Course Introduction

AI for Project Feasibility Screening and Option Analysis Course provides project professionals with practical methods for using artificial intelligence to assess potential projects, evaluate feasibility, compare alternatives, and support evidence-based investment decisions. Organizations frequently face competing initiatives, incomplete information, uncertain benefits, resource constraints, and complex strategic choices before a project is formally approved. This course explores how AI can strengthen early-stage screening by combining project data, business assumptions, historical evidence, financial indicators, operational considerations, and strategic priorities.

The course examines how AI can support systematic feasibility screening across strategic, technical, financial, operational, commercial, regulatory, organizational, and implementation dimensions. Participants learn how AI can structure fragmented information, identify missing assumptions, detect inconsistencies, summarize feasibility evidence, and highlight areas requiring further investigation. The emphasis is on using AI as an analytical and decision-support capability rather than treating automated outputs as final investment recommendations.

A central focus is placed on option analysis and comparative evaluation. Participants explore how AI can help generate, structure, and compare alternative project approaches, delivery models, technologies, locations, implementation strategies, scope configurations, sourcing options, and investment scenarios. AI-assisted analysis can reveal trade-offs among cost, schedule, risk, value, complexity, scalability, resource requirements, and expected outcomes, enabling decision-makers to evaluate options against transparent criteria rather than relying solely on intuition or isolated business cases.

The course also addresses quantitative and qualitative analysis techniques that can strengthen early project decisions. Participants learn how AI can support scenario analysis, sensitivity analysis, assumption testing, cost and benefit comparisons, risk-adjusted evaluation, scoring models, and decision matrices. Particular attention is given to uncertainty, incomplete data, confidence levels, model limitations, and the distinction between analytical evidence and assumptions that still require expert validation.

Responsible AI considerations are integrated throughout the course because feasibility and option decisions can have significant financial, operational, social, and strategic consequences. Participants examine risks associated with biased data, hallucinated evidence, unsupported recommendations, misleading comparisons, outdated information, confidentiality, intellectual property, and automation bias. The course emphasizes human accountability, transparent assumptions, traceable evidence, appropriate governance, and clear escalation when AI-generated analysis is insufficient for high-impact decisions.

By the end of the course, participants will be able to design AI-supported feasibility screening processes, establish structured option evaluation frameworks, analyze project alternatives, identify critical uncertainties, and communicate decision-ready findings to sponsors and governance bodies. The course also explores emerging capabilities such as AI investment screening agents, conversational feasibility analysis, multimodal business-case assessment, predictive project success indicators, and intelligent decision-support systems that can help organizations improve the quality and speed of project selection.

Duration

5 days

Who Should Attend

• Project managers involved in project initiation, concept development, and business-case preparation.

• PMO professionals responsible for project intake, prioritization, governance, and investment recommendations.

• Program and portfolio managers evaluating competing strategic initiatives and investment options.

• Business analysts responsible for feasibility studies, requirements analysis, and option evaluation.

• Project sponsors and business leaders involved in investment and project selection decisions.

• Strategy professionals assessing initiatives against organizational objectives and strategic priorities.

• Finance and investment professionals supporting project business cases and financial feasibility analysis.

• Risk professionals evaluating uncertainty, assumptions, exposure, and project viability.

• Project controls professionals supporting cost, schedule, resource, and performance analysis.

• Procurement and commercial professionals evaluating sourcing, contracting, and delivery alternatives.

• Digital transformation professionals assessing technology-enabled project opportunities.

• Consultants developing feasibility studies, investment assessments, and project recommendations.

• Portfolio governance professionals responsible for stage-gate reviews and project approval processes.

• Operations and technical specialists contributing to technical and operational feasibility assessments.

• Senior decision-makers seeking more rigorous, transparent, and AI-enabled project option analysis.

Course Objectives

• Understand how AI can strengthen project feasibility screening by structuring evidence, assumptions, risks, opportunities, constraints, and strategic considerations.

• Apply AI-assisted techniques to evaluate strategic, technical, financial, operational, commercial, regulatory, and organizational feasibility dimensions.

• Develop structured criteria and decision models for comparing project alternatives, delivery approaches, technologies, investments, and implementation scenarios.

• Use AI to identify missing information, contradictory assumptions, weak evidence, dependencies, constraints, and issues requiring deeper feasibility investigation.

• Apply AI-assisted scenario, sensitivity, and assumption analysis to understand how changing conditions may affect project viability and option attractiveness.

• Evaluate the strengths, limitations, confidence levels, and reliability of AI-generated feasibility assessments and comparative recommendations.

• Design transparent option analysis processes that combine AI-generated insights with expert judgment, stakeholder priorities, governance requirements, and business objectives.

• Identify and manage responsible AI risks including bias, hallucinations, outdated information, automation bias, confidentiality concerns, and unsupported recommendations.

• Develop effective executive presentations and decision briefs that communicate feasibility findings, option trade-offs, uncertainty, risks, and recommended next steps.

• Build an AI-enabled project screening and option analysis framework that improves decision quality, investment discipline, speed, transparency, and strategic alignment.

Comprehensive Course Outline

Module 1: Foundations of AI for Project Feasibility and Option Analysis

• Understanding the role of AI in project initiation, feasibility screening, investment assessment, and early-stage decision-making.

• Exploring feasibility dimensions including strategic, technical, financial, operational, commercial, regulatory, organizational, and implementation considerations.

• Distinguishing feasibility assessment, business-case development, project prioritization, option analysis, and final investment decision processes.

• Examining the opportunities, limitations, risks, and governance requirements associated with AI-supported project selection.

Module 2: AI-Assisted Project Opportunity Screening and Strategic Alignment

• Using AI to screen project concepts against organizational strategy, objectives, priorities, capabilities, and portfolio direction.

• Developing AI-supported project intake criteria for evaluating strategic relevance, expected value, urgency, complexity, and organizational readiness.

• Applying AI to identify overlaps, conflicts, dependencies, and potential synergies between proposed initiatives and existing projects.

• Designing transparent screening processes that combine automated analysis with sponsor judgment and portfolio governance requirements.

Module 3: AI for Feasibility Data Collection and Evidence Assessment

• Identifying internal and external data sources required to support credible project feasibility studies and option comparisons.

• Using AI to organize business cases, market information, technical documents, historical project data, financial assumptions, and stakeholder inputs.

• Applying AI to identify missing evidence, inconsistent assumptions, outdated information, data quality issues, and unsupported feasibility claims.

• Establishing evidence traceability and confidence assessment practices so decision-makers can distinguish verified information from assumptions.

Module 4: AI-Powered Technical, Operational, and Organizational Feasibility

• Applying AI to assess technical requirements, technology readiness, integration complexity, infrastructure needs, and implementation constraints.

• Evaluating operational feasibility by analyzing processes, capabilities, resources, workforce requirements, service models, and organizational readiness.

• Using AI to identify implementation dependencies, capability gaps, change impacts, and operational constraints that could affect project viability.

• Comparing alternative technical and operating models while considering scalability, maintainability, resilience, interoperability, and long-term sustainability.

Module 5: AI for Financial and Commercial Feasibility Analysis

• Using AI to structure project cost assumptions, investment requirements, expected benefits, revenue opportunities, and financial constraints.

• Applying AI-assisted analysis to compare capital costs, operating costs, lifecycle economics, funding requirements, and potential financial returns.

• Evaluating commercial alternatives including sourcing strategies, contracting approaches, partnerships, suppliers, and delivery models.

• Identifying financial and commercial uncertainties that require sensitivity analysis, specialist review, or additional evidence before approval.

Module 6: AI-Powered Option Generation and Comparative Analysis

• Using AI to generate and structure alternative project approaches, delivery models, technologies, scope configurations, and implementation strategies.

• Designing weighted scoring models and decision matrices that compare alternatives across value, cost, risk, schedule, complexity, and strategic alignment.

• Applying AI to identify trade-offs, strengths, weaknesses, dependencies, constraints, and second-order effects associated with each option.

• Ensuring option recommendations remain transparent, evidence-based, context-specific, and subject to appropriate human validation and governance.

Module 7: AI for Scenario, Sensitivity, and Uncertainty Analysis

• Applying AI-assisted scenario analysis to examine how economic, operational, technical, regulatory, market, and organizational conditions may affect project viability.

• Using sensitivity analysis to identify assumptions and variables that have the greatest influence on feasibility outcomes and option attractiveness.

• Exploring risk-adjusted comparisons, probability-based thinking, uncertainty ranges, and confidence assessments for early project decisions.

• Developing decision thresholds and escalation criteria for situations where uncertainty is too significant for reliable automated recommendations.

Module 8: AI for Risk, Constraints, and Project Success Indicators

• Using AI to identify early indicators of project failure, feasibility weaknesses, implementation risks, and organizational constraints.

• Analyzing historical project information to identify patterns associated with successful or unsuccessful project initiation and delivery.

• Integrating feasibility findings with risk registers, dependency analysis, stakeholder concerns, resource constraints, and delivery readiness assessments.

• Exploring predictive project success indicators while recognizing limitations associated with historical data, changing environments, and model uncertainty.

Module 9: Responsible AI Governance for Feasibility and Option Decisions

• Establishing controls for data quality, confidentiality, intellectual property, privacy, source verification, and evidence traceability in feasibility analysis.

• Managing AI hallucinations, biased recommendations, incomplete comparisons, outdated evidence, false confidence, and automation bias in investment decisions.

• Defining human accountability, review requirements, approval thresholds, audit trails, and governance checkpoints for AI-supported project screening.

• Communicating uncertainty and analytical limitations clearly so sponsors and governance bodies can make informed decisions without overreliance on AI.

Module 10: Intelligent Project Screening and Emerging AI Decision Technologies

• Designing an enterprise AI-enabled feasibility screening framework that integrates project intake, option analysis, governance, prioritization, and investment decision processes.

• Exploring AI agents capable of reviewing business cases, comparing project options, identifying evidence gaps, and preparing decision-ready feasibility summaries.

• Examining emerging capabilities including multimodal feasibility analysis, conversational investment assessment, predictive project selection, and intelligent portfolio screening.

• Developing implementation roadmaps covering people, processes, technology, governance, adoption, skills, and continuous improvement for AI-enabled project selection.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.