Strategy: AI x Jenn
Approved snapshot · 10 Mar 2026

Public-data workbench · FWC PFDM

Florida commercial fisheries

Spiny lobster and stone crab research with traceable sources, interactive analysis, and an Excel-ready handoff.

Coverage
2000–2024
Annual records
50 verified
Data posture
Public · human sourced

Next system layer

A repeatable model for AI-assisted pattern finding

RAG supplies the relevant definitions, precedents, source excerpts, and house methods. A controlled calculation layer does the manipulation; validation keeps every result tied to the underlying rows.

AI adoption landscape · August 2026

How peer firms are structuring AI adoption

Leading firms are no longer treating AI as a license rollout. They are combining secure knowledge platforms, scaled learning, builder capacity, and a visible path from experiments to reusable work.

Operating-system view

Four layers between access and adoption

Select a layer to see what it contributes. The peer evidence below varies in scale and maturity, but these four components recur across the public record.

Reusable workAdaptable, reviewable, measurable
Select a layer to inspect it

What peers have put in place

The common pattern: platform, learning, builders, measurement

These are public proof points, not a claim that every program is directly comparable to FTI.

01

McKinsey

Lilli · firm knowledge platform

72%of the firm active
30%reported time savings, up to
500K+prompts each month
  • Built one secure surface for search and synthesis across firm knowledge.
  • Added specialized knowledge agents for workflow- and industry-specific tasks.
  • Paired the product with continued education, user communities, and skill development.
McKinsey source ↗
02

EY

EYQ · secure GenAI ecosystem

400Kemployees reached
115Kmonthly users reported
24Mtraining hours
  • Created a preferred internal alternative to external tools for confidential work.
  • Expanded from a general assistant into domain-specific agents using EY knowledge.
  • Tracked adoption alongside fairness, reliability, accountability, and training.
EY source ↗
03

Accenture

AI Navigator + industry assets

$3Bthree-year AI investment
80Ktarget AI talent base
19industries in scope
  • Connected use-case selection to business cases, architecture, and responsible-AI policy.
  • Invested in talent, acquisitions, pre-built models, and industry-specific solutions.
  • Made builder capacity an explicit part of the operating model—not volunteer work.
Accenture source ↗
04

PwC

ChatPwC + My AI upskilling

$1BUS investment over three years
65KUS employees in upskilling scope
200K+global ChatPwC rollout reported
  • Modernized internal platforms around a secure enterprise GenAI environment.
  • Combined firmwide learning with prompting, responsible AI, and workflow examples.
  • Published practitioner case studies showing review and human oversight in delivery.
PwC source ↗

Comparative read

Where FTI can differentiate

Its advantage is narrower and more defensible: expert standards, high-consequence workflows, and practitioners who know where an answer can fail.

Operating layerPeer patternFTI opportunity
Start pointOne secure, common entry surfaceMake approved tools and boundaries legible in one place
  • FTI has made the stack decision: Copilot for everyday work, FTI GPT and Ariadne for client-facing work. The missing piece is a single page that answers whether AI can be used on a given matter. The case-checker prototype built in LDR does this for one practice; the open step is extending it.
  • Trainings surfaced the limit that bites: Copilot grounds on tenant content with no per-case off switch, so on expert engagements the control is behavioral. The entry surface has to carry that boundary visibly, not assume analysts remember it.
  • Wave-0 Copilot Premium is champions-first, and broader segment access is conditioned on demonstrated value. The entry surface is also where that evidence gets captured: what was tried, on what work, with what result.
KnowledgeFirm content retrieved inside the assistantEncode review rubrics, source hierarchies, formats, and failure modes
  • The rubric format exists and is in use: the nine-session Copilot training series logs every session the same way: the capability, the limit that bites, the one reusable move, a measured test on LDR work, and the counsel/privilege risk line. Those pages live in OneNote.
  • The content is written; retrieval is not built. Seven LDR AI guidance documents and an 18-lesson practitioner course tailored to LDR work exist, and none of that content surfaces inside the assistant at the moment a question is asked.
  • The reusable part extends beyond the prompt level: judgment, document structure, citation practices, review process, and examples that embody the team's standards, written down so the next analyst does not rediscover them.
AdoptionTraining plus named builder capacityMove champions from advocates to workflow builders
  • The builder step happened once this summer: an intern cohort produced an expert report, a defended mock deposition, and a 35-slide closing presentation under an AI-supported sequence: show the standard, focus the review, add human judgment, test independence.
  • The pass bar is the independence test: not "did AI produce it?" but "can the analyst reproduce, defend, and adapt it?" Training gets measured by delivery change, not completion counts.
  • Champions currently field recurring Copilot questions with no way to tell whether one was answered before, where the answer lives, or whether it warrants a tool. The first builder deliverable is a routing layer that records all three, built once, centrally.
ProofInvestment and usage presented at scaleMeasure reuse, error reduction, time-to-proficiency, and senior-review leverage
  • The measurement unit comes from the session-notes system: signed minutes saved or lost with an honest denominator, logged per use to the AI usage register. A count out of a stated number of tries, never an adjective.
  • Senior reviewers set two bars during the training series: state the limit before the feature, and do not present specified capabilities as verified. Program reporting follows both.
  • Segment access decisions run on this evidence: what champions test and document shapes seat allocation for the wider group. The register is what supports the case for further tooling.

Public evidence

FTI AI services McKinsey Lilli EYQ rollout Accenture AI investment PwC AI investment

Share-safe system view

How I turn an AI idea into work another person can test.

This is the portable part of my setup: the questions, controls, review path, and evidence that carry a prototype toward useful work. It is shown with public or approved demonstration data.

Working path

The setup is a controlled flow, not a list of tools.

AI leader readout

The questions I would ask before calling a workflow ready.

The six-stage flow above compresses into three leadership decisions. The evidence is uneven. Fisheries shows working Build and Governance controls. Clifford shows that Transfer can work, while other projects still depended on the original builder.

01Pre-development
Development

Build

Leadership question

What is the recurring question, and who can reject the answer?

My answer

Fisheries gives me a concrete case. The Florida Fish and Wildlife Conservation Commission Public Fisheries Data Module supplies 3,335 trip-ticket rows for 2000–2024. The Federal Reserve Bank of St. Louis FRED series CPIAUCSL supplies 300 monthly, seasonally adjusted CPI-U observations. The workflow preserves those sources, the calculation path, exceptions, and an inspectable FTI workbook. The practitioner can still reject it when the code runs. A reviewer did exactly that when the time control was unclear, years appeared on the wrong axis, and county results needed a map. Those objections became requirements.

Evidence
FWC PFDM records, FRED CPIAUCSL, the FTI workbook, and three reviewer objections carried into the next version.
Leadership decision
Name the practitioner and the rejection standard before development begins. The practitioner’s standard and the permission boundary are set before the tool is chosen.
02Before upload

Governance

Leadership question

What happens when the model, permissions, or data change, and who approves?

My answer

A change reopens the boundary instead of inheriting the last approval. New FWC PFDM data or a revised CPIAUCSL vintage enters as a candidate, never as a replacement for the approved snapshot. Schema, period, duplicate, and reconciliation checks run before promotion; a failure quarantines the candidate and names every failed gate. A reviewer’s CPI catch became a fail-closed contract rather than a one-time correction. The public demonstration deliberately disables promotion. A firm pilot would require an authenticated practitioner to approve the candidate, preserve the prior snapshot, and retain a rollback path.

Evidence
The refusal receipt, candidate quarantine, unchanged approved snapshot, named failure gates, and portable file-based outputs.
Leadership decision
Require controlled promotion, with approval outside the model and separate from the output it produced.
03After upload

Transfer

Leadership question

Can a second person run this and explain where it fails?

My answer

The evidence is mixed, which is useful. Over ten weeks, Project Clifford used LDR Hub to coordinate three interns across A-01 market concentration, A-03 fleet age, A-04 backlog, A-05 price index, and A-99 overcharge regression. They produced 28 report exhibits, and each intern defended an individual method without me answering for them. A separate exit review found that other tools still depended on the original builder. Fisheries has completed a reviewer correction loop, but not a full independent handoff. I would not call it organizational capability until a second analyst can reproduce the output, defend the method, adapt when inputs change, and identify the decisions that remain human.

Evidence
Five named Clifford workstreams, 28 exhibits, three individual method defenses, one Fisheries reviewer loop, and a negative finding that exposed builder dependence elsewhere.
Leadership decision
I would withhold a scale claim until the second-user test passes. Every workflow also needs an owner, users, access rules, a review interval, and a retirement condition.

Source basis: public demonstration artifacts and internal notes from documented events.

Operating record

What the execution archive can actually support.

This is a sanitized count of Claude Code and Codex Desktop execution records from March 19 through August 17, 2026. It measures system activity, not the quality of every interaction or the amount of human attention behind it.

2,525Execution records

543 parent threads across a 4.85 GB source archive.

152,815Tool calls

Observed in provider execution records.

26,617Confirmed file interactions

7,442 unique paths, counted conservatively.

765.20Machine-active hours

Derived with a five-minute inactivity cap; not human hours.

Provider-specific token fields remain separate because their accounting models and archive coverage differ.
ProviderRuns with usageInput and cacheOutputTotal reported context
Claude Code707 of 72224,325,750 fresh input1,046,800,753 cache creation; 16,283,627,577 cache read72,657,30117,427,411,381
Codex Desktop1,079 of 1,803586,525,565 uncached input10,784,740,096 cached input43,098,31413,905,477 reasoning output11,414,622,375

Frozen August 17, 2026. Confirmed file interactions require a dedicated file tool or an explicit patch target; generic shell path mentions are excluded. Unique paths are therefore a lower bound. Token totals are provider-reported activity measures, not cost. Human attention, hours saved, and substantive usefulness are unavailable from these records and are not inferred.

The controls are available in the working demonstration.

These links open the current research workflow and the artifacts used to test it.

What this page shares

The portable operating model

  • Workflow stages and decision points
  • Public demonstration data and source receipts
  • Verification categories and handoff expectations
  • Working controls that can be opened and tested

What stays private

The internal operating record

  • Credentials, local paths, and raw session content
  • Private stakeholder notes and contact history
  • Unapproved client or firm data
  • Internal comparison and prioritization records

A useful comparison

One bounded workflow creates a useful comparison of evidence, correction, review, and transfer.

That comparison would show where the approaches genuinely differ and which controls are worth carrying into a firm-approved pilot.

Open the working demonstration

Live tool · LDR Hub

Map studio

The working Project Harness map builder, including its real basemap, track templates, CSV import, review checks, and export controls.

Open full LDR builder ↗
Public demonstrationNo LDR Hub access code is required for this tool.