1
AI in Professional Services: from task to value
~1.5 h
Applied AI for Big4 IT Professionals
A practical AI course for IT professionals at Big4 and other professional-services firms. Not data-science training: you learn to choose the right AI approach, measure quality, cost and risk, and design a PoC that can honestly say no. The whole course follows one running case. You work in the Digital & IT team of the fictional firm Aldervane Advisory LLP and run two pilots: a Triage PoC that suggests and partly automates routing of ENGAGE Service Desk tickets, and a Policy Assistant, a RAG assistant over internal IT policies that later gets limited agent actions. The data is synthetic but carries real-world defects: leakage in the ticket export, a superseded policy version in the index, a restricted client folder. Topics: framing AI use cases; optimization; probability, base rates and Bayesian updating; classical ML (regression, classification, similarity, triage); enterprise documents; evaluation (precision, recall, overfitting, shadow mode, monitoring); neural networks without magic; LLMs and adaptation options; RAG; AI agents, least privilege and prompt injection; Responsible AI and governance, including the EU AI Act, NIST AI RMF and ISO/IEC 42001 as of September 2026; business cases and go / iterate / stop criteria; a capstone proposal. Every topic is taught as pain, solution and cost. About 190 practice items including calculations, diagrams and illustrations in every unit, and three optional Python labs with ready-made data generators: workload forecasting, ticket classification and a RAG prototype. Course language: Russian.
Your lesson is in your language: your assistant leads it.
- Material: Russian
- Exam: Russian — taken on the site, not translated
How it works
Your assistant takes the course material from our server and leads the lesson in your language.
The exam is taken on the site, without an assistant. Exam questions are not translated: this is decided for every course.
Path through the course
· 19 unitsthe order is free · the steps below are the order the author suggests, not a conditionView: map · steps
2
Optimization: when AI searches for the best solution
~1.5 h
by the author’s design, after “AI in Professional Services: from task to value”
3
~1.5 h
by the author’s design, after “AI in Professional Services: from task to value”
4
Bayesian reasoning: updating confidence on evidence
~1.5 h
by the author’s design, after “Probability and uncertainty”
5
~2 h
by the author’s design, after “AI in Professional Services: from task to value”
6
~1.5 h
by the author’s design, after “Machine Learning Fundamentals”
7
Classification, similarity and triage
~2 h
by the author’s design, after “Machine Learning Fundamentals”
8
Working with text and enterprise documents
~2 h
by the author’s design, after “Machine Learning Fundamentals”
9
Model Evaluation, overfitting and business metrics
~2 h
by the author’s design, after “Regression and forecasting”, “Classification, similarity and triage”
10
Neural Networks and Deep Learning without magic
~1.5 h
by the author’s design, after “Model Evaluation, overfitting and business metrics”
11
Generative AI and LLMs in the enterprise
~2.5 h
by the author’s design, after “Neural Networks and Deep Learning without magic”
12
~2.5 h
by the author’s design, after “Generative AI and LLMs in the enterprise”
13
AI Agents and workflow automation
~2 h
by the author’s design, after “RAG and Enterprise Knowledge”
14
Responsible AI, privacy, security and governance
~2.5 h
by the author’s design, after “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”
15
AI Business Case: from idea to PoC
~2 h
by the author’s design, after “Model Evaluation, overfitting and business metrics”, “Responsible AI, privacy, security and governance”
16
Capstone: AI Solution Proposal
~4 h
by the author’s design, after 13 of these: “AI in Professional Services: from task to value”, “Optimization: when AI searches for the best solution”, “Probability and uncertainty”, “Bayesian reasoning: updating confidence on evidence”, “Machine Learning Fundamentals”, “Regression and forecasting”, “Classification, similarity and triage”, “Working with text and enterprise documents”, “Model Evaluation, overfitting and business metrics”, “Neural Networks and Deep Learning without magic”, “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”, “AI Agents and workflow automation”, “Responsible AI, privacy, security and governance”, “AI Business Case: from idea to PoC”
17 · Technical Labs
Optional Lab: Regression in Python
~2 h
by the author’s design, after “Regression and forecasting”
18 · Technical Labs
Optional Lab: Text Classification in Python
~2 h
by the author’s design, after “Classification, similarity and triage”
19 · Technical Labs
~3 h
by the author’s design, after “RAG and Enterprise Knowledge”
final · in the browser, without the assistant
Exam
closed · enrol on the course
The path in words
- Stage 1 · AI in Professional Services: from task to value — ~1.5 h
- Stage 2 · Optimization: when AI searches for the best solution — by the author’s design, after “AI in Professional Services: from task to value”; ~1.5 h
- Stage 2 · Probability and uncertainty — by the author’s design, after “AI in Professional Services: from task to value”; ~1.5 h
- Stage 2 · Machine Learning Fundamentals — by the author’s design, after “AI in Professional Services: from task to value”; ~2 h
- Stage 3 · Bayesian reasoning: updating confidence on evidence — by the author’s design, after “Probability and uncertainty”; ~1.5 h
- Stage 3 · Regression and forecasting — by the author’s design, after “Machine Learning Fundamentals”; ~1.5 h
- Stage 3 · Classification, similarity and triage — by the author’s design, after “Machine Learning Fundamentals”; ~2 h
- Stage 3 · Working with text and enterprise documents — by the author’s design, after “Machine Learning Fundamentals”; ~2 h
- Stage 4 · Model Evaluation, overfitting and business metrics — by the author’s design, after “Regression and forecasting”, “Classification, similarity and triage”; ~2 h
- Stage 4 · Optional Lab: Regression in Python — by the author’s design, after “Regression and forecasting”; optional; ~2 h
- Stage 4 · Optional Lab: Text Classification in Python — by the author’s design, after “Classification, similarity and triage”; optional; ~2 h
- Stage 5 · Neural Networks and Deep Learning without magic — by the author’s design, after “Model Evaluation, overfitting and business metrics”; ~1.5 h
- Stage 6 · Generative AI and LLMs in the enterprise — by the author’s design, after “Neural Networks and Deep Learning without magic”; ~2.5 h
- Stage 7 · RAG and Enterprise Knowledge — by the author’s design, after “Generative AI and LLMs in the enterprise”; ~2.5 h
- Stage 8 · AI Agents and workflow automation — by the author’s design, after “RAG and Enterprise Knowledge”; ~2 h
- Stage 8 · Responsible AI, privacy, security and governance — by the author’s design, after “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”; ~2.5 h
- Stage 8 · Optional Lab: RAG Prototype — by the author’s design, after “RAG and Enterprise Knowledge”; optional; ~3 h
- Stage 9 · AI Business Case: from idea to PoC — by the author’s design, after “Model Evaluation, overfitting and business metrics”, “Responsible AI, privacy, security and governance”; ~2 h
- Stage 10 · Capstone: AI Solution Proposal — by the author’s design, after 13 of these: “AI in Professional Services: from task to value”, “Optimization: when AI searches for the best solution”, “Probability and uncertainty”, “Bayesian reasoning: updating confidence on evidence”, “Machine Learning Fundamentals”, “Regression and forecasting”, “Classification, similarity and triage”, “Working with text and enterprise documents”, “Model Evaluation, overfitting and business metrics”, “Neural Networks and Deep Learning without magic”, “Generative AI and LLMs in the enterprise”, “RAG and Enterprise Knowledge”, “AI Agents and workflow automation”, “Responsible AI, privacy, security and governance”, “AI Business Case: from idea to PoC”; ~4 h
Programme · 19 units
| 1 | AI in Professional Services: from task to value Distinguish an AI task from ordinary automation and analytics | ~1.5 h |
| 2 | Optimization: when AI searches for the best solution Understand optimization as choosing the best action under constraints | ~1.5 h |
| 3 | Probability and uncertainty Interpret a model's probability as a share over a set of cases and understand calibration | ~1.5 h |
| 4 | Bayesian reasoning: updating confidence on evidence Understand prior, likelihood and posterior, and calculate the update through odds and the likelihood ratio | ~1.5 h |
| 5 | Machine Learning Fundamentals Distinguish supervised (classification, regression), unsupervised and generative problem settings | ~2 h |
| 6 | Regression and forecasting Understand regression as predicting a number and forecasting as regression over time | ~1.5 h |
| 7 | Classification, similarity and triage Distinguish binary, multiclass and multilabel classification | ~2 h |
| 8 | Working with text and enterprise documents Distinguish extraction, classification, retrieval, summarization and generation, and match each with its own check | ~2 h |