Domain Knowledge Gathering

Executive summary

The Smart Industry Learning System: Economics → Value chain → Stakeholders → Systems and data → Regulation → AI opportunities → Client proposition — run as a 10-hour sprint per industry.

When to use this page

• Before starting a new industry engagement or proposal.
• When filling Industry Sprints, Battlecards and Domain Terms in the workbook.
• When you need domain fluency fast without drowning in passive reading.

The Smart Industry Learning System

Use the same seven-part structure for every industry:

Economics → Value chain → Stakeholders → Systems and data → Regulation → AI opportunities → Client proposition

You should be able to answer seven questions:

• How does this organisation make or receive money?

• What are its most important business processes?

• Which KPIs matter to its executives?

• What systems and data operate the business?

• What could go seriously wrong?

• Where can AI create measurable value?

• How would I explain the solution to the client?

When you can answer these, you have enough domain fluency to start shaping solutions.

1. Use a 10-hour sprint for each industry

You do not initially need weeks of study. Complete a focused 10-hour sprint, then deepen your knowledge when a real client opportunity appears.

Hour 1: Understand the industry

Learn:

• main business models;

• customer types;

• revenue sources;

• largest costs;

• major competitors;

• current industry pressures.

Create a one-page Industry Executive Summary.

Hours 2–3: Map the value chain

Identify the processes from the beginning of the business journey to the final outcome.

For a bank:

Customer acquisition → Onboarding → KYC → Account servicing → Payments/lending → Monitoring → Complaints → Retention

For a retailer:

Product planning → Sourcing → Warehousing → Merchandising → Marketing → Sales → Fulfilment → Returns

Mark each process as:

• customer-facing;

• operational;

• risk or control;

• finance or reporting;

• technology-enabled.

Hour 4: Learn the stakeholders

For every domain, identify:

• economic buyer;

• business sponsor;

• technical buyer;

• risk approver;

• operational user;

• data owner;

• affected customer.

For example, a banking AI solution may involve:

• COO;

• Head of Operations;

• CIO;

• CDO;

• Chief Risk Officer;

• Compliance;

• Model Risk;

• Data Protection Officer;

• frontline employees.

Hours 5–6: Learn systems and data

Do not try to memorise every product. Understand the categories:

• system of record;

• workflow system;

• customer platform;

• analytics platform;

• document repository;

• integration layer;

• security systems;

• industry-specific operational systems.

Then identify:

• structured data;

• unstructured data;

• real-time data;

• confidential data;

• personally identifiable data;

• regulated data.

Hour 7: Learn regulation and critical risks

Focus on the regulations and risks that affect solution design.

Ask:

• Can AI make the decision?

• Is human review required?

• Must the decision be explainable?

• Can data leave the organisation?

• How long must evidence be retained?

• Could the outcome harm a customer, patient, employee or citizen?

• What audit evidence is required?

Hours 8–9: Prioritise AI opportunities

Use a simple scoring model:

Factor	Question
Business value	Does it increase revenue, reduce cost or reduce risk?
Feasibility	Is suitable data available?
Adoption	Will users incorporate it into their workflow?
Risk	Can the risks be controlled?
Scalability	Can the capability be reused?
Differentiation	Does it create competitive advantage?

Score each factor from 1–5.

Hour 10: Present a client case

Create a five-minute briefing containing:

• Client problem

• Current process

• Business impact

• Proposed AI capability

• Architecture overview

• Security and governance

• Implementation roadmap

• Expected value

• Key risks

• Decision required

This final exercise creates far more learning than passive reading.

2. What to learn first in each industry

Financial Services

Learn first

• Banking, insurance and asset-management business models

• KYC, AML and financial crime

• Customer onboarding

• Lending and credit risk

• Payments

• Claims and underwriting

• Consumer Duty

• Operational resilience

• Model risk management

Essential processes

Banking

Lead → Application → Identity verification → Credit decision → Account opening → Servicing → Monitoring → Collections

Insurance

Quote → Underwrite → Bind → Policy servicing → Claim → Assessment → Settlement → Renewal

Essential KPIs

• cost-to-income ratio;

• net interest margin;

• default rate;

• fraud loss;

• straight-through processing rate;

• claims leakage;

• combined ratio;

• complaint-resolution time;

• customer retention.

Best learning case

Design an AI-assisted KYC and onboarding platform.

Learn how it handles:

• identity documents;

• PII;

• sanctions screening;

• adverse media;

• human escalation;

• decision evidence;

• model monitoring.

Technology, Media and Telecommunications

Learn first

• SaaS business models

• Product-led growth

• Subscription economics

• Cloud economics

• OSS and BSS in telecoms

• Network operations

• Content rights

• Advertising and subscriptions

• Platform ecosystems

Essential processes

SaaS

Acquisition → Trial → Activation → Subscription → Usage → Support → Renewal → Expansion

Telecommunications

Customer acquisition → Provisioning → Network usage → Billing → Assurance → Support → Retention

Essential KPIs

• annual recurring revenue;

• monthly recurring revenue;

• churn;

• net revenue retention;

• active users;

• average revenue per user;

• network availability;

• mean time to repair;

• cost per transaction.

Best learning case

Design an AI customer-service and network-assurance platform for a telecom company.

Include:

• customer-service agent;

• billing explanation;

• network incident retrieval;

• churn prediction;

• human handoff;

• OSS/BSS integration.

Government and Public Services

Learn first

• Public-sector funding

• Policy implementation

• Public procurement

• Digital service standards

• Citizen case management

• Benefits and grants

• Accessibility

• Public accountability

• Equality and transparency

Essential process

Policy → Funding → Service design → Eligibility → Application → Assessment → Decision → Service delivery → Appeal → Reporting

Essential KPIs

• cost per case;

• processing time;

• fraud and error;

• service availability;

• citizen satisfaction;

• digital adoption;

• accessibility;

• policy outcome.

Best learning case

Design an AI caseworker copilot for a government department.

It should support:

• document summarisation;

• policy retrieval;

• correspondence drafting;

• eligibility checks;

• evidence citation;

• human approval;

• full audit trail.

Healthcare

Learn first

• Patient journeys

• Primary and secondary care

• Referral and triage

• Electronic patient records

• Waiting-list management

• Clinical safety

• Information governance

• Medical-device considerations

• Human clinical responsibility

Essential process

Patient need → Appointment → Triage → Diagnosis → Treatment → Monitoring → Discharge → Follow-up

Essential KPIs

• waiting time;

• length of stay;

• readmission;

• appointment utilisation;

• patient outcomes;

• diagnostic turnaround;

• clinician administrative time;

• patient satisfaction.

Best learning case

Design an AI clinical-documentation and patient-triage assistant.

You must address:

• hallucination;

• clinical validation;

• health-data privacy;

• human sign-off;

• demographic bias;

• safety monitoring.

Consumer Markets and Retail

Learn first

• Product and category management

• Merchandising

• Pricing

• Promotions

• Demand forecasting

• Inventory

• E-commerce

• Customer loyalty

• Fulfilment

• Returns

Essential process

Product planning → Sourcing → Inventory → Marketing → Purchase → Fulfilment → Customer service → Returns → Retention

Essential KPIs

• gross margin;

• conversion rate;

• average order value;

• stockout rate;

• inventory turnover;

• return rate;

• customer-acquisition cost;

• lifetime value;

• on-time delivery.

Best learning case

Design an AI demand-forecasting and customer-personalisation platform.

Cover:

• forecasting;

• inventory recommendations;

• product recommendations;

• promotion optimisation;

• unfair pricing risks;

• customer consent.

Energy and Utilities

Learn first

• Generation

• Transmission and distribution

• Energy retail

• Smart metering

• Energy trading

• Grid balancing

• Asset management

• Field services

• Renewable-energy integration

• Critical national infrastructure

Essential process

Generate/procure → Transmit → Distribute → Meter → Bill → Support → Maintain assets → Report

Essential KPIs

• network availability;

• outage duration;

• forecast accuracy;

• asset utilisation;

• cost per megawatt-hour;

• maintenance cost;

• network losses;

• carbon intensity.

Best learning case

Design an AI predictive-maintenance and field-engineer copilot.

Include:

• IoT telemetry;

• anomaly detection;

• maintenance history;

• engineering-document retrieval;

• offline operation;

• safety controls;

• cyber and operational-technology security.

Industrial Manufacturing

Learn first

• Product lifecycle

• Bills of materials

• Production planning

• Procurement

• Manufacturing execution

• Quality control

• Maintenance

• Warehouse management

• Supply-chain planning

• Operational technology

Essential process

Design → Source → Plan → Manufacture → Inspect → Store → Deliver → Service

Essential KPIs

• overall equipment effectiveness;

• throughput;

• downtime;

• yield;

• scrap rate;

• cycle time;

• first-pass quality;

• on-time delivery;

• warranty cost.

Best learning case

Design an AI visual-inspection and predictive-maintenance platform.

Cover:

• machine data;

• computer vision;

• manufacturing execution integration;

• edge deployment;

• worker safety;

• false positives;

• human override.

Private Equity and Funds

Learn first

• Investment thesis

• Deal origination

• Due diligence

• Valuation

• Investment Committee

• Acquisition

• Portfolio value creation

• Exit planning

• EBITDA

• IRR and investment returns

Essential process

Fundraising → Origination → Screening → Due diligence → Investment decision → Acquisition → Value creation → Exit

Essential KPIs

• EBITDA;

• EBITDA margin;

• cash conversion;

• internal rate of return;

• multiple on invested capital;

• leverage;

• working capital;

• revenue growth;

• cost savings.

Best learning case

Design an AI due-diligence and portfolio-intelligence platform.

Include:

• data-room analysis;

• contract extraction;

• market intelligence;

• financial anomaly detection;

• portfolio KPI reporting;

• source traceability;

• confidential-data controls.

3. Learn through one common AI use case

A very effective method is to study the same AI capability across different industries.

Use customer service as an example:

Industry	Customer-service AI must understand
Banking	Accounts, payments, fraud, complaints and Consumer Duty
Insurance	Policies, claims, coverage and renewals
Telecoms	Plans, billing, provisioning and network incidents
Retail	Products, orders, delivery, returns and loyalty
Energy	Tariffs, meters, billing, outages and vulnerable customers
Government	Eligibility, applications, policy and appeals
Healthcare	Appointments, referrals, patient information and escalation

The underlying architecture may be similar, but the following change:

• domain terminology;

• workflows;

• integrations;

• risk level;

• required evidence;

• escalation rules;

• success metrics.

This teaches you to distinguish reusable AI capability from industry-specific solution design.

4. Use the 30–20–10 learning method

For each industry, create:

30 domain terms

Examples for banking:

• net interest margin;

• loan-to-value;

• expected credit loss;

• KYC;

• AML;

• sanctions;

• arrears;

• collections;

• capital adequacy;

• operational resilience.

20 executive questions

Examples:

• What is driving the current cost-to-serve?

• Which process creates the highest customer friction?

• Where do cases require manual review?

• Which decisions require regulatory explanation?

• What evidence must be retained?

• Which legacy systems constrain automation?

• How will benefits be measured?

10 AI use cases

For every use case capture:

• problem;

• user;

• workflow;

• data;

• AI capability;

• integration;

• controls;

• KPI;

• value;

• implementation complexity.

That creates a complete first-level domain foundation.

5. Use active learning instead of passive reading

Avoid spending hours highlighting reports. Use this cycle:

Step 1: Read

Spend no more than 30–45 minutes on one topic.

Step 2: Close the source

Without looking, write:

• five things you learned;

• three terms you need to remember;

• two risks;

• one AI opportunity.

Step 3: Explain it

Explain the industry aloud as though speaking to:

• a CEO;

• an operations leader;

• a solution architect;

• a risk officer.

Each audience requires different language.

Step 4: Apply it

Design one AI solution or improve an existing process.

Step 5: Retrieve later

Review after:

• one day;

• one week;

• one month.

This will produce stronger retention than repeatedly rereading material.

6. Create a one-page Industry Battlecard

Your battlecard should contain:

Industry overview

• business models;

• customers;

• revenue;

• major costs;

• market pressures.

Executive priorities

• growth;

• cost;

• customer experience;

• resilience;

• compliance;

• innovation.

Value chain

A single end-to-end process diagram.

KPIs

The ten most important metrics.

Technology landscape

• core systems;

• major data sources;

• integration challenges;

• cloud and legacy considerations.

Regulation

The principal regulator, laws and control expectations.

AI opportunity map

• quick wins;

• strategic opportunities;

• high-risk opportunities;

• opportunities to avoid initially.

Client questions

Ten discovery questions.

This should be the first document you open before an industry-related client discussion.

7. Create three levels of knowledge

Do not aim for the same depth across every sector.

Level 1: Conversational fluency

You can:

• explain the business model;

• use basic terminology;

• identify stakeholders;

• discuss common AI opportunities.

Target: every big 4 firm industry.

Level 2: Solution fluency

You can:

• map processes;

• understand data and systems;

• discuss architecture;

• identify regulations;

• build a business case.

Target: four industries.

Level 3: Advisory fluency

You can:

• challenge the client;

• compare operating models;

• understand regulatory nuances;

• quantify value;

• shape transformation programmes.

Target: two or three industries.

For you, I recommend:

• Level 3: Financial Services and TMT

• Level 2: Government, Healthcare, Consumer Markets and Energy

• Level 1: Manufacturing, Private Equity and remaining subsectors

8. Your 12-week accelerated schedule

Weeks 1–2: Financial Services

Complete banking and insurance battlecards and design one regulated AI solution.

Weeks 3–4: TMT

Study SaaS and telecommunications economics and create a telecom AI platform proposal.

Weeks 5–6: Government and Healthcare

Study public-service case management, healthcare journeys and human-accountability requirements.

Weeks 7–8: Consumer Markets

Study retail economics, demand planning and customer journeys.

Weeks 9–10: Energy and Manufacturing

Study asset-heavy operations, predictive maintenance, IoT and operational technology.

Week 11: Private Equity

Learn deals, due diligence, investment returns and portfolio value creation.

Week 12: Executive simulation

Prepare three client presentations:

• Banking onboarding AI

• Telecom customer-service AI

• Government caseworker copilot

For each, present:

• business case;

• proposed architecture;

• governance model;

• implementation roadmap;

• value;

• risks.

9. How to know you have learned an industry

You are ready when you can complete these tasks without notes:

• explain the industry in three minutes;

• draw its value chain;

• identify ten domain terms;

• name its major executive KPIs;

• identify the core systems and datasets;

• explain three regulatory risks;

• propose five AI use cases;

• ask ten strong discovery questions;

• present one credible AI solution;

• explain why the client should invest now.

The best next step is to build your first complete Financial Services AI Solution Engineering Learning Pack, using this method as the template for all subsequent industries.

Related Business Learning content

• Business Learning overview
• Domain Knowledge workbook
• Business and consulting

Key takeaways

• Use this page as practice material, not only reference reading.
• Capture outputs in the Domain Knowledge workbook so learning becomes evidence.
• Link back to Stage 1 Learning Map topics when you need the curriculum framing.

What to do next

1. Open the Domain Knowledge workbook and run your first industry sprint.
2. Record artefacts in the Domain Knowledge workbook.
3. Return to Business Learning overview.