A Comprehensive Framework for Managing AI Projects
Building on academic research, industry best practices, and real-world experience delivering enterprise AI solutions.
Artificial Intelligence is transforming how organisations enhance customer value, boost productivity, and uncover insights. Yet managing AI projects is uniquely challenging. Unlike traditional software projects, AI projects face unprecedented complexities: experimental feedback loops, data quality uncertainties, and the need for cross-functional expertise spanning data science, engineering, operations, and compliance.
The Challenge
85% of AI projects fail to achieve the value businesses expected. This isn't due to lack of capability or resources. It's a structural problem: organisations attempt to manage AI projects using frameworks designed for traditional software development. This fundamental mismatch between methodology and the unique nature of AI work is the primary driver of failure.
1. Why AI Projects Fail
AI projects fail not because of AI capability, but because of fundamental management challenges:
Unclear Problem Formulation
Projects start with "let's use AI" rather than clearly defining the specific business problem AI will solve.
Weak Data Foundations
Data quality, governance, and infrastructure aren't established before model development.
Misaligned Teams
Data scientists, engineers, and business teams work in isolation without collaboration.
Operations Neglected
Success measured by model accuracy rather than production performance and business impact.
2. The 3-Phase Framework
Successful AI projects follow a structured, three-phase approach: Design, Develop, and Deploy. Each phase requires distinct expertise and has clear objectives and deliverables.
3. Design Phase: Stages 1–4
The design phase answers five critical questions before any development work begins.
Stage 1: Problem Statement
Identify, clarify, and formulate the problem with stakeholders. Determine if an AI solution is appropriate. Document problem statement, goals, business case, and problem typology (strategic, tactical, operational, or research).
Stage 2: Compliance Assessment
Review problem, approach, and solution for security, ethical, and legal compliance. Assess algorithmic justice, data representations, and stakeholder interests. Reference frameworks: EU AI Act, NIST AI RMF, ISO 42001.
Stage 3: Technical Literature Review
Review published research, deployed systems, and libraries relevant to the problem. Evaluate pre-trained models (e.g., GPT) for potential reuse. Assess licensing, suitability, and legal constraints.
Stage 4: Secure Buy-In
Gain executive approval, budget allocation, and cross-functional commitment. Present architecture design, timeline, resource requirements, and expected ROI.
4. Develop Phase: Stages 5–13
The develop phase transforms the conceptual design into a validated, production-ready model.
Data-Centric Stages (5–8)
5. Infrastructure Build
Provision cloud resources, databases, and development environments for scalable, reliable data systems.
6. Data Collection & Integration
Consolidate siloed data into unified repository (data warehouse, data lake) to ensure single source of truth.
7. Data Exploration & Preparation
Analyse data quality, relationships, outliers, and distributions. Prepare data for model training through cleaning, imputation, and formatting.
8. Feature Engineering
Identify and construct critical features. Create derived features from raw data to improve model performance.
Model Development Stages (9–13)
9. Modelling & Training
Select algorithms, train models, and iteratively refine based on performance. Focus on reproducibility and version control.
10. Data Augmentation & Benchmark
Address class imbalance and model limitations. Establish baseline from human expertise or industry standards.
11. Evaluation & Metrics
Assess performance using relevant metrics (accuracy, precision, recall, F1, R², RMSE). Measure against business objectives.
12. AI Interpretability Review
Implement explainability methods (LIME, SHAP). Ensure transparency for regulatory compliance and stakeholder trust.
13. Feasibility Study & Go/No-Go Decision
Assess viability (solves business issue), desirability (ethics/governance), and feasibility (cost-effectiveness). Critical gate for project continuation.
5. Deploy Phase: Stages 14–17
The deploy phase moves AI from controlled environments into production, where it must perform reliably and generate measurable business value.
Stage 14: Model Deployment
Put evaluated model into operational use. Decide between real-time vs. batch processing. Extend ethics and governance considerations into production. Conduct risk assessment and mitigation planning.
Stage 15: Post-Deployment Review
Expert panel conducts technical and ethical review. Ensure compliance, standardisation, and documentation. Address intellectual property and service level agreements.
Stage 16: Operationalisation (MLOps)
Implement automated data and AI pipelines. Use microservices and containers for scalable, reliable model serving. Establish CI/CD practices for continuous improvement.
Stage 17: Continuous Monitoring
Monitor model drift, staleness, and performance degradation. Track end-user activity and adoption. Measure ROI through cost reduction, revenue increase, and productivity gains.
Documentation & Knowledge Management
Throughout all phases, maintain comprehensive documentation covering purpose, methodology, assumptions, limitations, and usage. This ensures compliance, reproducibility, knowledge transfer, and provides audit trails for governance.
6. Team Structure: Core & Extended Roles
AI project success depends on assembling the right team. You need diverse expertise collaborating with clear accountability.
Core Team (5 Essential Roles)
Product Owner
Own product success, define requirements, liaise with stakeholders.
Project Manager
Monitor progress, manage budget, ensure milestones, facilitate updates.
Data Engineer
Transform raw data into usable formats, build pipelines.
Data Scientist
Solve business challenges using algorithms and data science methods.
ML/AI Engineer
Bridge testing and production, operationalise models, ensure CI/CD.
References
This framework builds on academic research, industry best practices, and real-world experience. Key sources include:
Core Frameworks
- ✓ CRISP-DM: Cross-Industry Standard Process for Data Mining
- ✓ TDSP: Team Data Science Process (Microsoft)
- ✓ Amershi et al. (2019): Software Engineering for Machine Learning
AI Governance & Ethics
- ✓ EU Artificial Intelligence Act (2024)
- ✓ NIST AI Risk Management Framework
- ✓ ISO 42001: AI Management System
Industry Research
- ✓ DeNisco Rayome: Why 85% of AI Projects Fail
- ✓ McKinsey: The State of AI
- ✓ Gartner: AI Maturity Models & Magic Quadrant
Apply This Framework to Your AI Initiative
This framework is grounded in real experience across diverse industries and organisations. Whether you're evaluating an existing AI initiative or planning a new one, this structure provides a clear roadmap.