AI-Enabled Stakeholder Engagement Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Digital Transformation Managers, Product Managers and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 7 Nov 2026 Course code SD-DT-029

Before you join: Participants should have experience contributing to a digital, data, automation or AI initiative, or supporting stakeholders affected by technology change.

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

AI programmes can fail to gain acceptance when affected groups are consulted too late, communications overstate system capability, or feedback does not reach product and governance decisions. Banks introducing automated credit triage, public bodies deploying citizen-service assistants, and multinationals using workforce analytics all face scrutiny from users, employees, regulators, suppliers and senior decision-makers. Effective engagement requires more than stakeholder lists: it requires evidence, clear commitments, documented responses and measurable follow-through.

This course equips participants to plan and run stakeholder engagement for AI-enabled products, services and internal systems. Participants map stakeholders using the Stakeholder Salience Model, define participation commitments through the IAP2 Spectrum, gather and analyse stakeholder evidence, and connect concerns to AI risk, governance and delivery decisions. They learn to communicate model limits, data practices, uncertainty, human oversight and routes for challenge in language suited to each audience. The course also addresses feedback loops, consultation records, grievance handling, vendor obligations and post-deployment monitoring.

Teaching combines short instructor briefings with realistic cases, group analysis, facilitated simulations and structured application planning. Participants work with scenarios such as a bank’s AI-assisted customer onboarding process, a government eligibility decision service and an employee-facing generative AI tool. They leave with an AI stakeholder engagement plan, stakeholder map, engagement charter, communication matrix, risk-linked feedback register and 90-day implementation plan. The course is delivered over five days in a classroom in 50+ cities or live online, with approximately 32.5 contact hours and a certificate of completion.

What you will be able to do

  • Map AI stakeholders using power, legitimacy, urgency, impact and decision-rights criteria
  • Segment affected groups and select participation methods using the IAP2 Spectrum
  • Design consultation plans with clear objectives, channels, commitments and feedback routes
  • Conduct stakeholder research using interviews, focus groups, surveys and thematic analysis
  • Apply NIST AI RMF and ISO/IEC 42001 concepts to stakeholder risks and governance actions
  • Produce audience-specific AI communications, including model cards, disclosures and response messages
  • Establish traceable feedback loops linking stakeholder concerns to requirements, controls and decisions
  • Measure engagement quality through representation, trust, response-time and closure indicators

Benefits of attending

For you

  • Produce defensible stakeholder engagement plans for AI initiatives
  • Handle AI concerns about fairness, privacy, accuracy and human oversight
  • Translate technical model information into audience-specific communications
  • Connect consultation findings to governance decisions and product backlogs
  • Lead evidence-based conversations with executives, users, regulators and delivery teams

For your organisation

  • Earlier identification of stakeholder harms, objections and adoption barriers
  • Clearer evidence trails for AI governance reviews and regulatory enquiries
  • Better informed product requirements, controls and implementation decisions
  • More credible communication about AI use, limits and accountability
  • Reduced rework caused by late-stage stakeholder escalation or resistance

Course modules

10 modules over 5 days

Participants can identify and prioritise stakeholders affected by an AI initiative.

  • Stakeholder salience model using power, legitimacy and urgency attributes
  • Mapping decision rights, information needs and influence pathways for AI initiatives
  • Distinguishing affected users, decision-makers, sponsors, regulators and data subjects
  • Persona evidence drawn from interviews, service data and operational observations
  • Stakeholder registers linking interests, concerns, commitments and contact ownership
  • Bias and harm concerns framed from each stakeholder perspective

Participants can define AI use cases and explain their stakeholder implications clearly.

  • AI system lifecycle touchpoints from problem framing through monitoring
  • Use-case boundaries stated through decisions, users, data and intended outcomes
  • Materiality assessment ranks stakeholder impacts by severity, scale and reversibility
  • Data-flow mapping identifies collection, inference, sharing and retention touchpoints
  • Human-in-the-loop roles clarified for approvals, exceptions and escalation
  • Plain-language AI explanations matched to audience knowledge and decision stakes

Participants can select proportionate engagement methods and set clear participation commitments.

  • IAP2 Spectrum participation levels and commitments, from informing to shared decisions
  • Engagement objectives separate awareness, input, consent, adoption and accountability
  • Stakeholder segmentation combines salience, digital access, trust and impact exposure
  • Channel selection weighs confidentiality, accessibility, language, urgency and feedback quality
  • Engagement cadence aligns discovery, design, deployment and post-release monitoring
  • Consultation charters define scope, decision influence, response times and recordkeeping

Participants can gather credible stakeholder evidence and convert it into usable findings.

  • Question design elicits concerns about accuracy, fairness, privacy and recourse
  • Semistructured interviews probe workflows, exceptions, incentives and trust breakdowns
  • Focus-group protocols manage power differences, sensitive disclosures and dominant voices
  • Survey sampling plans test representation, nonresponse risk and subgroup comparability
  • Sentiment analysis limits documented before interpreting open-text stakeholder feedback
  • Qualitative coding uses thematic analysis, codebooks and inter-rater checks

Participants can connect stakeholder concerns to AI risk controls, assurance and escalation.

  • NIST AI RMF functions translated into stakeholder communication commitments
  • ISO/IEC 42001 roles connected to consultation, documentation and management review
  • Risk registers record stakeholder harms, controls, owners, indicators and review dates
  • Impact assessment distinguishes intended benefits, foreseeable misuse and residual risk
  • Escalation thresholds define when concerns reach risk committees or executives
  • Grievance mechanisms specify reporting routes, investigation stages and remedy communications

Participants can create clear AI communications for different stakeholder groups.

  • Audience-message matrices match evidence, uncertainty, action and communication channel
  • Model cards communicate intended use, limits, evaluation results and cautions
  • Data sheets document dataset provenance, consent, gaps and known quality issues
  • Explainability narratives distinguish system rationale from individual decision justification
  • Uncertainty disclosures state confidence limits, assumptions and monitoring commitments
  • Plain-language testing detects ambiguity, jargon, misleading claims and inaccessible wording

Participants can assign engagement accountabilities and manage contested AI decisions.

  • RACI matrices assign engagement decisions, approvals, communications and issue ownership
  • Cross-functional governance joins product, legal, data, security and operations
  • Decision logs capture alternatives, stakeholder input, rationale and accountable approvers
  • Conflict-resolution protocols separate interests, evidence, authority and negotiated options
  • Consent and consultation records support audit trails and regulatory enquiries
  • Vendor governance clauses set disclosure, incident notification and participation duties

Participants can convert stakeholder feedback into tracked delivery and change actions.

  • Feedback loops convert consultation findings into backlog items and control changes
  • Traceability matrices link stakeholder commitments to requirements, tests and owners
  • Prioritisation uses impact-effort scoring, risk severity and dependency analysis
  • Closed-loop responses explain decisions, rejected requests and next review points
  • Change-management messages prepare teams for altered workflows, roles and controls
  • Adoption metrics track usage, confidence, workarounds and support demand

Participants can measure engagement performance and identify emerging stakeholder concerns.

  • Engagement KPIs measure reach, representation, response time, trust and closure
  • Equity checks compare participation and outcomes across affected stakeholder groups
  • Dashboard definitions specify sources, refresh cycles, owners and decision audiences
  • Leading indicators flag emerging concerns before formal complaints or incidents
  • Post-deployment monitoring combines feedback, performance drift and harm signals
  • Review cycles trigger communications, retraining, control updates or suspension decisions

Participants can prepare a practical plan to apply stakeholder engagement methods after the course.

  • Engagement roadmaps sequence stakeholders, milestones, channels, owners and dependencies
  • First-90-day plans prioritise discovery, governance setup and early feedback loops
  • Action registers convert course findings into dated commitments and escalation routes
  • Stakeholder briefings present evidence, uncertainties, choices and requested decisions
  • Implementation risks assessed through likelihood, impact, owner and contingency fields
  • Personal application plans define transfer actions, sponsors and review dates

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

What you take back to work

  1. AI stakeholder register template
  2. IAP2-based engagement charter
  3. Stakeholder communication matrix
  4. AI concern and feedback register
  5. Stakeholder-to-control traceability matrix
  6. 90-day AI engagement plan

Who should attend

  • Digital Transformation Managers — responsible for adoption and stakeholder acceptance of AI change
  • Product Managers — need stakeholder evidence to shape AI product requirements and release decisions
  • AI Governance and Risk Managers — need consultation records linked to controls and accountability
  • Data and AI Leaders — need credible communication of model use, limits and monitoring
  • Change Managers — need practical engagement methods for AI-enabled workflow changes
  • Public Affairs, Compliance and Customer Experience Managers — need defensible responses to stakeholder concerns

Skills and tools covered

Stakeholder mappingAI risk communicationConsultation designFeedback traceabilityParticipation measurementAI governance coordination

Tools and standards

Stakeholder Salience Model, IAP2 Spectrum, NIST AI Risk Management Framework, ISO/IEC 42001

How the course runs

Training methodology

The course uses short instructor briefings to introduce methods, followed by case work, group exercises, simulations and application planning. Participants analyse stakeholder situations from banking, public services, employee technology and customer-facing AI. They practise mapping influence and impact, designing consultation approaches, responding to difficult stakeholder concerns and converting feedback into delivery actions. Facilitated discussions examine trade-offs between speed, transparency, confidentiality, inclusion and governance. Each participant develops materials that can be adapted to an active or planned AI initiative.

Requirements and prerequisites

Participants should have experience contributing to a digital, data, automation or AI initiative, or supporting stakeholders affected by technology change. Familiarity with basic project delivery, business processes and stakeholder communication is assumed. Participants should understand, at a high level, how AI systems use data to produce predictions, recommendations or generated content. Coding, data science, mathematical modelling and prior AI governance certification are not required. The course explains relevant governance concepts before applying them to engagement decisions.

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

Participants need experience with a digital, data, automation or AI-related initiative and a basic understanding of stakeholder communication. Technical AI development, coding and formal AI governance qualifications are not required.

Participants should bring details of an active or planned AI initiative, where confidentiality permits. Useful information includes intended users, affected groups, known concerns, governance arrangements and planned deployment dates.

The course suits managers responsible for AI adoption, product delivery, change, governance, risk, compliance, customer experience or public engagement. It is particularly relevant where AI decisions affect customers, employees, citizens or regulated populations.

AI governance courses usually focus on policy, risk, controls and accountability structures. This course concentrates on how to identify affected stakeholders, conduct meaningful engagement, communicate AI decisions and track responses into delivery work.

Participants can use the templates to prepare an engagement plan for a current AI use case, product release or governance review. The methods also support consultation before procurement, piloting, deployment and major model changes.

The programme contains ten modules delivered in teaching order across five days, with about 32.5 contact hours from 08:30 to 16:30. Time is divided between instruction, case analysis, group work, simulations and application planning.

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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