Artificial Intelligence for Business Training Course

Participants can identify and rank AI opportunities using structured business criteria.

• Process pain points translated into candidate AI use cases
• Value-feasibility-risk matrix used to rank proposed AI opportunities
• Jobs-to-be-Done framing clarifies users, triggers and desired outcomes
• Prioritisation scores combine strategic fit, data readiness and implementation effort
• Low-risk pilot candidates separated from high-consequence decision applications
• Use-case backlogs documented with owners, assumptions and decision deadlines

Participants can write testable AI problem statements and measurable success criteria.

• SMART problem statements connect operational pain to intended AI outcomes
• Baseline measures established before estimating AI-related performance improvements
• Leading and lagging indicators selected for service and control objectives
• Acceptance criteria define accuracy, timeliness, explainability and escalation requirements
• Cost-of-error analysis distinguishes false positives from false negatives
• Benefits hypotheses recorded with assumptions, owners and measurement methods

Participants can assess whether available data is suitable for an AI initiative.

• Data inventories identify source systems, fields, owners and refresh frequencies
• Completeness, accuracy, consistency and timeliness assessed using data-quality dimensions
• Data lineage traces inputs from source capture through model consumption
• Training, validation and test datasets separated to prevent information leakage
• Representative samples checked for coverage gaps and historical distortions
• Data access requirements mapped to roles, purposes and retention periods

Participants can select appropriate machine-learning approaches for common business decisions.

• Supervised learning predicts known outcomes from labelled historical records
• Classification models assign categories such as fraud, churn or approval
• Regression estimates numerical values including demand, cost and duration
• Unsupervised clustering identifies customer segments and unusual transaction patterns
• Feature selection links business variables to model inputs and governance
• Training data quality determines practical limits on predictive performance

Participants can specify safe and useful generative AI applications for knowledge work.

• Large language models generate text by predicting probable token sequences
• Prompt structures define role, task, context, constraints and output format
• Retrieval-augmented generation grounds responses in approved enterprise knowledge sources
• Hallucination controls include citations, abstention rules and human review
• Document classification, extraction and summarisation compared for suitable workflows
• Sensitive prompts screened for confidential, personal and regulated information

Participants can evaluate non-text AI methods for operational processes and customer services.

• Computer vision detects objects, defects and document elements from images
• Optical character recognition extracts printed text from scanned records
• Speech-to-text supports call transcription, quality assurance and case documentation
• Entity extraction identifies names, dates, amounts and policy references
• Confidence scores determine routing between automation and human review
• Input quality standards address image resolution, noise and language variation

Participants can compare AI delivery approaches and define a proportionate solution design.

• Build-buy-partner options compared against capability, speed, control and cost
• API-based AI services assessed for data handling, availability and integration
• Workflow architecture maps triggers, inputs, decisions, actions and exception queues
• Human-in-the-loop controls assigned at review, approval and escalation points
• Minimum viable product scope limits variables, users and decision consequences
• Architecture assumptions documented for security, scale, latency and support

Participants can interpret model performance measures and set business-appropriate thresholds.

• Confusion matrices show correct and incorrect classification outcomes
• Precision and recall selected according to operational error consequences
• ROC curves compare classification thresholds across alternative model settings
• Calibration checks whether predicted probabilities match observed outcome rates
• Error analysis groups failures by customer, product, channel and circumstance
• Business validation tests model outputs against process acceptance criteria

Participants can identify fairness, transparency and accountability requirements for AI systems.

• Fairness objectives defined before selecting metrics or protected-group comparisons
• Sampling bias, measurement bias and proxy bias traced through data processes
• Disparate impact analysis tests outcome differences across relevant populations
• Explainability needs vary by decision consequence, user role and regulation
• Human oversight procedures specify authority, competence and intervention triggers
• Appeal and redress routes address contested AI-supported decisions

Participants can apply formal governance controls across an AI system lifecycle.

• NIST AI RMF functions organise govern, map, measure and manage activities
• Risk registers record harms, likelihood, impact, controls and accountable owners
• ISO/IEC 42001 concepts link AI objectives to management-system responsibilities
• Model Cards document intended use, limitations, evaluation and monitoring evidence
• Approval gates distinguish experimentation, pilot, deployment and material change
• Incident management defines detection, containment, investigation and stakeholder notification

Participants can identify privacy, security and contractual requirements affecting AI proposals.

• Purpose limitation constrains personal-data collection and secondary AI use
• Data minimisation reduces unnecessary fields within model inputs and prompts
• Privacy impact assessments identify processing risks before implementation decisions
• Access controls apply least-privilege principles to datasets and model services
• Threat modelling examines prompt injection, data poisoning and model extraction
• Contract clauses address ownership, confidentiality, audit rights and subcontractors

Participants can conduct structured due diligence on AI vendors and proposed services.

• Supplier questionnaires test data practices, model documentation and support commitments
• Proof-of-concept criteria define representative data, benchmarks and exit conditions
• Service-level measures cover availability, response time, accuracy and incident reporting
• Third-party dependencies reveal foundation models, hosting providers and data processors
• Commercial terms separate implementation costs, usage charges and change requests
• Vendor evidence reviewed for security testing, audit reports and model limitations

Participants can define operating roles and adoption actions for AI-enabled work.

• RACI matrices assign accountability for data, models, decisions and controls
• Target operating models describe process changes, skills and handoffs
• Role impact assessments identify tasks removed, changed and newly created
• Training plans address AI literacy, escalation procedures and quality review
• Stakeholder maps identify sponsors, affected users, control owners and critics
• Adoption measures track usage, overrides, satisfaction and process compliance

Participants can design monitoring arrangements for deployed AI systems and services.

• Performance dashboards track accuracy, latency, cost and user acceptance
• Data drift detects changes in input distributions against reference periods
• Concept drift identifies changing relationships between inputs and outcomes
• Override rates reveal potential weaknesses in recommendations or workflow design
• Monitoring thresholds trigger review, retraining, rollback or suspension decisions
• Periodic reviews confirm continued fitness for purpose and approved use

Participants can prepare a financially credible investment case for an AI initiative.

• Total cost of ownership includes data, integration, licences, people and monitoring
• Benefit estimates distinguish cashable savings from capacity release and risk reduction
• Scenario analysis tests optimistic, expected and downside adoption assumptions
• Sensitivity analysis identifies variables with greatest effect on investment outcomes
• Stage-gate funding links expenditure to evidence from pilot and deployment
• Benefits-realisation plans assign measures, owners, review dates and corrective actions

Participants can sequence practical AI delivery activities from discovery through scaled adoption.

• Discovery activities confirm problem, users, data and decision constraints
• Pilot plans define scope, controls, evaluation evidence and sponsor decisions
• Implementation roadmaps sequence integration, testing, training and operational readiness
• Dependencies mapped across technology, data, legal, procurement and business teams
• Milestones link deliverables to funding releases and governance approvals
• Change plans prepare users for revised processes, roles and escalation routes

Participants can build, review and present a complete governed AI business case and implementation pack.

• Selected business problem framed with measurable outcomes and accountable sponsorship
• Use-case assessment scores value, feasibility, data readiness and risk
• Solution design specifies AI method, workflow, users and human oversight
• Data assessment records sources, quality issues, access controls and gaps
• Risk treatment plan applies NIST AI RMF controls and monitoring measures
• Investment case quantifies costs, benefits, assumptions and staged funding needs
• Implementation roadmap sequences pilots, approvals, adoption actions and performance reviews
• Executive presentation communicates recommendation, evidence, trade-offs and next decisions