Domain Knowledge workbook

Executive summary

A single operating system for building AI solution engineering skills, industry fluency, case studies and portfolio evidence. Download the Excel file, edit scores and trackers, and keep it as your personal practice ledger—the artefact managers review to see whether Business Learning is actually happening, not whether you bookmarked another page.

When to use this page

• At the start of every week: pick industry + capability focus in the 12-week plan.
• During each 10-hour industry sprint: fill sprints, battlecards, terms, questions and use cases.
• In weekly review: log hours, outputs, presentation practice and next actions.
• Before staffing or promotion conversations: export dashboard + case studies + artefact register as evidence.

Download the workable Excel

Download Domain Knowledge.xlsx — editable offline in Excel, Google Sheets or LibreOffice.

Formulas on the dashboard, skill matrix, 12-week plan, use cases and case-study sheets update as you enter scores and status.

:::tip Keep one master copy Save a dated copy each month (Domain-Knowledge-YYYY-MM.xlsx) so progress is recoverable and reviewable with a manager or coach. :::

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Operating principles

Before sheet detail, internalise three rules from 00_Start_Here:

1. Solution engineering sequence: Ambiguity → Discovery → Problem → Value → Options → Architecture → Controls → Proposal → Roadmap → Adoption → Benefits.
2. Industry learning sequence: Economics → Value chain → Stakeholders → Systems/Data → Regulation → AI opportunities → Client proposition.
3. Automation hierarchy: Process redesign → Policy/training → Rules → Search/analytics → ML → GenAI → Agentic AI — choose the simplest effective solution.

Learning ratio to protect: 20% learn → 60% produce → 20% present / feedback. If a week exceeds 50% reading with empty output columns, reset.

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Weekly cadence

Day	Activity	Sheets
Monday	Set week focus; confirm sprint stage or lifecycle row	03, 04 or 09
Tue–Thu	Produce: terms, questions, use cases, or case study draft	06–08, 10
Friday	Five-minute briefing practice; log hours and outputs	13, link 11
Weekend optional	Read one Business Learning / Stage 1 topic	—

Weekly minimum: 6 hours logged on 13_Weekly_Review; at least one tangible output linked in 11_Artefact_Register or advanced on sprint/battlecard sheets.

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Manager review ritual

Duration: 20–30 minutes weekly; 45–60 minutes monthly.

Weekly (manager + engineer)

1. Open 03_12_Week_Plan — confirm planned vs actual focus.
2. Open 13_Weekly_Review — verify hours, outputs, briefing practice row.
3. Spot-check one row on 06, 07 or 08 — can the engineer explain without reading?
4. Update one 02_Skill_Matrix row with evidence URL or file path.
5. Agree one next-week done-when deliverable.

Monthly

1. Review 01_Dashboard — trend in scores, sprint completion, case study count.
2. Audit 11_Artefact_Register — reusable IP growing?
3. Read one 10_Case_Studies draft — VALUE clean?
4. Compare to Competency test Q1–Q4 — written answers improving?

Manager red flags: Empty 13 rows; sprint "complete" with <8 hours; battlecard pasted from unverified AI; skill scores rising without evidence links.

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How to use this workbook (workflow)

Step	Action	Sheet
1	Choose the week — one main industry and one primary capability focus	03_12_Week_Plan
2	Run the industry sprint — seven stages of the 10-hour method	04_Industry_Sprints
3	Build active knowledge — 30 terms, 20 executive questions, 10 AI use cases	06–08
4	Apply the solution lifecycle — ambiguity through benefits realisation	09_Solution_Lifecycle
5	Create evidence — case studies and artefact register	10–11
6	Review weekly — hours, outputs, presentation practice, feedback	13_Weekly_Review

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Sheet-by-sheet operating guide

00_Start_Here

Purpose: In-workbook instructions, mastery checks and system rules—the contract for how other sheets relate.

When used: First open; when onboarding a coach; when you change industries and need a reset reminder.

Quality bar: You can explain the three sequences (solution, industry, automation hierarchy) without reading. Mastery checks ticked only with evidence elsewhere.

Common mistakes: Skipped entirely; treated as legal text not operational rules.

Example: Before week 1, read Start Here, then jump to 03—not to 06 random terms.

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01_Dashboard

Purpose: Personal development dashboard driven by scores and status entered on other sheets—single pane for manager monthly review.

When used: End of week/month; before promotion packets.

Quality bar: Dashboard reflects honest inputs—no manual override of formulas to "look green." Trends show movement over ≥4 weeks.

Common mistakes: Expecting dashboard value with empty 02 and 13; snapshot once never updated.

Example: After updating three skill matrix rows and completing a sprint, refresh dashboard and screenshot for monthly 1:1.

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02_Skill_Matrix

Purpose: Capability scores (1–5), gaps, priority, evidence links and next actions across AI Solution Engineering skills.

When used: Baseline week 1; update after any artefact that proves growth; monthly deep review.

Quality bar: Each score has evidence (file, link, engagement code) or is labelled "self-assessed—no evidence yet." Next action is SMART. Priority aligns to role target (T-shaped profile from Learning Map overview).

Scoring guide:

Score	Meaning
1	Aware term exists
2	Can explain concept
3	Produced artefact with feedback
4	Repeated success on live work
5	Teaches others; firm reusable IP

Common mistakes: Inflated scores after reading only; no evidence column; all priorities "high."

Example: "Stakeholder mapping — 3 — evidence: /artefacts/acme/stakeholder-map-v2.pptx — next: facilitate exec readout 2026-08-15."

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03_12_Week_Plan

Purpose: Weekly control centre for the 90-day accelerated plan—one industry + one capability focus per week.

When used: Every Monday; adjusted when live client work overrides practice industry.

Quality bar: Each week row names industry, capability focus, planned hours, expected output, status. Actual outputs logged cross-ref to 13.

Common mistakes: Copy-paste same industry twelve times without sprint completion; no capability rotation; plan disconnected from Stage 1 topics.

Example week row: Industry: UK retail banking; Focus: value hypothesis; Hours: 7; Output: battlecard draft + 10 terms; Status: in progress.

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04_Industry_Sprints

Purpose: Tracker for the 10-hour industry sprint (Domain Knowledge Gathering)—seven stages with hour budgets.

When used: Whenever starting or continuing an industry immersion.

Quality bar: All seven stages logged with hours and notes; total ≥8 hours before marking complete; primary sources cited in notes (annual report, regulator, trade body).

Stage reminder:

1. Economics and market structure (~1.5h)
2. Value chain (~1.5h)
3. Stakeholders (~1h)
4. Systems and data (~1.5h)
5. Regulation (~1.5h)
6. AI opportunities (~1.5h)
7. Client proposition (~1.5h)

Common mistakes: LLM summary in one hour marked complete; skipping regulation; no link forward to 05 battlecard.

Example note (stage 5): "FCA Consumer Duty — read PS22/15 summary; implications for advice AI — 1.2h."

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05_Industry_Battlecards

Purpose: One-page industry battlecards—executive-ready snapshot for client conversations.

When used: After sprint stages 1–5 minimum; refresh quarterly or when major regulation changes.

Quality bar: One screen / one page: economics, value chain, stakeholders, systems, regulation, top AI bets, landmines. Passes VALUE. No unexplained acronyms.

Common mistakes: Essay not battlecard; generic AI opportunities; missing "landmines" (what not to say); unverified stats.

Example outline in cell structure: Headline industry thesis → 3 KPIs → chain diagram text → 5 stakeholders → 5 systems → 3 regulatory bullets → 5 AI use cases (one line each) → 3 landmines.

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06_Domain_Terms

Purpose: 30 domain terms per industry with definitions in client language.

When used: During sprint stage 1–2 and week 4 production block; flashcard review before briefings.

Quality bar: Exactly 30 terms; each definable ** aloud in ≤20 seconds**; includes mix of commercial, operational and regulatory vocabulary; source noted for non-obvious terms.

Common mistakes: IT jargon only; circular definitions; duplicate synonyms; ChatGPT list without verification.

Example rows: "Net interest margin (NIM)" — "Interest earned minus paid, divided by earning assets — primary profitability lens for retail banks — source: bank annual report glossary."

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07_Executive_Questions

Purpose: 20 executive questions you should be able to ask—or answer—in a C-suite conversation for the industry.

When used: Sprint stage 3 and week 4–5; before pursuit meetings.

Quality bar: Questions are respectful and sharp—not trivia. Mix of strategy, economics, risk, operations, data, AI realism. Paired with why it matters note.

Common mistakes: Questions answerable by Google in ten seconds; no follow-up depth; all questions to CFO only.

Example: "Where in the value chain do you lose most margin today—and is that operational, pricing or mix?" — tests whether AI talk connects to P&L.

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08_AI_Use_Cases

Purpose: 10 AI use cases per industry with scoring fields (value, feasibility, risk, readiness)—forces prioritisation.

When used: Sprint stage 6; input to 09_Solution_Lifecycle and client workshops.

Quality bar: Each use case: problem, pattern, data, metric, simplest alternative. Scores justified. At least two stopped ideas with reasons (credibility).

Common mistakes: All GenAI; no data owner; no metric; scores all high.

Example: "Agentic claims FNOL" — pattern: workflow + doc extraction — data: FNOL photos + policy PDF — metric: cycle time — alternative: rules for straight-through low value — feasibility: amber (data quality).

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09_Solution_Lifecycle

Purpose: Apply the solution engineering sequence to a concrete opportunity—ambiguity through benefits—row by row.

When used: After first battlecard; when moving from industry learning to specific use case on live or fictional client.

Quality bar: Each lifecycle stage has entry criteria met before marking done. Outputs link to Core deliverables where applicable (problem statement, value hypothesis, etc.).

Common mistakes: Jumping to architecture before value; empty controls row; benefits without baseline.

Example row (Value): "Reduce average handle time 12% in tier-1 service — baseline: 480s from ops dashboard Q1 — hypothesis: £2.1m annualised — owner: COO service line."

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10_Case_Studies

Purpose: Professional evidence portfolio—write-ups suitable for staffing, promotion or sales support (sanitised).

When used: After completing one lifecycle pass or finishing a real engagement chapter.

Quality bar: Structure: context, problem, approach, outcome, limits, reusable pattern. Client anonymised unless approved. Honest about failures. Linked artefacts in 11.

Common mistakes: Marketing fluff; no metrics; confidential data exposed; "I built a chatbot" without business frame.

Example sections: Client context (public) → Problem and metric → Options considered → What we did → Eval/controls highlight → Outcome (range) → What I'd do differently → Pattern library tag.

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11_Artefact_Register

Purpose: Index of reusable artefacts—maps, decks, models, SOWs, eval sets—with location, version, audience.

When used: Whenever you produce anything reusable; weekly Friday update.

Quality bar: Every row: name, type, industry, date, path/link, VALUE reviewed (Y/N), reuse notes. No orphan files on personal drive unlisted.

Common mistakes: Register never updated; broken links; "final_v3_FINAL" naming chaos.

Example row: "Acme stakeholder map" — pptx — retail — 2026-07-15 — /artefacts/acme/stakeholder-v2.pptx — VALUE Y — reuse: change BU names only.

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12_Lists

Purpose: Shared dropdown lists (status, priority, fluency, audience)—keeps sheets consistent for filtering and dashboard logic.

When used: When adding rows to other sheets; do not edit unless extending controlled vocabulary with coach agreement.

Quality bar: Consistent use of dropdown values—not free-text "done", "Done", "COMPLETE".

Common mistakes: Typo statuses breaking filters; personal abbreviations only you understand.

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13_Weekly_Review

Purpose: Weekly practice and reflection log—hours, outputs, presentation practice, feedback received, next step.

When used: Every Friday; non-negotiable for Business Learning discipline.

Quality bar: Hours honest; outputs specific ("battlecard v1" not "studied"); briefing practice logged with recording or witness; feedback captured verbatim; next step SMART.

Common mistakes: Blank weeks; outputs copied from plan without delivery; no presentation row ever filled.

Example row: Week 2026-W30 — 7.5h — outputs: battlecard v1, 15 terms — briefing: 5 min retail AI to manager — feedback: lead with metric — next: complete terms 16–30 by Aug 5.

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Method pages in this subsection

• Domain Knowledge Gathering — narrative method for the 10-hour sprint
• Business Keywords — vocabulary while filling battlecards and terms
• Business Power Words — language for the five-minute briefing practice
• Business Briefing — worked example of a complete business and product proposition
• Business Learning overview — 12-week path and manager coaching

Output

After using the workbook for one week you should have:

• Updated skill-matrix scores for at least three critical gaps
• One industry sprint row in progress or complete
• At least one artefact linked in the artefact register
• A weekly review entry with hours and next step

First-week checklist

• Downloaded Domain-Knowledge.xlsx and saved a personal working copy
• Set Week 1 industry and capability focus on 03_12_Week_Plan
• Scored yourself honestly on 02_Skill_Matrix (edit suggested starters)
• Started 04_Industry_Sprints for the chosen industry
• Booked one five-minute briefing practice for the weekly review

Twelve-week workbook calendar (detailed)

Week	03 focus	Primary sheets	Done-when
1	Pick industry A; comms baseline	02, 03, 04 start	Skill matrix baseline; sprint stage 1–2 logged
2	Economics deep dive	04, 06 start	15 terms; sprint stage 3–4
3	Stakeholders + systems	04, 05 draft	Battlecard v0.1; 25 terms
4	Regulation + AI bets	04, 06–08	Sprint complete; 30 terms; 10 use cases scored
5	Executive questions	07, 13	20 questions; weekly review habit locked
6	Lifecycle: ambiguity → value	09	Rows through value hypothesis
7	Lifecycle: options → controls	09, 11	Architecture/control rows; one artefact registered
8	Briefing production	10 start, 13	Business Briefing-style write-up linked
9	Case study draft	10, 11	Case study v1; manager review
10	Second industry scan OR deepen A	03, 04	New sprint started OR lifecycle complete on use case
11	Portfolio polish	01, 02, 11	Dashboard reviewed; three evidence links added
12	Capstone	all	Monthly save; competency Q1–Q4 draft in case study appendix

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Troubleshooting

Symptom	Likely cause	Fix
Formulas show errors	Wrong dropdown value or deleted row	Restore from monthly backup; check 12_Lists
Dashboard flat	No updates to 02/08/13	Enter at least one score change and one status change
"Complete" sprint, thin battlecard	Rushed stages 5–7	Reopen 04; add regulation and landmines
Manager cannot review	No 13 entries	Book fixed Friday slot; treat as delivery commitment
Google Sheets breaks formulas	Import compatibility	Prefer Excel for master; export CSV artefacts to 11

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Related Learning Map topics

• Industry and Domain Knowledge
• Business Fundamentals
• Communication and Executive Articulation
• Business Learning overview
• Core deliverables
• Competency test

Key takeaways

• The workbook is the proof ledger for Business Learning—empty sheets mean empty capability claims.
• Follow sheet order: plan → sprint → battlecard → terms/questions/use cases → lifecycle → evidence → weekly review.
• Manager rituals weekly and monthly turn Excel rows into coaching conversations, not surveillance.
• Each sheet has a quality bar—battlecards, terms and use cases have numeric completeness targets (30/20/10).
• Save monthly dated copies; link everything reusable in 11_Artefact_Register for career evidence.

What to do next

1. Download the Excel file above and complete the first-week checklist.
2. Follow Domain Knowledge Gathering while filling sheets 04–08.
3. Return to Business Learning overview for the full 12-week practice path.
4. Map completed lifecycle rows to Core deliverables and test yourself with Competency test Q1–Q4.