Practical AI for Product Owners & Business Analysts (Enterprise Edition)

Course structure

13 modules, each with a concrete output

Every module builds toward an enterprise AI solution brief you can take to stakeholder review.

#	Topic	Purpose	Output
1	AI in the enterprise context	Understand what AI means inside a large organisation.	Shared vocabulary
2	Finding real AI value	Identify suitable business problems and avoid shallow use cases.	Use case shortlist
3	Building the business case	Justify AI investment to stakeholders and secure delivery capacity.	Value case one-pager
4	From idea to use case	Structure an AI idea into a usable business use case.	Use case canvas
5	AI product discovery	Map users, workflows, decisions, risks and human review points.	Discovery notes
6	AI requirements	Capture prompts, data, outputs, guardrails, citations and escalations.	Requirement checklist
7	Data & knowledge readiness	Assess documents, metadata, access, quality and ownership.	Readiness assessment
8	RAG, assistants and agents	Understand key solution patterns and their trade-offs.	Pattern selection
9	Risk and governance	Design for safe, responsible and auditable AI use.	Risk register
10	Working with delivery teams	Clarify collaboration across functions.	RACI-style view
11	Testing and evaluation	Define what good looks like for AI outputs.	Evaluation plan
12	Enterprise delivery lifecycle	Move from prototype to pilot to production.	Delivery roadmap
13	Capstone	Create a complete enterprise AI solution brief.	Final brief

Detailed module outline

1 AI in the Enterprise Context ▾

What AI, GenAI, LLMs, RAG, agents, copilots and automation mean in practical terms.
Where AI helps enterprise knowledge work and where it does not.
Common enterprise AI patterns: internal assistant, policy Q&A, document triage, report drafting, workflow support and decision augmentation.
Why enterprise AI must consider security, governance, adoption, cost and operating model from day one.

2 Where AI Creates Real Business Value ▾

How to identify pain points in document-heavy, decision-heavy and knowledge-heavy processes.
How to separate automation, augmentation and advisory use cases.
Value, feasibility, data readiness and risk as prioritisation lenses.
Typical use cases: policy assistant, contract review, due diligence support, operational knowledge search, donor reporting, audit evidence search and impact summaries.

3 Building the Business Case ▾

Framing expected value honestly: efficiency, quality, risk reduction or capacity release — and which one actually applies.
Estimating cost and effort at a rough order of magnitude: build/run cost, data preparation, review overhead and ongoing maintenance.
Distinguishing one-off pilot cost from ongoing operating cost, including model usage, monitoring and human review time.
Framing the ask for a steering committee or investment board: problem, expected benefit, cost range, risk profile and decision needed.
Common pitfalls: overstating automation benefit, ignoring change-management cost, and unrealistic manual-process baselines.

4 From Idea to Use Case ▾

Turning "Can we use AI for this?" into a clear use case.
Problem statement, target users, current process, pain points, expected outcome and measurable benefits.
Inputs, outputs, source systems, document sets, permissions and constraints.
Success criteria, assumptions and non-goals.

5 AI Product Discovery for POs and BAs ▾

Mapping user journeys and process flows with AI assistance embedded.
Identifying decision points, human review steps, exceptions and escalation paths.
Understanding user trust, adoption barriers and explainability needs.
Defining MVP scope without over-promising full automation.

6 Requirements for AI-Enabled Systems ▾

Writing requirements for prompts, expected answers, tone, format and business rules.
Specifying knowledge sources, citations, confidence, fallback responses and prohibited behaviours.
Capturing access control, data sensitivity, retention, audit trail and human-in-the-loop requirements.
Acceptance criteria for AI systems where outputs are probabilistic rather than deterministic.

7 Data, Knowledge and Document Readiness ▾

8 RAG, Assistants and Agents — Explained for Business Teams ▾

Retrieval-Augmented Generation explained with business examples.
Difference between a chatbot, AI assistant, workflow automation and agentic system.
When to use RAG, when to use structured workflows, and when not to use agents.
Enterprise risks: tool access, uncontrolled actions, stale knowledge and unclear accountability.

9 AI Risk, Governance and Responsible Use ▾

Hallucination, bias, confidentiality, explainability, accountability and model risk.
Responsible AI principles translated into PO/BA delivery behaviours.
Risk controls: human review, citations, disclaimers, guardrails, monitoring and approval gates.
Governance questions every AI use case should answer before pilot or production.

10 Working with Engineering, Architecture, Security & Compliance ▾

How POs/BAs collaborate with AI engineers, data teams, architects and platform teams.
What to discuss with security, legal, compliance, risk, procurement and audit.
How to frame build vs. buy vs. configure decisions before a use case reaches architecture or procurement.
What good handover looks like from business discovery to technical delivery.
Defining roles, responsibilities and sign-offs across the AI lifecycle.

11 Testing, Evaluation and Quality for AI Products ▾

How to define "good enough" for AI outputs.
Test datasets, golden questions, expected answers, citation checks and edge cases.
User acceptance testing for AI assistants and document intelligence solutions.
Production monitoring: feedback, drift, quality issues, cost and usage patterns.

12 Enterprise AI Delivery Lifecycle ▾

Moving from idea to prototype, pilot, production and continuous improvement.
What changes between demo, proof of concept, pilot and enterprise service.
Adoption as a delivery workstream: rollout sequencing, manager sponsorship, habit formation and in-context support.
Support model, cost ownership and operational readiness for production AI services.
Roadmap planning for AI products where quality improves through iteration.

13 Capstone — Enterprise AI Solution Brief ▾

Select one realistic enterprise problem and create a complete AI solution brief.
The brief includes problem, users, process, data sources, solution pattern, requirements, risks, metrics and delivery roadmap.
Final review focuses on clarity, feasibility, governance readiness and delivery handover quality.