deeper: the relevant report sections and methods.
• Recheck: evidence date and next update trigger.

Do not begin with an abstract history of AI or a decorative manifesto. The reader should know within one screen whether this page can help with the current decision.

Search and AI visibility

Google’s official 2026 guidance says the same foundations support ordinary Search and generative AI features: useful original content, crawlable text, clear structure, semantic HTML, indexability, and a good page experience. It also says no special AI markup or tiny “AI-sized” chunks are required and that Google does not use llms.txt. The rollout should therefore invest in the substance and technical legibility of the canonical site, not an AEO ritual. Google’s guide to optimization for generative AI.

At publication:

• use stable, descriptive URLs rather than dates alone;
• add canonical links, sitemap, robots policy, RSS or Atom for material updates, and internal links between every rendering;
• supply unique title and description metadata, Open Graph and social cards, accountable author or organization, date published, and date modified;
• use Article or TechArticle structured data only when the visible page supports every field, and Dataset metadata for genuinely downloadable public datasets;
• keep substantive findings in HTML text rather than images, canvas, or a PDF;
• give each major table and figure a stable anchor, caption, alt text, source, and downloadable form;
• expose a preferred citation and a frozen release only after publication is approved; and
• verify indexation and actual referrals separately from merely shipping tags.

The website is the primary reading and linking surface. The PDF is for printing, offline reading, and institutional circulation. It should not be the only indexable version.

After publication, use Search Console’s query dimension only as qualified discovery evidence. Preserve impressions, clicks, page, country, device, date window, and known omitted-query share where available; do not turn the displayed row list into a complete denominator. Google Trends may help compare broad relative interest in candidate terms, but its sampled, normalized 0–100 values do not describe this site’s readers. Neither source measures whether a reader made a better decision.

Rollout strategy

The sequence is reader proof, canonical release, earned interpretation, sustained evidence. The public report should launch once, but the audience routes should travel over several weeks.

Phase 0 — prove decision usefulness before publication

Timing: approximately two weeks, or until the acceptance threshold is met.

Recruit 8–12 named reviewers, with at least two from each of the first four audiences. Include different authority positions: practitioner and reviewer; BI author and platform/governance owner; early-career and senior researcher; reporter/developer and editor.

Give each reviewer one scenario, not the whole site and a satisfaction survey. Observe whether they can:

1. identify their route without explanation;
2. find the evidence relevant to a named decision;
3. distinguish provider claims, benchmark results, human evidence, and unknowns;
4. produce the intended decision record;
5. name what remains unresolved; and
6. cite or send the right unit to a colleague.

Record time to decision, wrong turns, unanswered questions, language mismatches, and whether the artifact changed, confirmed, or failed to affect the decision.

Gate: do not launch until at least one reader in each primary audience can complete its decision path without live guidance and no unresolved defect would misstate evidence, expose private source material, or obscure ownership.

Phase 1 — publish the complete canonical package

Publish together:

• the hub and audience router;
• executive brief;
• comprehensive report;
• research and practitioner companions;
• at least three decision tools: practitioner evaluator, BI scorecard, and research crosswalk;
• methods, public-safe source index, preferred citation, and corrections policy;
• public change log and evidence/recheck date; and
• social cards and a compact set of reusable figures.

The launch note should make one claim, show one original figure, state who the work is for, and link to the router. Avoid “we are thrilled to announce,” an exhaustive feature list, or a thread that reproduces the report without giving a reader a reason to use it.

Gate: human owner approves public claims, source rights, authorship, disclosure, repository/site destination, DOI choice, and publication. HTTP, mobile, accessibility, metadata, links, downloads, and analytics are separate acceptance checks.

Phase 2 — earned distribution by audience

Timing: three to four weeks after the canonical release. The exact dates depend on editorial and community permission.

Week 1: visualization practice. Pitch one original essay or protocol to Nightingale. Share in DVS only under the applicable community rules, framed as a substantive question and resource. Offer the practitioner evaluator, not just the report link. Give newsletters and podcasts one figure plus the evidence behind it.

Week 2: BI and analytics. Run or propose a concrete scorecard session for an AI and Tableau, Fabric/Power BI, or analytics-engineering group. Use product names in the scenario but keep the evaluation vendor-neutral. Ask participants to test the scorecard against one real pilot and report missing inputs.

Week 3: research. Circulate the research edition and benchmark crosswalk to relevant labs and workshop participants. Offer it as a citable synthesis and request corrections, conflicting evidence, and missing studies. The 2026 IEEE VIS submission cycle is already past; plan a 2027 paper, workshop contribution, or dataset release only if the resulting work is genuinely original.

Week 4: journalism and education. Offer the newsroom protocol to IRE/NICAR and OpenNews-adjacent practitioners for review or training. Offer the learning and access protocol separately to EduVis, AccessViz, and teaching communities. Do not collapse editorial accountability and classroom learning into one ethics essay.

Good secondary carriers include established visualization newsletters and podcasts, but outreach should wait until there is a distinctive finding, figure, or protocol they can discuss. Personal LinkedIn posts can make the work visible to existing relationships; they are exhaust from the release, not the strategy.

Phase 3 — keep the evidence alive without creating a content factory

• Publish a change note only when a material claim, source, tool contract, benchmark, or forecast status changes.
• Review high-volatility product and model statements monthly internally.
• Publish a quarterly evidence update or gap-ledger delta if it contains real changes.
• Preserve dated releases; never silently rewrite the historical snapshot.
• Invite corrections and missing primary sources through a bounded form.
• Turn repeated reader questions into better route pages or decision tools.
• Publish new experiments when they add evidence, not to fill a cadence.

Channel map and participation rule

The DVS research-recruitment program is a particularly appropriate route for future original studies: it describes a monthly opt-in email to members and states that member data is not shared with researchers. That is a research recruitment mechanism, not a report-promotion loophole. Use it only when there is a reviewed protocol and human-subjects obligations are clear. DVS research recruitment.

Visibility and usefulness metrics

Use a funnel that preserves the difference between being available and changing work:

Report denominators and segment the measures by audience route. Do not combine one highly engaged research cohort with general traffic and call the average “engagement.” Do not optimize raw visits if the work is meant to change a small number of consequential decisions.

First-cycle targets

Targets should be learning thresholds, not synthetic growth forecasts:

• 8–12 prelaunch decision-path reviews across the four primary audiences;
• one corrected route or artifact from each audience’s feedback;
• three or more completed uses of each launch decision tool;
• at least five attributable reuses across citations, internal protocols, syllabi, presentations, or linked technical work within the first quarter;
• at least two concrete outcome accounts, including “we did not adopt” or “we narrowed the pilot” as legitimate positive evidence; and
• a public ledger of substantive corrections and missing evidence.

If the package receives broad traffic but no recorded decision use, the problem is not solved. If a small number of credible teams use it to improve or stop a decision, that is meaningful early success.

Ownership and gates

No automated posting, synthetic persona outreach, private-community scraping, or posting quota belongs in this strategy. Automation can prepare views, maintain evidence, check links, assemble release assets, and surface approved opportunities. A human should own relationships, claims, and participation.

Risks and countermeasures

The first six artifacts to build

1. Canonical hub and audience router. This is the missing connective public surface and should precede broad distribution.
2. Should AI help with this visualization job? A practitioner evaluator using purpose, stakes, data authority, correction, delivery, and reader evidence.
3. Is this BI agent ready for a real pilot? A scorecard covering the context envelope, semantics, permissions, refusal, reproducible correction, incidents, total cost, and maintenance.
4. What does each benchmark establish? A research crosswalk with task, model, scaffold, renderer, judge, sample, measured outcome, and limits.
5. A newsroom acceptance protocol for AI-assisted analysis and graphics. A reproducibility path from source custody through editorial and reader review.
6. A public-safe evidence bundle. Figures, tables, method, citation, version, and change log that make the work reusable without releasing the private corpus.

The executive brief, comprehensive report, and current long-form companions are already substantive. These six pieces make the corpus navigable and actionable.

Ninety-day sequence

What would change this strategy

Revisit audience order if decision-path tests show another audience completing more consequential jobs with less explanation. Revisit channel choices if community policies, editorial terms, or event calendars change. Revisit the artifact family if readers consistently use the full report and reject the decision tools, or if the tools are used without consulting their evidence and limits. Revisit the positioning if search and referral evidence shows a more precise term consistently brings the intended readers without distorting scope.

Most importantly, replace this modeled audience map with observed use as soon as the first cohort exists. The strategy has succeeded when the report becomes an evidence spine that real people use to make named decisions—not when it has been reformatted into the largest number of posts.

Update log

• 2026-08-16 — Translated B91’s access stop without changing its content state. Every route now carries the unchanged 6 / 20 / 1 / 0 content ledger beside one access-blocked and unassessed row, zero active generic B91 targets, and four exact reopen conditions. Research effort moves only when new custody appears; paused never becomes negative.

• 2026-08-16 — Translated InfoViP’s actor map and closed opportunity across seven routes. Readers can now route research questions to three named mentors while preserving zero named service-operation, maintenance, QA-execution, release-approval, or authorization roles. Closed remains separate from selected, started, built, approved, and released.

• 2026-08-16 — Repaired one InfoViP date and role chain across seven routes. Every route now treats the apparent 2026 future-tense page as content-current to 2024, the 2026 project-lead biography as a precise role rather than operations authority, and the Elsa 4.0/HALO launch as separate from an unobserved InfoViP-specific integration.

• 2026-08-16 — InfoViP’s production-component and QA answer translated seven ways. Every route now carries the final report’s approval, AWS/AERS integration, >30-million-history and ~8,000-daily receipts beside its absent QA plan, planned audits, incomplete routine roles, and uninvestigated downstream effect. The current no-ATO inventory remains visible, and a 2026 Elsa/API/UI opportunity is treated only as a future recheck trigger.

• 2026-08-16 — InfoViP field-use, cost, and governance evidence translated seven ways. Every route starts from 20 unique real-work evaluators, a later 29-million-history plus daily operating pipeline, and a $2.40 million obligated / $2.86 million estimated three-award envelope. Every route keeps current routine use, acceptance/ATO, outcome, return, whole cost, and ROI unresolved; 15 named cases still yield zero complete episodes.

• 2026-08-16 — InfoViP operational-governance result translated seven ways. Added one canonical line from practitioner co-design through an operational pilot, at-scale incoming processing, performance and human control, and current development/no-ATO counterevidence. The audit is now 15 named cases and zero complete episodes.

• 2026-08-16 — One publisher-abstract answer translated seven ways. Every route starts from B92’s exact multicriteria, Monte Carlo, rank-stability abstract and the current 6 / 20 / 1 / 0 ledger. B91 remains unassessed; no route promotes a non-AI method into human use, delivery, outcome, or impact.

• 2026-08-16 — One partial-lineage answer translated seven ways. Every route starts from one live page, zero exact public builds, eight of twelve matching dependency names, two of three canonical asset matches, and zero audience outcomes. No route promotes a source link, demonstration, instrumentation, or intended audience into deployment or use.

• 2026-08-16 — One public-delivery afterlife translated seven ways. Every route starts from B110’s official abstract, pinned source artifacts, and one current same-named live interface while preserving B92 and B91 as content-unassessed at that pass. No route promotes reachability into continuous use, source custody into the deployed build, or intended audience into measured outcome.

• 2026-08-16 — One residual-recovery answer translated seven ways. Every route starts from 14 investigated rows, 11 substantive surfaces, one full chapter, three metadata-only rows, one evaluation-to-redesign mechanism, two practice-context near misses, and zero complete AI lifecycles. No route promotes expert evaluation, stakeholder application, or multi-unit evaluation into deployment or outcome.

• 2026-08-16 — One prototype-evaluation answer translated seven ways. Every route starts from nine investigated rows, eight substantive surfaces, seven explicit human evaluations, InfoViP’s prospective production intent, and 14 remaining content-unassessed DOI rows. No route turns future tense into deployment, conventional NLP into GenAI, or evaluation into outcome.

• 2026-08-16 — One field-use result translated seven ways. Every route starts from the same eight-key authority repair and non-AI cancer-diary comparator while keeping one gated primary text, missing lifecycle receipts, and the unknown complete-review denominator visible.

• 2026-08-16 — One primary-evidence ladder translated seven ways. Five recovered full texts add controlled audience measurement, public-sector co- design/demo, and enterprise production-intent lanes, but no accepted field release plus later affected-audience recheck. Every route preserves 22 DOI rows and eight non-DOI stable keys as unassessed.

• 2026-08-16 — One mature platform result translated without borrowing an AI role. Seven routes now keep AIDSVu’s ten-year delivery, aggregate use, governance, and later data release while retaining its missing AI role, version-bound audience outcome, and affected-audience recheck. Eighty-six abstract cue nonmatches remain unexcluded.

• 2026-08-16 — One custody answer translated without inventing audience evidence. Seven routes now begin from the same bounded result: 122 titles triaged, one explicit GPT signal, one pinned supplemental-output bundle, and zero audience-delivery or later-recheck chains. The 121 title nonmatches remain not surfaced rather than excluded.

• 2026-08-16 — Longitudinal evaluation boundary translated seven ways. Practice gets an evaluation-to-acceptance card; BI gets a release-linked decision ledger; research gets the twelve-receipt matrix; newsrooms get a publication gate; builders get a drift-aware release export; learning and access keep outcomes separate; sponsors get one near-miss and zero complete episodes across 14 named cases. No route promotes development selection or reported confidence into audience impact.

• 2026-08-16 — Full 122-of-127 review frame translated across seven routes. Every route gets the same five unresolved authority joins and one quarantined identifier conflict. The useful layer changes by audience; candidate identity, review provenance, unstarted screening, and the null lifecycle count do not.

• 2026-08-16 — One 12-of-13 Education result translated across seven routes. Every route now distinguishes an authoritative review key from a plausible external candidate. M. Lu (2020) stays unresolved until A208- controlled evidence joins it; no audience may promote confidence into provenance or a lifecycle count.

• 2026-08-16 — One repair overlay translated seven ways. Every route now receives the same source-preserving result: the supplement still prints ?, while A208’s article and DOI metadata uniquely reconstruct Hernández- Calderón et al. (2023). The decision language changes by audience; the absent publisher correction, unfinished stable-key crosswalk, and unknown lifecycle count do not.

• 2026-08-16 — Review inventory integrity translated seven ways. Every route now receives the same three-state answer: 127 studies reported, 126 appendix citations identified, and the complete GenAI lifecycle count unknown. Audience language changes the decision gate without guessing the missing Education row or turning it into a negative result.

• 2026-08-16 — Review-lineage boundary translated seven ways. Every route begins with the same 127-study coverage map and unknown qualifying lifecycle count, then asks for its own artifact, pilot, study, publication, instrumentation, access, or funding receipt. No translation reports zero of 127 or treats review breadth as field efficacy.

• 2026-08-16 — Conditional retry burden translated seven ways. Every route begins with the same 732-of-888 endpoint and 584-versus-714 revision record, then asks for its own stopping, telemetry, acceptance, or reader receipt. No translation turns fewer generations into lower total cost or audience use.

• 2026-08-16 — Production recovery translated seven ways. Every audience route begins with the same 3-afterlife, 2-restoration, 0-affected-actor- recovery, 0-authority-transfer result, then receives its own decision question without turning issue closure or contribution into recovery.

• 2026-08-15 — Reader-outcome boundary translated seven ways. Each audience route keeps the same one-direct, five-adjacent, zero-delivery/recheck result while changing the decision question; no route upgrades participation, creator interpretation, or model QA into delivered audience evidence.

• 2026-08-15 — Governance boundary translated seven ways. Practice, BI, research, journalism, product, learning/access, and sponsor routes now begin with the same 3-control-contract, 2-organizational-use, 2-adjacent-process, 0-complete result and ask for the first missing same-deployment receipt.

• 2026-08-15 — Public acquisition translated seven ways. Practice, BI, research, journalism, product, learning/access, and sponsor routes now begin with one provenance-aware result: 12/12 named listings have install signals, while 0/12 exposes invocation, retention, organizational acceptance, or outcome. No route turns registry or repository attention into adoption.

• 2026-08-15 — System anatomy translated seven ways. Practice, BI, research, journalism, product, learning/access, and sponsor routes now enter one eleven-stage information-and-authority map. The canonical result remains 6/6 generation-execution-critique, 4/6 meaningful human control, and 0/6 accepted-delivery, intended-reader, or maintenance chains; no route turns it into an architecture winner or observed audience demand.
• 2026-08-15 — Capability-to-practice bridge translated seven ways. Ten primary cases yield three controlled partial bridges and zero exact-version accepted-delivery or complete episodes. Practice, BI, research, journalism, product, learning/access, and sponsor layers now ask for the first missing receipt without changing the canonical result or implying audience use.
• 2026-08-15 — Fixed-budget result translated seven ways. Selective TTS adds one matched declared partial call/output-token budget without supplying observed route-wide use or contract acceptance. Practice, BI, research, journalism, product, learning/access, and sponsor routes now keep the three receipt levels separate. The accepted-cost result advances to 0/11 and no route winner is implied.
• 2026-08-15 — Six-receipt null translated seven ways. Practice, BI, research, journalism, product, learning/access, and sponsor routes now use one exact release-validation card. The answer stays zero of seven in named surfaces; every layer retains the first missing receipt and the return-user reopen condition.
• 2026-08-15 — Longitudinal co-design result translated seven ways. Graphy adds three returning blind co-designers, 12 sessions, four workshops, eight months, and implemented changes. Every audience layer keeps formal efficacy, workshop-version binding, release, representative recheck, adoption, and whole cost visibly missing.
• 2026-08-15 — Accepted-cost result translated seven ways. Practice, BI, research, journalism, product, learning/access, and sponsor layers now ask for one route-wide numerator and one frozen accepted-output denominator. At that evidence cut, zero of nine held fragments supplied both; no route winner was implied.
• 2026-08-15 — Version-floor boundary translated seven ways. Added the distinction among study surface, first containing release, participant-tested build, later maintenance, representative recheck, and rewrite. MAIDR narrows the target but still contributes zero complete lifecycle episodes.
• 2026-08-15 — One lifecycle result translated seven ways. Added the exact artifact-version join, four rejected near-misses, and a decision layer for practice, BI, research, journalism, product, learning/access, and sponsors. Every route keeps the same result: zero new episodes, a strong pre-AI accessibility baseline, and no cross-version human-evidence transfer.
• 2026-08-15 — One validity result translated seven ways. Added a shared five-threat configuration boundary and audience-specific questions for practice, BI, research, journalism, product, learning/access, and sponsors. Every route keeps the same result: benchmark success is lane-bound and no current Vizier configuration winner exists.
• 2026-08-15 — Phase and topology language added. Practitioner, BI, research, and builder layers now translate the same receipt: cold start, discovery, execution phase, effective topology and runtime state stay beside planned and observed totals; hidden work remains missing.
• 2026-08-15 — Handoff now has four audience-facing states. The practitioner, BI, research, newsroom, product, education/access, and executive layers now distinguish accepted change, repeat contribution, maintenance authority, and independent recovery. Prism reaches the first two; OpenClaw reaches the first and has an open repair proposal. Neither reaches the last two.
• 2026-08-15 — Recovery now has five actor-separated receipts. Prism’s multi-attempt Home Assistant chain turns report, repair, corrected release, maintainer recheck, and independent recheck into a practitioner, BI, research, newsroom, product, and access decision rule. The last receipt is missing in the case, so the route is not called independently recovered.
• 2026-08-15 — Planned and observed resource receipts. The practitioner, BI, research, and product routes now ask whether each arm stayed inside a frozen multidimensional cap and retain what each task actually used. Matching rounds, candidates, rates, nominal caps, or model labels no longer counts as equal budget; no comparison ran and no cost winner exists.
• 2026-08-15 — Fabric context envelope and correction receipt. Recovered the formerly gated practitioner issue through a first-party RSS representation and added a Microsoft-authored implementation account. The BI route now asks what the assistant can traverse, under which permissions, which semantic objects it can use, and whether a wrong answer can be reproduced, corrected, regression-tested, and owned. Both sources are C-grade self-report; they do not establish a bug, breach, efficacy, prevalence, demand, or reader use.
• 2026-08-15 — Total-cost decision products expanded. The practitioner clock now begins at route preparation, while the BI procurement route keeps five cost classes separate, declares volume and amortization, and rejects source telemetry whose denominator does not reconcile. This is a decision aid, not evidence that either route is cheaper.
• 2026-08-15 — Retained-human-value check. Added independent newsletter, practitioner, and Portuguese teaching evidence for purpose, collaboration, creative friction, audience context, inspectable correction, feedback, and delivery. These are candidate evaluation values, not demand or effect estimates.
• 2026-08-15 — Audience-language evidence ladder. Added primary custody for an independent podcast, a Spanish public-data implementation, AccessViz community outcomes, and official search-measurement limits. Separated source wording, qualified discovery, decision-path use, and changed work; expanded the accessibility route around co-creation, verification, agency, and sustained use. At that pass, Power BI/Fabric community wording remained a capture gap; the later first-party RSS pass above closes only that custody condition.
• 2026-08-14 — Initial public edition. Defined audience routes, decision products, evidence boundaries, publication gates, and an outcome-oriented rollout sequence.