Take one weekly report and ask what an AI system would actually change: its permitted actions, input evidence, reliability, total effort, review process or model choice. Those six questions give industry trends a practical test. A stronger benchmark score or wider adoption does not, by itself, show that your report becomes faster or more accurate.
The video’s six trends can be turned into six questions about one workflow. Use a fictional weekly operations report as the example: collect a table, read several notes, identify unresolved issues and prepare a draft for a person to review. The exercises below are proposed evaluations, not results from a product test.
1. What can the system actually change?
The 2026 AI Index reports agent success on OSWorld rising from about 12% to roughly 66%. That is a benchmark result under specified conditions, rather than a success rate for every agent application. Substantial measured improvement can coexist with substantial failure.
For the fictional report, divide the job into collecting evidence, drafting and distributing. Let the trial prepare a draft before considering distribution. Record whether the application can edit the source table, contact colleagues or change a shared document, and which of those operations the assignment requires.
A demonstration that collects five notes successfully does not establish permission to send them to everyone in a directory. Keep capability and authority as separate entries in your evaluation. Expansion should have a named purpose and a check appropriate to the added action.
2. Which format carries the evidence?
A table, a screenshot of that table and a spoken description may contain overlapping information without offering the same evidence. For the report trial, provide invented totals in a small spreadsheet and an image of a chart showing those totals. Include one deliberately inconsistent label.
Ask the application to preserve the original values and identify the disagreement. Check whether it reports the conflict or produces a smooth account that hides it. Then repeat with only the screenshot. You are testing how this workflow handles different inputs, rather than assuming that accepting an image means interpreting every detail correctly.
Stanford illustrates uneven capability with strong mathematics results alongside unreliable analog-clock reading. That comparison cautions against borrowing confidence from an unrelated task. Your chart-reading check needs its own result, even if the same model performs well on coding.
3. Does use become a repeatable improvement?
The Index reports AI adoption in 88% of surveyed organizations. An adoption measure does not establish the benefit of one organization’s reporting process. Keep the survey population and definition separate from your own claim.
For the fictional workflow, write down the normal process before adding AI. Which person checks the totals? Who resolves missing notes? What makes the report ready? Otherwise, a trial may quietly compare an unchecked AI draft with a fully reviewed human report.
Try the same defined process on several invented reporting weeks. Include a missing input and a corrected figure. Record whether the application asks for clarification, preserves the correction and leaves unresolved issues visible. Repetition helps expose obvious variation; a handful of examples is still not a general reliability estimate.
4. What does a useful result cost?
Model usage is only one entry in a workflow budget. For each trial, record preparation time, waiting time, usage charges if applicable, review time and correction time. Compare completed reports that meet the same standard.
A fictional run might create a draft quickly but require the reviewer to reopen every source. Another might take longer to generate while attaching accurate references that shorten review. The evaluation should preserve that difference rather than rank the runs by response speed alone.
The Index documents concentrated chip supply and data-center infrastructure. Those are industry constraints, not a way to calculate the electricity used by your particular report. Avoid converting a global infrastructure figure into a per-task environmental claim without a suitable measurement method.
5. Who notices a plausible error?
Stanford records 362 documented AI incidents for 2025, compared with 233 for 2024. A documented-incident count is not an incident rate per user or proof of why the count changed. Its practical relevance here is to keep evaluation broader than successful demonstrations.
For the report, plant an invented note that contradicts a table entry. Give one reviewer the final draft and the source set. Can they identify which statement needs checking? A draft that sounds reasonable while concealing its evidence creates a different review burden from one that marks uncertainty clearly.
Name the person responsible for release and define how a correction will be made after distribution. Do this while the task is small. A vague commitment to human oversight becomes useful only when someone can describe what they review and what authority they have.
6. Which model fits this particular job?
Compare candidate models on the same invented input set, required output and permission boundary. Keep settings and tool access visible. If one candidate has a search tool and another does not, the result compares those complete configurations, not model intelligence alone.
Write down failure types as well as scores: missing correction, wrong total, unsupported explanation or unnecessary action. The site’s guide to reading AI benchmarks explains why task setup matters. Your local comparison should make its setup just as explicit.
End with a decision limited to the evidence: which configuration is worth a further trial, which task remains with a person and which question is unresolved. The six trends provide reasons to investigate. The inspected output, actual effort and working controls provide reasons to change a workflow.
