Artificial Intelligence in Contract Management Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Contract Managers, Commercial 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-CM-084

Before you join: Participants should have practical experience of contract administration, commercial management, procurement, legal operations or project delivery.

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

Contracts teams are under pressure to shorten review cycles, find obligations quickly, control non-standard terms and maintain an accurate record of contractual commitments. Artificial intelligence can assist with clause extraction, document comparison, drafting, risk triage, obligation tracking and renewal planning. It can also create material risks when outputs are unsupported, source documents are incomplete, confidential information is mishandled or automated recommendations bypass authorised review.

This course examines how to select, govern and apply AI within contract management activities. Participants assess suitable use cases, prepare contract data, establish human review controls and use AI-supported methods for drafting, redlining, clause review, negotiation preparation and post-award administration. The course addresses contract lifecycle management integration, AI vendor selection, model performance testing, privacy, auditability and relevant governance requirements, including the NIST AI Risk Management Framework and ISO 31000.

Teaching combines short instructor briefings with contract cases, group analysis, AI output review, negotiation scenarios and implementation planning. Participants work with realistic examples such as supplier agreements, service contracts, project change requests and regulated outsourcing arrangements. They leave with a practical AI-in-contract-management implementation pack: a use-case assessment, governance checklist, contract data plan, AI review workflow and 90-day action plan that can be adapted to their organisation.

The course is suited to professionals responsible for commercial contracts, procurement agreements, supplier performance, legal operations and contract technology. It is particularly relevant where teams manage high contract volumes, complex terms, dispersed repositories or time-sensitive obligations.

What you will be able to do

  • Assess contract management use cases using value, feasibility, risk and data-readiness criteria
  • Design an AI governance model with accountability, human review and escalation controls
  • Prepare a contract data taxonomy for extraction, search, reporting and obligation management
  • Draft AI-assisted contract content using approved sources, prompt patterns and clause libraries
  • Evaluate AI clause extraction and risk scoring using confidence thresholds and reference datasets
  • Apply AI-supported methods to negotiation preparation, redline review and concession tracking
  • Establish post-award AI controls for obligations, renewals, performance and claims warnings
  • Produce a 90-day implementation plan for a controlled contract AI pilot

Benefits of attending

For you

  • Recognise which contract tasks are suitable for AI assistance
  • Review AI-generated contract outputs with greater confidence and discipline
  • Create evidence-based proposals for contract AI pilots
  • Improve clause review, obligation tracking and renewal planning methods
  • Build credibility with legal, procurement, technology and risk stakeholders

For your organisation

  • Shorter contract review cycles through controlled extraction and triage
  • More consistent treatment of non-standard clauses and approval exceptions
  • Earlier identification of obligations, notice dates and renewal decisions
  • Reduced exposure from unsupported AI outputs and weak data controls
  • Clearer requirements for selecting and managing contract AI suppliers

Course modules

10 modules over 5 days

Participants can identify appropriate AI uses and control points across the contract lifecycle.

  • Contract lifecycle management stages, data objects, controls and ownership boundaries
  • Generative AI, machine learning and rules engines distinguished by use case
  • Use-case triage using value, feasibility, risk and data-readiness criteria
  • Human-in-the-loop decisions mapped for drafting, review, approval and administration
  • CLM integration points: repositories, CRM, procurement, ERP and e-signature platforms
  • AI error modes: hallucination, extraction failure, bias and stale context
  • Baseline metrics for cycle time, deviation rates and obligation compliance

Participants can define the contract data foundations required for reliable AI-supported processes.

  • Contract corpus inventory classified by template, jurisdiction, counterparty and status
  • Metadata taxonomy designed for clauses, obligations, risks and renewal dates
  • Data quality profiling: completeness, accuracy, consistency, timeliness and duplicate detection
  • Document ingestion workflows using OCR, parsing and version-control rules
  • Gold-standard annotation sets created for clause extraction and classification
  • Training, validation and test sets separated to prevent data leakage
  • Retention schedules aligned with records policies and legal holds

Participants can establish governance controls for AI-supported contract decisions and outputs.

  • NIST AI RMF functions applied to contract-management use cases
  • ISO 31000 risk registers adapted for AI-assisted contracting decisions
  • Accountability matrix assigns business, legal, procurement, IT and vendor responsibilities
  • Privacy impact assessment identifies personal data, purpose and access controls
  • Model cards document intended use, limitations, evaluation and approval conditions
  • Third-party AI due diligence reviews security, data use and subcontractors
  • EU AI Act obligations screened against relevant deployment scenarios

Participants can use AI to prepare contract content while maintaining approved terms and review controls.

  • Prompt patterns specify role, source documents, constraints and output format
  • Clause-library retrieval grounds drafts in approved fallback positions
  • Redline generation preserves defined terms, cross-references and drafting conventions
  • Playbook-guided negotiation responses distinguish acceptable, escalated and prohibited positions
  • Source citation requirements reduce unsupported legal and commercial assertions
  • Version comparison detects substantive changes beyond formatting differences
  • Approval gates route non-standard terms to authorised reviewers

Participants can evaluate AI-supported contract review outputs and route exceptions appropriately.

  • Clause extraction models identify limitation, indemnity, termination and payment terms
  • Confidence thresholds determine auto-acceptance, human review and exception handling
  • Obligation extraction separates actions, owners, deadlines, dependencies and evidence
  • Contract risk scoring combines clause deviations, value, jurisdiction and supplier criticality
  • Semantic search retrieves precedent language and related contractual commitments
  • False-positive and false-negative rates measured against annotated reference sets
  • Review queues prioritised by materiality, confidence and contractual deadline

Participants can apply AI-supported analysis to negotiation preparation and controlled concession management.

  • Negotiation issue lists generated from playbooks, risk scores and deal context
  • BATNA, reservation point and ZOPA translated into negotiation parameters
  • AI summaries distinguish agreed points, open issues and proposed concessions
  • Concession logs track trade-offs, approvals, rationale and reciprocal value
  • Counterparty communications checked for tone, confidentiality and unintended commitments
  • Escalation triggers identify deviations requiring legal, finance or executive approval
  • Meeting transcripts converted into validated actions, owners and deadlines

Participants can use AI-supported methods to monitor commitments, performance and contractual events.

  • Obligation registers link commitments, milestones, service levels and evidence
  • AI alerts identify missed notices, expiring rights and renewal windows
  • Service-level performance matched against contractual measurement and remedy clauses
  • Change-control requests assessed against scope, price, schedule and authority
  • Claims early-warning indicators combine correspondence, delay events and notice requirements
  • Supplier performance reviews connect operational data to contractual remedies
  • Renewal decisions use spend, performance, risk and market evidence

Participants can evaluate contract AI suppliers and define measurable implementation requirements.

  • AI vendor evaluation compares functionality, integration, security and commercial terms
  • Proof-of-concept criteria define accuracy, throughput, explainability and user adoption
  • Acceptance tests specify reference datasets, thresholds, exceptions and remediation
  • Service agreements address model changes, uptime, support and exit assistance
  • Data processing terms allocate controller, processor, retention and deletion duties
  • Audit rights cover model performance, security incidents and subcontractor controls
  • Implementation roadmap sequences configuration, migration, training and controlled rollout

Participants can design an operating model for controlled adoption of contract AI processes.

  • Target operating model defines roles, handoffs, controls and decision rights
  • RACI matrix assigns accountability for prompts, outputs, overrides and incidents
  • Pilot design selects contract populations, success measures and review periods
  • Process maps compare current-state work with AI-assisted future-state controls
  • Change impact assessment addresses adoption, skills, workload and resistance
  • KPI dashboard tracks cycle time, extraction accuracy, exceptions and adoption
  • Incident management workflow records errors, harms, corrections and lessons learned

Participants can prepare a prioritised and governed plan for a contract AI implementation.

  • Use-case business case quantifies effort, cost, risk reduction and benefits
  • Prioritisation matrix ranks initiatives by value, readiness, complexity and exposure
  • 90-day action plan assigns milestones, sponsors, resources and governance checkpoints
  • Stakeholder communications frame controls, expected changes and decision responsibilities
  • Contract AI policy establishes permitted uses, prohibitions and escalation routes
  • Personal application commitments link selected use cases to workplace outcomes
  • Implementation assumptions tested against legal, operational and technology constraints

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

What you take back to work

  1. Contract AI use-case assessment matrix
  2. AI governance and human review checklist
  3. Contract data taxonomy template
  4. AI-assisted drafting and redline review protocol
  5. Contract AI vendor evaluation scorecard
  6. 90-day contract AI implementation plan

Who should attend

  • Contract Managers — need practical controls for AI-supported review and administration
  • Commercial Managers — need faster, consistent handling of contractual risk and deviations
  • Procurement Managers — need to assess AI for supplier agreements and sourcing workflows
  • Legal Operations Professionals — need governed AI processes, data structures and review routes
  • Project Managers — need earlier visibility of contract obligations, notices and change controls
  • CLM and Contract Technology Leads — need a structured approach to AI selection and rollout

Skills and tools covered

AI contract governanceClause extractionContract data managementAI-assisted draftingObligation monitoringImplementation planning

Tools and standards

NIST AI Risk Management Framework, ISO 31000, WorldCC Contracting Principles, EU AI Act

How the course runs

Training methodology

The course uses short briefings to introduce AI concepts, contract controls and governance requirements, followed by case work using supplier, service and project contracts. Participants examine AI outputs for accuracy, source support, confidentiality and escalation needs. Group exercises apply clause extraction criteria, risk scoring, negotiation playbooks and vendor evaluation methods. Simulations test human review decisions where AI recommendations conflict with contract evidence. Each participant develops an application plan for a selected contract process and receives structured instructor feedback.

Requirements and prerequisites

Participants should have practical experience of contract administration, commercial management, procurement, legal operations or project delivery. They should understand common contract components such as scope, payment, change control, confidentiality, liability, termination and renewal. Familiarity with a contract lifecycle management system is useful but not essential. No coding, data science, machine learning development or specialist legal qualification is required. The course explains AI concepts from a contract management perspective, while assuming participants can read and discuss commercial contract terms.

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 practical familiarity with contract management, procurement, commercial management or related work. No coding, data science or AI development experience is required.

Participants should bring examples of non-confidential contract processes, reporting challenges or AI use cases they want to assess. They should not upload confidential contracts, personal data or restricted information into public AI tools.

The course suits contract managers, commercial managers, procurement professionals, legal operations staff, project managers and contract technology leads. It is designed for people who already work with contracts and need to assess or introduce AI responsibly.

This course focuses on contract lifecycle activities, contract data, clause controls, approval routes, supplier terms and post-award obligations. Prompting is covered as one controlled method within a wider contract governance approach.

Participants can use the templates to assess a priority use case, such as clause extraction, obligation tracking or drafting support. The 90-day plan helps them define owners, controls, measures and pilot steps with internal stakeholders.

The programme provides approximately 32.5 contact hours, delivered from 08:30 to 16:30 each day. Time is divided between AI foundations, data and governance, contract drafting and review, post-award management, vendor selection 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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