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Intelligence Index

The AI Dividend Tracker · Q4 2026 · Every entry links to its public source. Not investment advice.

Playbooks

How the best-documented AI use cases were built, and how to rebuild each one with tools a single person can buy.

3 complete, 2 in preparation. Each starts from a measured result in the free use-case database and its public source, and ends with a plan to measure your own.

The result, its source and the outline are public. The full build is for members.

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The library

Results are quoted as the source reported them. The evidence line says who measured it and how.

  1. 01 · Available now · Team leads and operators who own a support queue or shared inbox

    The case-reply copilot

    Tag every new support case and have a sourced reply draft waiting before anyone opens it. Rebuilt from smart Europe’s Case Handler, scaled down to one team and one workflow tool.

    “40% decrease in case resolution times … 20% increase in ‘first contact resolution’”

    Source
    smart Europe · vendor blog
    Evidence
    Vendor blog co-written with the customer. Not independently checked.
    Effort
    2–4 days for a working pilot on one queue; 2 weeks of measured running
    Cost
    Roughly €20–60 a month in tools plus model usage of about 1–3 cents per case

  2. 02 · Available now · Anyone whose day is back-to-back conversations followed by write-ups, such as managers and consultants

    Notes that write themselves

    Turn every client or team conversation into a structured, quote-backed note and a follow-up draft, so documentation stops eating your evenings. Rebuilt from the ambient documentation CHRISTUS Health rolled out with Abridge.

    “Clinicians spent an average of 60% less time on documentation outside of work hours”

    Source
    CHRISTUS Health · vendor press release
    Evidence
    Vendor press release citing a clinician survey. Self-reported.
    Effort
    Half a day to set up; one week to tune the template
    Cost
    €0–40 a month: built-in transcription you may already pay for, plus a chat assistant plan

  3. 03 · Available now · Analysts and operators who write some Excel formulas, SQL or Python and have a backlog of small automations

    Build the internal tool yourself

    Use an AI pair programmer to write the small scripts and tools you would otherwise wait months for, and test your own speed-up the way GitHub’s randomised experiment did.

    “developers who used GitHub Copilot completed the task significantly faster–55% faster than the developers who didn’t use GitHub Copilot”

    Source
    GitHub / Microsoft · vendor research (controlled experiment)
    Evidence
    Randomised controlled experiment, also published as a paper on arXiv. Run by the vendor, on a single small task.
    Effort
    One hour to set up; first useful tool in one to two days
    Cost
    $0–20 a month: free tiers exist; Copilot Pro is $10 a month (US dollars, check current pricing)

  4. 04 · In preparation · Analysts and consultants who answer RFPs, due-diligence or ESG questionnaires

    The two-hour questionnaire in fifteen minutes

    Answer long, repetitive due-diligence and risk questionnaires from your own documents, with every answer linked to the page it came from.

    “the banks using them have reduced the time required to answer climate risk questions by approximately 90 percent, from more than two hours to less than 15 minutes”

    Source
    Banks surveyed by McKinsey (unnamed) · consultant article
    Evidence
    Consultant article about unnamed banks. The banks cannot be identified or checked.

  5. 05 · In preparation · Lawyers and tax advisers who write the same kinds of analyses again and again

    Up to ten hours a week back on first drafts

    A drafting assistant that writes first drafts of memos and client letters from your own templates and precedents. You review and sign.

    “Up to 10 hours of work saved per week per lawyer”

    Source
    Steuerrecht.com · vendor case study
    Evidence
    Vendor case study. The founder’s own estimate.

In every playbook

The measured result and how much to trust it. Why it paid off. A day-one version in a chat assistant. The automated build with copy-ready prompts and a build sheet. Effort and cost in days and euros. A measurement plan on the source’s own metric. The pitfalls.

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