Quelles sont les étapes pour définir la portée et les livrables du projet d’IA?
Les projets d’IA sont complexes et nécessitent une planification et une exécution minutieuses pour atteindre les résultats souhaités. Définir la portée et les livrables d’un projet d’IA est une étape cruciale qui peut vous aider à aligner vos objectifs, vos attentes, vos ressources et vos risques. Dans cet article, vous apprendrez les étapes pour définir la portée et les livrables du projet d’IA à l’aide de méthodes agiles et de meilleures pratiques.
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Ashish Patel 🇮🇳🔥 6x LinkedIn Top Voice | Sr AWS AI ML Solution Architect at IBM | Generative AI Expert | Author - Hands-on Time…
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Balagopal MadhusoodhananDirector Intelligent Automation | LinkedIn Top Voice (AI) | Speaker | Strategy & Architecture | Cloud computing |…
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Paul H.Chief Command and Control, NATO
La première étape consiste à comprendre le problème que vous souhaitez résoudre avec l’IA. Quelle est la situation actuelle, les points douloureux, les opportunités et l’état souhaité? Vous devez effectuer une analyse approfondie du domaine du problème, des parties prenantes, des sources de données et des solutions potentielles. Vous devez également définir les critères de succès et les indicateurs clés de performance
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Ashish Patel 🇮🇳
🔥 6x LinkedIn Top Voice | Sr AWS AI ML Solution Architect at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 12+ Years in AI | MLOps | IIMA | 100k+Followers
Tips for defining AI project scope and deliverables: 🔹 Be as specific as possible. The more detailed the scope statement, the less likely there will be misunderstandings or disagreements down the road. 🔹 Be flexible. The scope of an AI project can change as the project progresses, so it's important to be flexible and willing to adapt. 🔹 Use an agile approach. Agile methods can help you to define the scope of an AI project iteratively and incrementally, which can help to reduce risk and improve the chances of success.
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Balagopal Madhusoodhanan
Director Intelligent Automation | LinkedIn Top Voice (AI) | Speaker | Strategy & Architecture | Cloud computing | LowCode | Supply Chain Transformation
In addition to the impact of the AI project, would recommend to perform initial risk assessment of the use case as well. By gauging the level of risk in relation to the projected return on investment (ROI), organizations can make informed decisions on whether to prioritize or deprioritize the AI use case. This proactive approach not only safeguards against unforeseen challenges but also optimizes resource allocation, ensuring that AI initiatives align with business goals and contribute positively to the overall strategic direction.
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Vikrant Mastoli
Applied Machine Learning & Generative AI | Cloud | People Leader | Lifelong Learner | DE&I Ally and Advocate
In the Agile methodology, assigning a Product Owner role to the Pod (team) is paramount. Particularly when this role is assumed by a member of the business sponsor's team, it acts as a bridge, connecting the technical aspects of the project with the overarching business goals. With their profound understanding of the business's needs and the nuances of the data landscape, the Product Owner ensures that the project scope and the vision of the analytics product remains finely attuned to business objectives. This alignment enhances the relevance and effectiveness of the AI solution.
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Alison Grieve
Understand why you think AI will help you solve this problem. Be experimental and go for an agile iterative approach so you can learn as you go.
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Rizwana Begum
It’s very important to ensure alignment with goals. ✨Understanding the problem is the foundational step in any project. ✨Analyze the existing scenario to identify challenges & inefficiencies. ✨Recognize potential areas where AI can bring transformative improvements. ✨Define the ideal outcome you aim to achieve with the AI solution. ✨Conduct a comprehensive analysis of the problem domain. ✨Underst& the needs & expectations of stakeholders. ✨Identify relevant data sources for developing the AI solution. ✨Explore various avenues to address the identified problem. ✨Define clear criteria for determining the success of the AI solution. ✨Establish measurable Key Performance Indicators (KPIs) which will help measure the solution's effectiveness.
L’étape suivante consiste à définir la solution que vous souhaitez créer avec l’IA. Quelles sont les caractéristiques, les fonctions et les capacités de votre solution d’IA ? Comment sera-t-il résolu le problème et apportera-t-il de la valeur aux parties prenantes? Vous devez créer une vision et un énoncé de portée de haut niveau qui décrit les principaux objectifs, avantages et exigences de votre solution d’IA. Vous devez également identifier les contraintes et les risques techniques, éthiques et juridiques qui peuvent affecter votre solution d’IA.
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Ashish Patel 🇮🇳
🔥 6x LinkedIn Top Voice | Sr AWS AI ML Solution Architect at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 12+ Years in AI | MLOps | IIMA | 100k+Followers
Clearly define the problem that you want to solve with AI, and the features, functions, and capabilities of your AI solution. Identify the technical, ethical, and legal constraints and risks.
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Aldo Segnini
AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions
Clearly defining the AI solution is vital. Ensure that the AI features and capabilities directly address the identified pain points and opportunities. Moreover, it's crucial to consider ethical and legal implications from the outset, especially in industries with strict regulations. Consult legal and compliance experts to avoid complications later in the project.
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Abid Mohammed
Architecting and engineering applications, data platforms and products
I like to think it as an AI Task, and use below framework Definition - High-level process flow: To understand the fitment of the AI task into the overall process. - The inputs and the outputs of the task - The resources it will access. E.g. database, knowledge base, etc - Does it interface with humans through chatbot? or as an API to be called from an application? Design - Model type: Prediction, Forecasting, Generative, Recommendation, Classification, etc - Training Needs: From-scratch, Fine-tuning, Off-the-shelf. - Training Data: Available data and its structure. The structure needed for training - Data Engineering Design: to collect and process data for training. - Evaluation and Testing Approach - Deployment & Operations approach
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Lakshmanan Sethu
✨LinkedIn Top AI Voice | Helping Customers with Google Cloud AI/ML,Data Solutions | Published Author | Speaker
Before defining AI solutions, you need to make sure the solution meets the following factors 1. Specific use case that you are solving for 2.AI Security framework 3. AI ethical framework 4. AI Legal framework
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Rizwana Begum
✨Defining the AI solution shapes the blueprint, a roadmap considering all dimensions success. ✨Enumerate the specific features & functions the AI solution will encompass. ✨Clarify how the AI solution will address the problem & provide value. ✨Create a vision statement outlining the challenging objectives. ✨Define the scope, including main objectives & benefits. ✨Identify the essential requirements that the AI solution must meet. ✨Ensure alignment with stakeholders' needs & expectations. ✨Recognize technical limitations that may influence the solution. ✨Address ethical implications, ensuring responsible AI use. ✨Identify legal constraints & ensure adherence to regulations. ✨Analyze potential risks & establish mitigation strategies.
La troisième étape consiste à planifier le projet qui livrera votre solution d’IA. Comment allez-vous organiser, gérer et exécuter votre projet d’IA? Quels sont les rôles, les responsabilités et les compétences de votre équipe de projet? Quels sont les outils, les méthodes et les normes que vous utiliserez? Vous devez créer un plan de projet qui définit la portée, le calendrier, le budget, la qualité et la communication de votre projet d’IA. Vous devez également adopter une approche agile qui vous permet d’itérer, de tester et d’apprendre de votre solution d’IA.
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Naima AL FALASI - PMP®️ ICBB™
Strategist | AI Advisor and Shaper | Executive Mentor & Speaker | Advocate for Women Empowerment & Sustainability
In AI-driven projects, it's key to remember that static planning can be a limitation. It's not just about following a roadmap, but also about expecting detours and welcoming them. Integrating continuous feedback loops, not only from team members but also from the AI itself, can yield richer insights. As AI models train and evolve, so should your approach. Embrace unconventional team structures; consider involving AI ethicists or anthropologists to foresee societal implications. Remember, AI isn't merely a tool; it's a partner in your journey. Craft your plan keeping in mind that AI will 'grow', and your strategies should be malleable to harness its ever-evolving potential.
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Rizwana Begum
Planning lays the groundwork for efficient project execution,fostering adaptability & collaboration. ✨Outline how the AI project will be structured & managed. ✨Detail the approach to executing the AI solution development. ✨Define roles, responsibilities,& skills of project team members. ✨Specify tools, methods,& standards for development. ✨Embrace an agile approach for flexibility & iterative progress. ✨Develop a comprehensive project plan with Scope Definition, Schedule, Budget, Quality Standards & Communication Plan. ✨Integrate regular feedback cycles to refine the AI solution. ✨Identify potential risks & formulate mitigation strategies. ✨Allocate resources effectively to meet project demands. ✨Engage stakeholders for alignment & support.
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Aldo Segnini
AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions
When determining how to organize and manage the project, take into account the existing capabilities and resources within your organization. Agile methods are indeed valuable for AI projects, allowing for flexibility and adaptation as the project progresses. Make sure to involve cross-functional teams that include both technical experts and domain specialists. Collaboration between IT and business units is key to success.
La quatrième étape consiste à décomposer le projet en unités gérables et livrables. Quels sont les principaux composants, modules et tâches de votre solution d’IA ? Comment allez-vous les diviser, les hiérarchiser et les affecter à votre équipe de projet ? Vous devez créer une structure de répartition du travail (WBS) qui cartographie la hiérarchie et les dépendances de votre projet d’IA. Vous devez également créer un backlog produit qui répertorie les user stories, les fonctionnalités et les critères d’acceptation de votre solution d’IA.
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Rizwana Begum
This facilitates efficient task management & effective collaboration, ensuring steady progress. ✨Divide the AI project into manageable components & modules. ✨Create a work breakdown structure (WBS) outlining hierarchy & dependencies. ✨Define tasks & sub-tasks needed for each module. ✨Prioritize tasks based on their significance & interdependencies. ✨Assign tasks to project team members based on skills & capacity. ✨Develop a product backlog with User Stories, Features & Acceptance Criteria. ✨Map out task dependencies to ensure smooth progression. ✨Align with agile principles, enabling flexibility & adaptability. ✨Plan iterations with focused goals & deliverables. ✨Regularly review & refine the work breakdown structure & backlog.
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Naima AL FALASI - PMP®️ ICBB™
Strategist | AI Advisor and Shaper | Executive Mentor & Speaker | Advocate for Women Empowerment & Sustainability
Breaking down an AI project is more than just task allocation; it's akin to creating the DNA of your solution. Recognize that unlike traditional projects, AI thrives on iteration and experimentation. Instead of a rigid hierarchy, envision a fluid ecosystem. While WBS helps in alignment, a 'learning backlog' can be introduced, where the AI's insights, anomalies, or 'surprises' are catalogued for team evaluation. Such practices make space for serendipity - sometimes the AI may find a solution pathway you hadn't thought of. Cultivate an environment where both humans and AI can refine and redefine the project's trajectory.
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Aldo Segnini
AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions
Breaking down the AI project into manageable units is where your extensive technology background will shine. Consider the modularity of your AI solution, making it easier to prioritize and assign tasks. Embrace the idea of continuous improvement. Encourage your team to regularly review and refine the work breakdown structure to ensure alignment with evolving project goals and emerging insights from the data.
La cinquième étape consiste à estimer le projet en termes de temps, de coût et d’effort. Combien de temps faudra-t-il pour terminer chaque unité de votre solution d’IA? Combien cela coûtera-t-il de développer, déployer et maintenir votre solution d’IA ? Combien d’efforts cela nécessitera-t-il de la part de votre équipe de projet et d’autres ressources? Vous devez utiliser diverses techniques et outils d’estimation pour calculer la durée, le budget et les besoins en ressources de votre projet d’IA. Vous devez également tenir compte de l’incertitude et de la variabilité de votre projet d’IA.
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Naima AL FALASI - PMP®️ ICBB™
Strategist | AI Advisor and Shaper | Executive Mentor & Speaker | Advocate for Women Empowerment & Sustainability
Estimating AI projects goes beyond traditional metrics; it's about forecasting the known and preparing for the unforeseen. While time, cost, and effort are foundational, one should also estimate the 'knowledge accumulation'—how quickly the AI model matures and the iteration loops needed. Introduce the 'elasticity factor,' gauging the system's ability to adapt to new data without significant reworks. And don't just budget for development; allocate resources for post-launch AI 'tuning' sessions. Remember, AI is a bit like fine wine; it matures with time and data. So, set up a 'refinement reserve'—a dedicated space in your estimation for AI's unique evolutionary path.
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Rizwana Begum
It ensures realistic outlook & effective resource management. ✨Estimate duration required for each AI solution unit. ✨Determine costs for development, deployment, & maintenance. ✨Assess the effort needed from the project team & resources. ✨Utilize various techniques, such as Expert Judgment, Analogous Estimation, Parametric Estimation. ✨Utilize estimation tools & software for accuracy. ✨Account for uncertainties & project variability. ✨Formulate plans to mitigate unforeseen challenges. ✨Allocate buffers to handle unforeseen delays. ✨Factor in potential risks that could impact estimation. ✨Continuously refine estimates based on actual progress. ✨Optimize resource allocation for efficiency. ✨Establish a budget that covers all project aspects.
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Sagar Nikam
Head of Product - AI and Data | Advancing AI & Data-Driven Innovations | Business Intelligence and Data Analytics | Data Product Leader | Mentor | Speaker
A couple of essential things to consider in estimation are the Uncertainty/ risk involved in this type of project and the complexity of the solutions. AI is still new and requires lots of research and experimentation before reaching out to a perfect model for the business case. Having contingency in the project gives Data scientists scope to try new things. I generally work closely with my project team and stakeholders to establish the estimations for the AI projects.
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Aldo Segnini
AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions
Accurate estimation is challenging in AI projects due to their inherent complexity and unpredictability. It's wise to provide stakeholders with a range of estimates to account for uncertainties. Be prepared to adjust these estimates as the project unfolds and you gain a better understanding of the data and technical challenges.
La dernière étape consiste à valider le projet auprès de vos parties prenantes et de vos clients. Comment allez-vous vous assurer que votre solution d’IA répond aux attentes, aux besoins et aux valeurs de vos utilisateurs et bénéficiaires cibles ? Comment allez-vous recueillir, analyser et intégrer les commentaires et les suggestions de vos parties prenantes et de vos clients? Vous devez utiliser diverses méthodes et outils de validation pour vérifier, valider et évaluer votre solution d’IA. Vous devez également utiliser des principes et des pratiques agiles pour adapter, améliorer et fournir votre solution d’IA.
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Sagar Nikam
Head of Product - AI and Data | Advancing AI & Data-Driven Innovations | Business Intelligence and Data Analytics | Data Product Leader | Mentor | Speaker
The main validation of the project deliverable is against the core objective of customer and Product vision. It needs to go through rigorous testing and code review to ensure high-quality output One of the important validations for AI projects is - Bias and Fairness Analysis: Assessing the AI solution for biases that might lead to unfair or discriminatory outcomes. Use tools and methodologies to identify and mitigate biases that could negatively impact certain groups.
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Rizwana Begum
✨Engage stakeholders & customers throughout the process. ✨Ensure AI solution aligns with user expectations & needs. ✨Collect feedback from stakeholders & customers; analyze & integrate as needed. ✨Employ varied techniques, such as:Usability Testing, Beta Testing & Surveys/Interviews. ✨Leverage agile principles to adapt & improve. ✨Embrace iterative cycles for ongoing enhancement. ✨Ensure the solution delivers tangible value to beneficiaries. ✨Validate solution's adherence to requirements. ✨ Confirm alignment with regulations & ethics. ✨Utilize testing tools to ensure reliability & accuracy. ✨Validate that the solution meets the defined scope. ✨Do final review of solution before deployment. ✨Continuously iterate based on validation insights.
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Marjukka Niinioja
Transforming Businesses with Intelligent API and Information Architecture & APIOps Culture Shift
Validating especially an AI project should include business, risk, compliance, bias and impact to people: employees, customers, citizen, policy, business environments and markets, not just technical validation.
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Paul H.
Chief Command and Control, NATO
Sounds like this whole article was written by ChatGPT. Lots of statements of motherhood and apple pie, with little real insight. So lesson #1 for managing AI projects is to use your own brain, skills and talent in your team and not use ChatGPT to generate pseudo-content.
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Rizwana Begum
The following measures enhance the efficacy & sustainability of AI solutions. ✨Continuous iteration helps in optimal performance. ✨Collaboration across domains enriches AI projects. ✨Solutions should be scalable without major overhaul. ✨Prioritize robust data security & user privacy measures. ✨Solutions should avoid bias & promote fair & ethical outcomes. ✨Ensure AI decisions are understandable & transparent. ✨UI/UX should prioritize user comfort & interaction. ✨Solutions that learn & adapt enhance relevance over time. ✨Utilize pre-built AI libraries & frameworks for efficiency. ✨Solutions should anticipate & accommodate emerging trends (future-proofing).
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Balagopal Madhusoodhanan
Director Intelligent Automation | LinkedIn Top Voice (AI) | Speaker | Strategy & Architecture | Cloud computing | LowCode | Supply Chain Transformation
Documentation is often overlooked while developing software. It helps to ensure that the project is well-planned, well-executed, and well-documented, and that it can be easily maintained and scaled in the future. - Ensure work done in the project is repeatable and reproducible - Documentation helps to make it easier to maintain the machine learning model over time. - Documentation helps to ensure that the machine learning model is transparent and understandable - Documentation helps to ensure that the machine learning project is compliant with relevant regulations and standards
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Alison Grieve
Make sure you have plenty of diverse perspectives included in the process. Many software projects could have achieved their potential if they had involved more viewpoints and challenge. Especially from the various stakeholders who will use or be the recipients of the system.
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Jonathan Reilly
Senior Product Manager @ Vibes | Product Leadership, Machine Learning & AI
If you have a desired end state, you have set your goals, and scoped the project as best you can then you should begin looking for data sources that are available to you and the models that are within reach. Often times you may find a model you think will get you there but you don't have enough data so you need to get creative. Often times the grand vision you have set out requires a few forks in the road and machine learning models, the data to train them, and your actual training methods will take some tweaking and time. Make sure to set those expectations up front. Many times people think AI will just magically work but there is a lot of unknowns going into it more so than most work.
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