AI Literacy and Responsible Use for Technology Teams Training Course

Course overview

Technology teams are being asked to use generative AI in software delivery, support, analytics, documentation and internal decision-making—often before clear working practices exist. Individual experimentation can create value, but it can also expose source code, customer data, credentials and intellectual property; introduce unverified outputs into technical work; or create inconsistent decisions about which tools are acceptable. This course gives practitioners and team leads a shared, practical basis for judging where AI belongs in their work and how to use it responsibly.

Participants learn the core concepts behind predictive AI, generative AI, large language models and retrieval-augmented generation without treating model output as authoritative. They practise writing bounded prompts, assessing output quality, identifying hallucinations and bias, protecting sensitive information, and applying human review controls. The course also connects everyday tool use to organisational governance through risk classification, model and use-case documentation, vendor questions, NIST AI RMF and ISO/IEC 42001 principles.

Delivered over five instructor-led days, the programme combines short technical briefings with prompt labs, incident analyses, policy workshops and team-based risk assessments. Participants work through realistic technology scenarios such as AI-assisted coding, service-desk summarisation, knowledge-base search and data analysis. They leave with an AI use-case assessment pack: a defined use case, data-handling decision, prompt and review protocol, risk register, control recommendations and a 90-day adoption action plan ready to discuss with their manager or governance team.

The course suits IT, data and digital professionals who already work with business systems, software, data or technology suppliers and need practical AI judgement rather than model-building expertise. It is particularly useful where teams need to move from informal experimentation to repeatable, defensible use.

Course objectives

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

• Distinguish predictive models, generative AI, large language models and retrieval-augmented generation in technology use cases
• Classify AI use cases using a risk-and-impact assessment matrix
• Write bounded prompts that specify context, constraints, output format and verification requirements
• Evaluate AI-generated technical content using accuracy, provenance, bias and security review criteria
• Apply data classification rules to decide what information may be entered into an AI tool
• Create a human-in-the-loop review protocol for AI-assisted coding, analysis or customer-support work
• Document an AI use case with purpose, data flows, owners, risks, controls and residual-risk decisions
• Produce a 90-day responsible AI adoption plan for a technology team

Benefits of attending

Target competencies

Who should attend

• IT Managers — who need consistent controls for AI use across delivery, support and operations teams
• Software Developers and Engineering Leads — who use AI coding assistants and must maintain code quality, security and accountability
• Data Analysts and BI Professionals — who need to validate AI-generated analysis and protect governed data
• Digital Product Managers — who assess AI features, suppliers and user impacts before release
• Service Desk and IT Operations Leads — who are introducing AI-assisted triage, summarisation and knowledge retrieval
• Technology Risk, Security and Compliance Professionals — who need practical evidence for proportionate AI controls

Requirements and prerequisites

Participants should be comfortable working in a technology, data or digital role and should understand everyday concepts such as cloud applications, access permissions, confidential data, software testing or data quality. Familiarity with tools such as ChatGPT, Microsoft Copilot or an AI coding assistant is useful, but not essential; a short pre-course orientation explains the interfaces used in class. No programming, statistics, machine-learning mathematics, model training experience or prior AI governance qualification is required. Complete beginners to AI should expect to work from practical workplace scenarios rather than build or tune models.

Training methodology

The instructor uses concise concept sessions to establish a common vocabulary, then moves quickly into guided application. Participants test prompts in Microsoft Copilot or ChatGPT Enterprise-style scenarios, compare reliable and unreliable outputs, and assess cases involving code assistants, service-desk records and internal knowledge bases. Small groups complete risk classification and control-design exercises using NIST AI RMF and ISO/IEC 42001 concepts. Each day adds a section to an individual AI use-case assessment pack, which is peer-reviewed and refined into a practical 90-day action plan on day five.

Course outline

Day 1: AI foundations for technology work

• Predictive AI, generative AI and agentic workflow distinctions
• Large language model inputs, tokens and probabilistic output
• Retrieval-augmented generation and enterprise knowledge sources
• Common technology-team use cases for AI assistance
• Hallucination, non-determinism and automation bias
• AI-assisted coding, analysis and support workflow boundaries
• Value-versus-risk framing for AI experimentation

Workshop: Participants map one current team task to an AI use-case canvas and identify the intended value, users, inputs and decision boundaries.

Day 2: Prompting and output assurance

• Structured prompt components: role, context, task, constraints and format
• Few-shot examples and reusable prompt templates
• Prompt injection and untrusted-content handling
• Source attribution and evidence-requesting techniques
• Output validation for technical accuracy and completeness
• Bias, harmful content and misleading confidence indicators
• Human-in-the-loop approval and escalation checkpoints

Workshop: Participants design, test and revise a bounded prompt for a technical work task, then produce an output-review checklist.

Day 3: Data, security and responsible use

• Data classification for AI prompts, files and connectors
• Personal data, confidential information and intellectual property exposure
• Source code, secrets and credential leakage scenarios
• Identity, access control and enterprise AI tenant considerations
• Data retention, model training and supplier terms review
• Security threats including prompt injection and data exfiltration
• Acceptable-use rules for public and enterprise AI tools

Workshop: Participants assess a data-sharing scenario involving an AI support assistant and produce a permitted, restricted or prohibited data-handling decision.

Day 4: AI governance and risk controls

• NIST AI RMF Govern, Map, Measure and Manage functions
• ISO/IEC 42001 AI management system principles
• AI use-case inventory and accountable owner assignment
• Impact assessment for users, operations and business decisions
• Risk register structure, likelihood scoring and control selection
• Supplier due diligence questions for AI products and services
• Monitoring, incident reporting and change-control requirements

Workshop: Teams complete a risk register and control set for an AI-enabled internal knowledge-search or coding-assistant use case.

Day 5: Applying responsible AI in the team

• Prioritising AI use cases by value, feasibility and risk
• Pilot design with success measures and stop criteria
• Workflow redesign around review, exception and escalation paths
• Roles for product, engineering, security, legal and data governance
• Communicating AI limitations to users and decision-makers
• Creating team guidance, training and adoption communications
• Ninety-day implementation roadmap and governance checkpoints

Workshop: Participants present their completed AI use-case assessment pack and produce a 90-day responsible adoption plan with named actions, owners and review dates.

Tools & standards covered

Microsoft Copilot, OpenAI 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