Artificial Intelligence Fundamentals for Business Training Course

Course overview

Business teams are being asked to identify useful AI applications, assess vendor claims, and use generative AI without exposing confidential information or creating unreliable outputs. Many professionals understand the headlines but cannot distinguish machine learning from rules-based automation, explain why a model produced a poor result, or frame an AI proposal with measurable business value and manageable risk. This course gives participants a practical foundation for making informed decisions about AI initiatives rather than treating AI as either a technical black box or a general-purpose chatbot.

Participants examine the main AI approaches used in organisations: predictive machine learning, natural language processing, computer vision, recommendation systems and generative AI. They learn to translate a business problem into an AI use-case statement, identify data requirements, define success metrics, test generative AI prompts and outputs, and recognise limitations such as bias, hallucination, model drift and privacy exposure. The course also introduces AI governance practices, including NIST AI RMF risk categories, human oversight controls and documentation aligned to ISO/IEC 42001.

Instructor-led briefings are combined with tool demonstrations, guided prompt testing, data-quality exercises and business cases drawn from customer service, operations, finance, HR and sales. Working in small groups, participants build an AI opportunity assessment for a realistic business process, including the problem definition, stakeholders, data sources, expected value, risks, controls and pilot measures. They leave with a reusable AI use-case canvas, an evaluation checklist for AI outputs and a certificate on completion.

The course is designed for business professionals, analysts, product teams and managers who need AI literacy for planning, purchasing, governing or using AI-enabled services. It is not a programming or model-engineering course; its focus is sound business application, responsible adoption and confident communication with technical specialists and suppliers.

Course objectives

By the end of this course, participants will be able to:

• Differentiate predictive machine learning, generative AI, automation and analytics for a defined business problem
• Frame an AI use case using a problem statement, user journey, data inputs, value hypothesis and success measures
• Assess data readiness by identifying source systems, data quality gaps, access constraints and sensitive data
• Write and test structured prompts using role, context, constraints, examples and output-format specifications
• Evaluate generative AI outputs for factuality, relevance, bias, privacy exposure and required human review
• Apply NIST AI RMF risk categories to document model, data, operational and stakeholder risks
• Compare AI vendor proposals using capability claims, integration requirements, governance controls and total-cost factors
• Produce an AI opportunity assessment and pilot plan for a selected business process

Benefits of attending

Target competencies

Who should attend

• Business Analysts — who must translate operational problems into feasible AI requirements
• Product Managers — who prioritise AI-enabled product features and define measurable user value
• Digital Transformation Managers — who need a structured way to select and sequence AI initiatives
• Operations Managers — who evaluate AI for workflow improvement, forecasting and service delivery
• Data and Analytics Managers — who need to align business stakeholders on data readiness and model limitations
• Procurement and Vendor Managers — who assess AI supplier claims, controls and implementation commitments

Requirements and prerequisites

Participants should be comfortable discussing business processes, KPIs and the data their function uses, such as customer records, transactions, documents or operational reports. Familiarity with spreadsheets and ordinary web-based business software is assumed because exercises involve reviewing sample data, completing templates and testing prompts in a browser. No programming, statistics, Python, model-building experience or prior use of generative AI is required. Complete beginners to AI are welcome, but should expect to work with business cases and structured decision tools rather than learn to build or train models from code.

Training methodology

The five-day programme combines instructor-led explanation with short demonstrations, facilitated discussion and practical workshops. Participants inspect sample datasets, test prompt patterns in a controlled generative AI environment, review AI output failures and work through cases involving service triage, demand forecasting and document processing. Teams use an AI use-case canvas, risk register and pilot scorecard to develop one application from problem definition to governance controls. The final session is an application-planning workshop in which each participant prepares a 90-day action plan for their own function.

Course outline

Day 1: AI concepts and business value

• Artificial intelligence, machine learning and automation distinctions
• Supervised learning, unsupervised learning and reinforcement learning basics
• Generative AI, large language models and token-based output generation
• Natural language processing, computer vision and recommendation use cases
• Business-process selection criteria for AI adoption
• Value hypotheses, baseline measures and benefit-realisation metrics
• AI lifecycle stages from problem framing to monitoring

Workshop: Participants map a familiar business process and produce a shortlist of three AI opportunities ranked by business value and feasibility.

Day 2: Data, models and decision quality

• Training, validation and test data purposes
• Structured, unstructured and multimodal data sources
• Data quality dimensions: completeness, accuracy, timeliness and representativeness
• Features, labels, predictions and confidence scores
• Classification, regression, clustering and recommendation model selection
• False positives, false negatives and decision-threshold trade-offs
• Model drift, data drift and post-deployment monitoring

Workshop: Using a customer-service case, participants complete a data-readiness assessment and define appropriate model performance measures.

Day 3: Generative AI in business workflows

• Large language model strengths, limitations and common failure modes
• Structured prompt patterns using role, task, context and constraints
• Few-shot examples and output-schema prompting
• Retrieval-augmented generation and approved knowledge sources
• Hallucination testing and factuality verification methods
• Human-in-the-loop review design for generated content
• Microsoft Azure AI Studio and ChatGPT Enterprise workflow examples

Workshop: Participants design, test and revise prompts for a policy-summary task, producing a prompt card and output-review checklist.

Day 4: Responsible AI, governance and suppliers

• Bias sources in data, labels, model design and deployment decisions
• Privacy, confidentiality and personal-data handling in AI systems
• NIST AI RMF govern, map, measure and manage functions
• ISO/IEC 42001 AI management system concepts
• Explainability, transparency and user-notification requirements
• Human oversight, escalation paths and audit evidence
• AI vendor due diligence and contract-control questions

Workshop: Teams assess an AI recruitment-screening proposal and produce a risk register with mitigations, owners and approval gates.

Day 5: From opportunity to controlled pilot

• AI use-case canvas and stakeholder mapping
• Pilot scope, assumptions and minimum viable data requirements
• Business case calculations for cost, benefit and adoption effort
• Acceptance criteria and evaluation-test design
• Operating model roles for business, IT, data and risk teams
• Implementation roadmap, decision gates and change-management actions
• Ninety-day AI adoption planning

Workshop: Participants present an AI opportunity assessment and pilot plan, receiving instructor and peer feedback before producing a 90-day action plan.

Tools & standards covered

Microsoft Azure AI Studio, ChatGPT Enterprise, NIST AI Risk Management Framework, ISO/IEC 42001

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions