AI/ML Project Development Phases 1/4: Discovery and Feasibility
Step 1 of 4 in a successful AI/ML project development, a well-executed discovery and feasibility phase minimizes risks and sets realistic expectations for your AI project.
This article is the first of a 4 part series to guide you through the four key stages of AI/ML development, emphasizing the importance of a data-centric approach and providing best practices for each phase.
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Phase 1: The discovery and feasibility phase
In this article, we discuss about the discovery and feasibility phase that is the cornerstone of any successful AI project. This stage involves a comprehensive analysis of the problem at hand, market research, and a thorough assessment of whether an AI solution is not only possible but also the most effective approach. It's during this phase that you define the scope of your project, identify potential challenges, and set realistic goals.
Today, I’ll cover the following practices:
1- Defining clear objectives for your AI/ML project
2- Conducting market research to understand the competitive landscape, identify unmet needs, and validate the demand for your AI solution.
3- Assessing data availability and quality to determine what is possible
4- Evaluate the technical feasibility to understand if you have the necessary resources and capabilities to successfully develop and deploy your AI solution.
5- Perform a cost-benefit analysis to determine if the potential benefits of your AI project justify the investment.
Let’s dive in!
Define clear objectives
Clear objectives provide direction and focus for your AI project. They help align stakeholders, guide decision-making, and serve as a benchmark for measuring success. Without well-defined objectives, projects can easily lose direction or scope, leading to wasted resources and suboptimal outcomes.
Conduct stakeholder discussions to understand diverse perspectives
Use the SMART (Specific, Measurable, Achievable, Relevant, Time-bound) framework to set goals
Create a problem statement that clearly articulates the issue you're addressing
Conduct thorough market research
Market research helps you understand the competitive landscape, identify unmet needs, and validate the demand for your AI solution. It reduces the risk of developing a product that the market doesn't need or want, and can reveal valuable insights that inform your development strategy.
Analyze existing AI solutions in your domain
Identify gaps in current offerings that your AI can address
Assess potential competition and differentiation strategies
Assess data availability and quality
Data is the lifeblood of AI systems. Assessing your data early on helps you understand what's possible, what limitations you might face, and what additional data you might need. Poor data quality or insufficient data can lead to unreliable AI models, so this step is crucial for setting realistic expectations and planning your development approach.
Inventory existing data sources within your organization
Evaluate the need for external data acquisition
Assess data quality using metrics like completeness, accuracy, and relevance
Identify potential biases in your data sources
Evaluate technical feasibility
This step helps you understand if you have the necessary resources and capabilities to successfully develop and deploy your AI solution. It prevents you from committing to projects that are beyond your current technical capabilities and helps you plan for any additional resources or expertise you might need.
Assess your team's AI/ML expertise and identify skill gaps
Evaluate available computing resources (on-premise or cloud)
Consider the complexity of the problem and required AI techniques
Estimate development time and potential roadblocks
Perform a cost-benefit analysis
A cost-benefit analysis helps you determine if the potential benefits of your AI project justify the investment. It forces you to consider both tangible and intangible benefits, potential risks, and long-term implications. This analysis is crucial for securing buy-in from stakeholders and ensuring that your AI project aligns with broader organizational goals and resources.
Estimate development and operational costs
Project potential ROI over short and long terms
Consider intangible benefits like improved decision-making or customer satisfaction
Assess risks and develop mitigation strategies
Remember, the discovery and feasibility phase sets the foundation for your entire AI project. Investing time and effort in this phase can save significant resources down the line and greatly increase your chances of success.
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📚Continue reading the full series: Discover The Four Critical Phases of AI Product Development
Discovery and Feasibility: Phase 1 of 4 in AI/ML Project Development
Data Preparation and Model Selection: Phase 2 of 4 in AI/ML Project Development
Prototype and Experimentation: Phase 3 of 4 in AI/ML Project Development
Production Deployment and Continuous Iteration: Phase 4 of 4 in AI/ML Project Development