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Managing an artificial intelligence project requires more than simple milestone tracking or traditional quality control. Due to the experimental nature of models, heavy reliance on datasets, and unpredictability of outcomes, a conventional management framework quickly hits its limits. Teams must incorporate iterative training loops, anticipate exploratory phases, and plan for post-deployment adjustments. To succeed, methodologies, skill sets, and governance need to be adapted—from defining business objectives to industrializing the solution. This article demystifies the key differences between AI projects and traditional IT projects, and offers concrete practices for structuring, monitoring, and effectively measuring your AI initiatives.
What Makes AI Projects Fundamentally Different
AI projects follow a non-linear lifecycle with successive experimentation loops. The exploration phases and post-delivery recalibration are just as critical as the initial production deployment.
Non-linear Lifecycle
Unlike a traditional software project where scope and deliverables are defined upfront, an AI project continuously evolves. After an initial prototyping phase, parameter and feature adjustments are required to improve model quality. Each training iteration can uncover new data requirements or biases that need correction.
This spiral approach necessitates frequent checkpoints and tolerance for uncertainty. The goal is not just to deliver software, but to optimize a system capable of learning and adapting.
Success hinges on the flexibility of teams and budgets, as training and fine-tuning work can exceed the initial schedule.
Continuous Post-Delivery
Once the model is deployed, the monitoring phase truly begins. Production performance must be monitored, model drift identified, and regular ethical audits conducted. Threshold or weighting adjustments may be necessary to maintain result relevance.
Recalibration requires collaboration between data scientists and business teams to interpret metrics and adjust predictions. Automated retraining pipelines ensure continuous improvement but require robust governance.
Periodic model updates are essential to address evolving data, usage patterns, or regulatory requirements.
Central Role of Data
In an AI project, the quality and availability of datasets are a critical success factor. Data must be cleaned, annotated, and harmonized before any training. Without a solid data foundation, models produce unreliable or biased results.
Data collection and preparation often account for more than 60% of the project effort, compared to 20% in a traditional software project. Data engineers are essential for ensuring traceability and compliance of data flows.
Example: A Swiss financial institution had to consolidate customer data sources spread across five systems before launching its AI scoring engine. This upstream centralization and standardization effort doubled the accuracy of the initial model.
Managing an Artificial Intelligence Project Starts with Data Management
Data is at the heart of every AI initiative, both for training and validating results. Incomplete or biased data undermines the effectiveness and integrity of the system.
Dispersed, Incomplete, or Biased Data
Organizations often have heterogeneous sources: operational databases, business files, IoT streams. Each can contain partial information or incompatible formats requiring transformation processes.
Historical biases (disproportionate representation of certain cases) lead to discriminatory or non-generalizable models. Profiling and bias-detection phases are essential to adjust data quality.
Creating a reliable dataset requires defining clear, documented, and reproducible rules for extraction, cleaning, and annotation.
Close Collaboration between PMs, Data Engineers, and Business Stakeholders
Data management requires ongoing dialogue between the project manager, technical teams, and business experts. Initial specifications must include data quality and governance criteria.
Data engineers handle the orchestration of ETL pipelines, while the business teams validate the relevance and completeness of the information used for training.
Regular data review workshops help prevent discrepancies and align stakeholders around shared objectives.
AI Data Governance: Rights, Traceability, and Compliance
Implementing a governance framework ensures compliance with regulations (nLPD, GDPR, sector-specific guidelines) and simplifies auditing. Each dataset must be tracked, timestamped, and assigned a business owner.
Access rights, consent management, and retention rules must be formalized during the scoping phase. Industrializing data pipelines requires automating these control processes.
Robust governance prevents ethical drift and secures the entire data lifecycle.
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Recruiting and Coordinating the Right Experienced AI Profiles
An effective AI team is multidisciplinary, combining technical expertise and business knowledge. Coordinating these talents is critical to align innovation with business objectives.
A Fundamentally Multidisciplinary AI Team
The foundation of an AI team consists of data scientists for prototyping, data engineers for data preparation, and developers for model integration. Added to this are business product owners to define use cases and legal experts to oversee regulatory and ethical aspects.
This mix ensures a holistic view of challenges, from algorithmic relevance to operational and legal compliance.
The complementary skill sets foster execution speed and solution robustness.
Example: A large Swiss logistics company formed an integrated AI cell, pairing supply chain experts with ML engineers. This multidisciplinary team reduced stock forecasting errors by 30%, while maintaining data governance in line with internal requirements.
The Project Manager’s Role (PM): Streamlining Communication and Aligning Technical and Business Goals
The AI project manager acts as a catalyst among stakeholders. They formalize the roadmap, arbitrate priorities, and ensure coherence between technical deliverables and business metrics.
By facilitating tailored rituals (model reviews, technical demonstrations, business workshops), they ensure progressive skill development and transparent communication.
The ability to translate algorithmic results into operational benefits is essential to maintain stakeholder buy-in.
Culture of Sharing and Skill Development
The exploratory nature of AI projects requires a culture of trial and error and continuous feedback. Code review sessions and lunch & learn events promote the dissemination of best practices and tool adoption across teams.
Continuous training through workshops or certifications maintains a high level of expertise in the face of rapidly evolving techniques and open-source frameworks.
A collaborative work environment, supported by knowledge management platforms, facilitates knowledge retention and component reuse.
Adapting Your Project Methodology for AI
Traditional Agile methods show their limitations in the face of uncertainties and data dependency. CPMAI offers a hybrid, data-first framework to effectively manage AI projects.
Why Traditional Agile Falls Short in AI Projects
Predefined sprints do not account for the unpredictability of algorithmic results. User stories are difficult to granularize when the data scope is unstable. Sprint reviews alone are not sufficient to adjust model quality.
This lack of flexibility can lead to misalignments between business expectations and achieved performance.
It then becomes impossible to define an accurate backlog before exploring and validating data sources.
Introduction to CPMAI (Cognitive Project Management for AI)
CPMAI combines Agile principles with data-driven experimentation cycles. Each sprint phase includes a model improvement objective, data profiling sessions, and in-depth technical reviews.
Deliverables are defined based on business and technical metrics, not solely on software features. The focus is on demonstrating performance gains or error reduction.
This framework embraces the exploratory nature and allows rapid pivots if data reveals unforeseen challenges.
Business-Oriented Scoping, Short Cycles, and Continuous Evaluation
The initial scoping of an AI project must define clear business KPIs: adoption rate, operating cost reduction, or conversion rate improvement, for example. Each short cycle of one to two weeks is dedicated to a mini-experiment validated by rapid prototyping.
The outcomes of each iteration serve as the basis for deciding whether to continue or adjust the development direction. Data scientists measure progress using quality indicators (precision, recall) supplemented by functional feedback.
This approach ensures traceability of decisions and continuous visibility on progress, up to production scaling.
Example: A financial sector player adopted CPMAI for its fraud detection project. Thanks to two-week cycles focused on optimizing alert thresholds, the model achieved a detection rate 25% higher than its predecessors while maintaining a controlled data footprint.
Transforming Your AI Projects into Value-Creating Assets for the Business
The specific features of an AI project—experimentation, data dependency, and constant adjustments—require a tailored management approach that blends agile methodologies with cognitive cycles. Implementing robust data governance, building multidisciplinary teams, and adopting frameworks such as CPMAI ensure successful and sustainable model industrialization.
Because each context is unique, the approach must remain flexible, built on modular open-source components free from vendor lock-in, and always aligned with key business metrics. Well-governed AI projects become levers for performance, growth, and differentiation.
Edana’s experts support companies in structuring, scoping, and delivering their AI initiatives with method, rigor, and efficiency.
