Analytics and AI for Strategic Management Training Course

Duration 5 days 10 modules · 33 contact hours
Level Advanced Certificate on completion
Who attends Strategy Directors, Business Unit Leaders 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-SP-054

Before you join: Participants should have management experience and regular exposure to strategic planning, performance reporting, investment decisions, transformation programmes or business cases.

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

Senior managers increasingly face strategic decisions shaped by volatile demand, regulatory change, supply constraints, customer behaviour and rapid AI adoption. Yet many strategy processes still rely on retrospective reports, untested assumptions and dashboards that do not clarify which decisions require action. This course addresses the practical challenge of turning organisational data, analytical methods and AI capabilities into better choices about growth, investment, risk, operating models and resource allocation.

Participants learn to frame decision questions, assess data fitness, build meaningful KPIs and interpret descriptive, predictive and prescriptive analytics. The course covers scenario planning, forecasting, optimisation, AI use-case assessment, model risk, data governance and benefits realisation. Participants learn when a spreadsheet analysis is sufficient, when predictive modelling adds value, and when an AI application introduces control requirements that outweigh likely benefits. They also learn to present analytical findings in decision-ready formats for executives and governance forums.

Instruction combines short faculty briefings with cases drawn from banking, public services, consumer businesses, infrastructure and multinational operations. Participants work with decision scenarios, datasets, scenario assumptions, AI use-case assessments and portfolio choices. They leave with a reusable Strategic Analytics and AI Action Plan, supported by templates for decision framing, KPI design, scenario analysis, AI governance and initiative prioritisation. The five-day instructor-led course is available in classrooms in more than 50 cities or live online, with a certificate of completion.

The course is designed for experienced managers, strategy professionals, business leaders and transformation sponsors who need to commission, assess or govern analytics and AI work rather than become data scientists.

What you will be able to do

  • Frame strategic decisions using value-driver trees, assumptions and measurable outcomes.
  • Assess data quality, lineage and stewardship requirements for strategic analyses.
  • Design KPI trees and executive dashboards that distinguish signals from operational noise.
  • Apply scenario planning, forecasting and sensitivity analysis to strategic uncertainty.
  • Interpret predictive models using validation measures, error costs and feature importance.
  • Evaluate AI use cases through value hypotheses, feasibility criteria and control requirements.
  • Prioritise analytics and AI initiatives using weighted scoring and portfolio roadmaps.
  • Present analytical recommendations through decision memos, executive narratives and action plans.

Benefits of attending

For you

  • Make strategic recommendations with clearer evidence, assumptions and quantified trade-offs
  • Challenge analytical claims using validation, error-cost and data-quality tests
  • Assess whether AI proposals merit investment, piloting, controls or rejection
  • Present complex findings in concise executive decision memos
  • Build a credible roadmap for analytics and AI career responsibilities

For your organisation

  • Improves strategic decisions through explicit assumptions, evidence and scenario testing
  • Reduces investment waste by prioritising feasible, high-value analytics and AI initiatives
  • Strengthens oversight of model bias, privacy, accountability and operational risk
  • Connects data programmes to measurable business outcomes and accountable owners
  • Creates common methods for evaluating AI proposals across business functions

Course modules

10 modules over 5 days

Participants can define strategic decisions and prioritise analytical work against business value.

  • Decision framing separates strategic choices from operational performance questions
  • Strategy maps link value drivers, assumptions, and measurable outcomes
  • Data inventory identifies internal records, external signals, ownership, and refresh cycles
  • Analytical maturity assessment exposes gaps in data, skills, governance, and adoption
  • Problem statements use SMART and MECE tests for decision usefulness
  • Value-versus-feasibility matrices prioritise analytics initiatives against strategic objectives

Participants can assess whether available data is fit for strategic analysis and decision use.

  • Data lineage traces critical metrics from source systems to board reports
  • Completeness, accuracy, timeliness, consistency, and uniqueness tests profile strategic datasets
  • Master data definitions resolve conflicting customer, product, and geographic measures
  • Outlier analysis distinguishes data errors from meaningful operational variation
  • Sampling plans assess data reliability before modelling high-consequence decisions
  • Data stewardship roles assign accountability for definitions, access, and remediation
  • Privacy-by-design controls classify personal data and restrict analytical use

Participants can design performance measures that identify drivers of strategic outcomes.

  • KPI trees decompose enterprise goals into leading and lagging measures
  • Balanced Scorecard links financial, customer, process, and capability measures
  • Variance analysis separates volume, price, mix, and productivity effects
  • Cohort analysis compares retention and value patterns across customer groups
  • Pareto charts focus management attention on material causes and segments
  • Control charts distinguish common-cause variation from special-cause performance shifts
  • Executive dashboards apply signal-to-noise principles and decision thresholds

Participants can develop scenarios and early-warning indicators for uncertain strategic conditions.

  • PESTLE and Porter's Five Forces structure external strategic evidence
  • Scenario planning defines focal issue, driving forces, uncertainties, and signposts
  • Cross-impact matrices test interactions among technological, regulatory, and market uncertainties
  • Trend extrapolation combines historical rates with explicit assumption ranges
  • Early-warning indicators trigger review when scenario signposts change direction
  • War-gaming tests competitor responses to pricing, entry, and capacity moves

Participants can interpret predictive analysis and judge its suitability for management decisions.

  • CRISP-DM frames business understanding, data preparation, modelling, evaluation, and deployment
  • Regression models estimate drivers of revenue, demand, cost, or attrition
  • Classification models rank cases by probability of churn or default
  • Time-series forecasting uses seasonality, trend, and forecast-error measures
  • Confusion matrices expose false-positive and false-negative decision costs
  • Train-test splits and cross-validation check model generalisation before deployment
  • Feature importance translates predictive outputs into management-relevant explanations

Participants can compare strategic options using probability, financial value and constrained-resource methods.

  • Decision trees compare strategic options under uncertain outcomes and probabilities
  • Expected monetary value quantifies choices using probabilities, payoffs, and costs
  • Sensitivity analysis identifies assumptions that materially change recommended actions
  • Monte Carlo simulation produces probability distributions for investment outcomes
  • Optimisation models allocate constrained budgets, capacity, or inventory against objectives
  • Real-options logic values staged commitments, deferral, expansion, and abandonment
  • Decision rules specify thresholds, owners, escalation paths, and response timing

Participants can identify, assess and design appropriate generative AI applications for strategic work.

  • Generative AI use cases distinguish drafting, synthesis, classification, and decision support
  • Prompt structures define role, context, constraints, evidence, and output format
  • Retrieval-augmented generation grounds responses in approved internal knowledge sources
  • Human-in-the-loop review assigns verification before consequential management decisions
  • Benchmark tasks compare accuracy, consistency, latency, cost, and traceability
  • Process redesign identifies handoffs removed, controls added, and roles changed
  • AI opportunity canvases connect use cases with strategic value pools

Participants can specify governance and assurance controls for analytics and AI decisions.

  • NIST AI RMF structures govern, map, measure, and manage activities
  • ISO/IEC 42001 identifies AI management-system responsibilities and documented controls
  • Model risk registers record purpose, limitations, owners, controls, and review dates
  • Bias testing examines disparate outcomes across relevant protected or customer groups
  • Explainability requirements match model complexity to decision consequences and stakeholders
  • Data protection impact assessments address privacy risks before AI deployment
  • Incident response plans define detection, containment, notification, and corrective actions

Participants can build an investment portfolio and delivery roadmap for analytics and AI initiatives.

  • Use-case intake captures decision owner, value hypothesis, data, and constraints
  • Benefit cases separate revenue, cost, risk, service, and capability impacts
  • Weighted scoring ranks initiatives by strategic fit, value, feasibility, and risk
  • Portfolio roadmaps sequence foundations, pilots, scale-up, and retirement decisions
  • Stage-gate governance releases funding against evidence, controls, and adoption milestones
  • Product operating models clarify sponsor, product owner, data owner, and users
  • Benefits realisation plans define baselines, targets, measurement cadence, and accountability

Participants can communicate analytical recommendations and create a focused implementation plan.

  • Executive narratives connect analytical findings, assumptions, risks, and recommended decisions
  • Data storytelling uses message hierarchy, annotated visuals, and evidence-based claims
  • Decision memos state options, trade-offs, recommendation, and required commitments
  • Stakeholder maps identify supporters, challengers, approvers, and affected operational teams
  • ADKAR planning addresses awareness, desire, knowledge, ability, and reinforcement
  • Operating reviews track adoption, realised benefits, model performance, and emerging risks
  • 90-day action plans assign priorities, owners, milestones, and review dates

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

What you take back to work

  1. Strategic decision-framing template
  2. KPI tree and executive dashboard specification
  3. Scenario planning and early-warning indicator template
  4. AI use-case assessment canvas
  5. Analytics and AI governance checklist
  6. 90-day Strategic Analytics and AI Action Plan

Who should attend

  • Strategy Directors — need evidence-based methods for growth, investment and market choices
  • Business Unit Leaders — need to convert performance data into strategic priorities
  • Transformation Leaders — need to select and govern analytics and AI initiatives
  • Chief Data Office Managers — need alignment between data programmes and business strategy
  • Risk and Governance Managers — need controls for consequential analytical and AI decisions
  • Senior Finance Managers — need stronger forecasts and investment cases for strategic decisions

Skills and tools covered

Strategic decision framingScenario planningPredictive analytics interpretationAI governancePortfolio prioritisationExecutive data storytelling

Tools and standards

Balanced Scorecard, CRISP-DM, NIST AI RMF, ISO/IEC 42001

How the course runs

Training methodology

Faculty-led briefings introduce each method before participants apply it to realistic strategic cases. Individual and group work uses decision scenarios, KPI packs, forecasting outputs, investment choices and AI use-case proposals. Simulations test strategic options under changing assumptions, competitor actions and risk events. Facilitated discussions focus on the quality of evidence, assumptions, governance and executive communication. Participants consolidate learning through structured application planning linked to a live organisational priority.

Requirements and prerequisites

Participants should have management experience and regular exposure to strategic planning, performance reporting, investment decisions, transformation programmes or business cases. They should be comfortable reading tables, charts and basic financial or operational metrics. Familiarity with spreadsheets and organisational data is useful. The course does not require programming, statistics qualifications, data-science experience or prior use of AI tools. Participants will gain most value by bringing a current strategic question, proposed AI initiative or performance challenge from their own organisation.

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 have management experience and familiarity with business performance measures, planning or investment decisions. Programming, advanced statistics and prior AI implementation experience are not required.

Bring a current strategic priority, proposed AI initiative, performance issue or investment question if you can. You will be able to apply the course templates to this context during application planning.

It is intended for senior managers, strategy teams, transformation leaders, data office managers, finance leaders and governance professionals. It is particularly relevant for people who commission or assess analytics and AI work.

This course focuses on strategic decision quality, business value, governance and executive use of analytics and AI. It explains how to interpret and govern analytical work rather than teaching participants to code models.

Participants receive decision-framing, prioritisation, governance and action-planning templates that can be used in strategy reviews and investment proposals. The methods also help managers challenge unclear assumptions and unsupported AI claims.

Each training day runs from 08:30 to 16:30 and provides about 6.5 contact hours. The course progresses through ten modules covering decision framing, analytics, scenarios, AI, governance, portfolio management and implementation 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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