Generative AI for Workplace Productivity Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Business Analysts, Managers and Team Leaders and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-AI-071

Before you join: Participants should be comfortable using office productivity software, web-based business applications and structured documents such as reports, spreadsheets or presentations.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Generative AI can reduce time spent drafting routine communications, summarising long documents, preparing meetings, analysing structured data and finding information. It can also introduce factual errors, fabricated citations, privacy exposures and inconsistent decisions when staff use it without clear methods or controls. Employers need people who can identify appropriate use cases, direct AI systems precisely, check outputs rigorously and retain accountable human judgement.

This course develops practical methods for using generative AI in knowledge work. Participants learn prompt design, research verification, AI-assisted writing, spreadsheet and SQL support, meeting productivity, workflow design and output evaluation. They apply frameworks including the CLEAR prompt pattern, RACI decision rights, the Pyramid Principle and NIST AI RMF. The course addresses how to distinguish useful drafting support from tasks requiring specialist review, source verification or formal approval.

Teaching combines short instructor briefings, realistic cases, group analysis, guided tool use, output-review simulations and individual application planning. Cases reflect common work in banking, government, NGOs, professional services and corporate functions, such as preparing policy briefings, analysing operational data, drafting stakeholder updates and documenting actions from meetings. Participants leave with a reusable prompt library, AI use-case assessment, verification checklist, workflow design and 90-day workplace adoption plan. A certificate of completion is issued to participants who complete the course.

The course is suited to professionals who already use digital workplace tools and need a disciplined, repeatable approach to generative AI for everyday work. It is particularly relevant where information quality, confidentiality, traceability and approval controls matter.

What you will be able to do

  • Assess workplace tasks using value, risk and readiness criteria
  • Design reusable prompts using context, constraints, examples and output schemas
  • Produce AI-assisted documents using approved facts, templates and style guides
  • Validate AI outputs through source triangulation, control totals and structured review
  • Build AI-assisted workflow designs with approval gates and fallback procedures
  • Apply NIST AI RMF principles to workplace generative AI use cases
  • Establish evaluation scorecards for quality, productivity and residual risk
  • Develop a 90-day generative AI adoption plan for a defined work area

Benefits of attending

For you

  • Produce first drafts, summaries and action registers faster with controlled prompting
  • Recognise hallucinations, weak evidence and privacy risks before sharing outputs
  • Create reusable prompt patterns for recurring reporting and communication tasks
  • Discuss AI use cases credibly with managers, security teams and technical specialists
  • Build a practical adoption plan for improving an identified work process

For your organisation

  • Reduce time spent on routine drafting, meeting follow-up and document synthesis
  • Improve consistency of AI-assisted outputs through shared prompts and review criteria
  • Lower information-security and privacy exposure through approved-use boundaries
  • Create auditable controls for AI-supported research, analysis and communications
  • Prioritise AI pilots using measurable productivity, quality and risk criteria

Course modules

10 modules over 5 days

Participants can identify suitable generative AI tasks and assign appropriate human accountability.

  • Generative AI, predictive AI and automation distinguished through workplace task examples
  • Transformer models, tokens, context windows and retrieval explained for users
  • Identify high-value, low-risk tasks using frequency, effort and error criteria
  • Classify outputs as drafting, summarisation, extraction, analysis or generation
  • Human-in-the-loop responsibility assigned with RACI decision rights for review
  • Model limitations: hallucinations, staleness, bias and prompt sensitivity

Participants can construct, test and reuse prompts for defined workplace tasks.

  • Use the CLEAR prompt pattern: context, limits, examples, action, review
  • Specify audience, purpose, tone, format and acceptance criteria explicitly
  • Few-shot examples calibrate extraction, classification and structured response formats
  • Chain-of-thought alternatives use concise reasoning requests without exposing sensitive data
  • Iterate prompts through draft, critique, revision and quality-check cycles
  • Create reusable prompt blocks with variables, constraints and output schemas

Participants can use generative AI for research while verifying evidence and documenting decisions.

  • Frame research questions with scope, source thresholds and decision purpose
  • Search planning separates discovery, verification, synthesis and citation tasks
  • Apply source triangulation across primary documents, reputable reporting and databases
  • Request claim-evidence tables containing links, dates, confidence and gaps
  • Detect fabricated citations, unsupported claims and stale information systematically
  • Document human verification decisions in an auditable research log

Participants can produce accurate, audience-appropriate written outputs with AI assistance.

  • Convert briefing notes into structured emails, reports and executive summaries
  • Apply Pyramid Principle headings, message maps and reader-specific framing
  • Generate first drafts from approved facts, templates and style guides
  • Edit AI text for accuracy, voice, accessibility and plain language
  • Use red-team prompts to find ambiguity, omissions and unsupported assertions
  • Maintain version control between source material, prompts and final documents

Participants can use AI-assisted analysis while checking calculations, logic and data handling.

  • Prepare tabular data with field definitions, missing-value rules and identifiers
  • Ask for spreadsheet formulas, pivot summaries and exception flags safely
  • Generate SQL drafts, then validate joins, filters and aggregations
  • Interpret descriptive statistics, trends and outliers before drafting conclusions
  • Reconcile AI calculations against control totals and independent methods
  • Protect confidential datasets through minimisation, masking and approved environments

Participants can apply AI to meeting preparation, follow-up and targeted stakeholder updates.

  • Transform meeting agendas into decision-focused facilitation plans and question sets
  • Generate pre-read summaries with risks, actions and unresolved decisions
  • Structure meeting notes using decisions, owners, deadlines and dependencies
  • Extract action registers from transcripts while preserving accountability and context
  • Draft stakeholder updates tailored to operational, technical and executive audiences
  • Review transcript permissions, retention periods and sensitive participant disclosures

Participants can design controlled AI-assisted workflows for repetitive knowledge-work processes.

  • Map repetitive work using trigger, inputs, transformation, output and owner
  • Separate AI-assisted steps from deterministic rules and approval gates
  • Design prompt-to-process handoffs with structured fields and exception queues
  • Specify acceptance tests for generated content before downstream release
  • Estimate time saved, rework reduced and control effort introduced
  • Document fallback procedures when models fail, disagree or become unavailable

Participants can apply governance controls to generative AI use involving sensitive information.

  • Classify information using public, internal, confidential and restricted handling levels
  • Apply NIST AI RMF functions: govern, map, measure and manage
  • Assess privacy risks from prompts, uploads, retention and third-party processing
  • Define approved-use boundaries for customer data, personnel records and contracts
  • Create disclosure language for AI-assisted documents and stakeholder communications
  • Escalate high-impact outputs through legal, security and subject-matter review

Participants can measure AI output quality, operational benefit and residual risk.

  • Build evaluation sets from representative tasks, edge cases and known answers
  • Score outputs against accuracy, completeness, relevance, tone and citation criteria
  • Compare model responses using blind review and weighted scorecards
  • Measure productivity with baseline time, quality defects and rework rates
  • Track prompt versions, model settings and source-data dependencies
  • Report residual risks, exceptions and improvement actions to process owners

Participants can prepare a practical plan for piloting and scaling a defined AI use case.

  • Prioritise use cases with impact, feasibility, risk and readiness scoring
  • Draft a 90-day adoption plan with owners, milestones and measures
  • Define pilot scope, participant groups, controls and success thresholds
  • Create communications that set expectations about review and accountability
  • Establish office hours, prompt libraries and peer-review routines
  • Present a use-case rationale using evidence, controls and implementation steps

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. CLEAR prompt library
  2. AI use-case assessment matrix
  3. Research verification checklist
  4. AI-assisted workflow design template
  5. Output quality scorecard
  6. 90-day AI adoption plan

Who should attend

  • Business Analysts — need faster research, requirements drafting and structured analysis
  • Managers and Team Leaders — need controlled AI use across team processes
  • Policy and Programme Officers — need reliable briefings, summaries and stakeholder communications
  • Operations Professionals — need to reduce repetitive documentation and coordination work
  • Data and Reporting Analysts — need safe AI support for spreadsheets, SQL and insight drafting
  • Information Governance Professionals — need practical controls for staff AI use

Skills and tools covered

Prompt engineeringOutput verificationAI-assisted writingWorkflow designAI risk managementProductivity measurement

Tools and standards

ChatGPT Enterprise, Microsoft Copilot, Gemini for Google Workspace, NIST AI RMF 1.0

How the course runs

Training methodology

The course uses short briefings to establish methods, followed by realistic cases, group exercises, tool demonstrations, output-review simulations and individual application planning. Participants work with examples such as executive briefings, policy documents, operational datasets, meeting transcripts and customer-facing communications. Instructor feedback focuses on prompt precision, evidence quality, confidentiality, approval controls and measurable productivity. Activities can be adapted to the organisation's sector, approved AI tools and internal information-handling rules.

Requirements and prerequisites

Participants should be comfortable using office productivity software, web-based business applications and structured documents such as reports, spreadsheets or presentations. Experience of drafting emails, preparing meeting notes, conducting basic research or analysing operational information is useful. Participants should understand their organisation's normal confidentiality and approval requirements. No programming, data science, machine learning or prior prompt-engineering experience is required. The course explains technical concepts from a workplace-user perspective, while expecting participants to assess outputs critically rather than treat AI responses as authoritative.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

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

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

You should be comfortable with standard office tools and routine knowledge-work tasks such as writing, research, meetings or spreadsheet reporting. No programming, data science or previous AI training is required.

Bring a laptop if your delivery venue permits it, plus examples of non-confidential work processes you want to improve. Do not bring customer data, personnel records, restricted documents or any information prohibited by your organisation's policies.

It suits managers, analysts, operations staff, policy professionals, reporting teams and information-governance personnel. It is designed for people applying generative AI to knowledge work rather than building AI models.

This course focuses on practical workplace use, prompt design, output checking, process controls and adoption planning. It does not teach model development, Python programming, neural-network engineering or advanced machine learning.

You will leave with prompt patterns, review checklists and a 90-day plan for a selected work process. These can be adapted to approved tools, local policies and the information types handled by your team.

The five days provide about 32.5 contact hours, combining instructor input, case analysis, group exercises, simulations and planning. Modules progress from task selection and prompting through writing, data, governance, evaluation and implementation.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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