The pre-study with Tensor Bridge has three overall goals:
- To define the business case: stakeholders, expected returns, market and competition
- To define the extend of the project in terms of technology, algorithms, staffing and costs
- To define success criteria for the project
A pre-study for an AI project is a critical phase that helps set the stage for a successful project. It involves gathering information and conducting an initial analysis to determine the feasibility, scope, and requirements of the AI project. Here are some detailed points typically included in a pre-study for an AI project.
Project Objectives and Scope: Define the specific goals and objectives of the AI project. Determine the scope of the project, including the problem to be solved and the potential outcomes.
Business Case: Identify the business need for the AI project and the expected benefits, such as increased efficiency, cost savings, or revenue growth.
Stakeholder Analysis: Identify key stakeholders and their roles in the project. Determine communication and reporting mechanisms for keeping stakeholders informed.
Cost-Benefit Analysis: Conduct a cost-benefit analysis to assess the potential return on investment (ROI) for the AI project.
Data Assessment: Evaluate the availability, quality, and relevance of data required for the AI project. Determine data collection and preprocessing needs.
Technology Stack: Select the AI and machine learning technologies, frameworks, and tools that are most suitable for the project.
Feasibility Analysis:Assess the technical feasibility of implementing AI solutions for the identified problem. Consider any potential challenges or constraints that may arise during implementation.
Resource Requirements: Estimate the resources needed, including personnel, hardware, software, and budget. Identify any skill gaps and training needs.
Risk Assessment: Identify potential risks and challenges that could impact the success of the project. Develop risk mitigation strategies.
Legal and Ethical Considerations: Determine the legal and ethical implications of the AI project, such as data privacy and compliance with regulations like GDPR.
Project Timeline: Create a preliminary timeline that outlines the major milestones and deliverables. Set realistic expectations for project duration.
Alternative Solutions: Consider alternative approaches to solving the problem, including non-AI solutions, and weigh their pros and cons.
Project Plan: Develop an initial project plan that outlines the steps, tasks, and responsibilities for the project. Define success criteria and key performance indicators (KPIs).
Approval and Funding: Seek approval from relevant stakeholders and secure the necessary funding for the project.
Documentation and Reporting: Establish a system for documenting project findings, decisions, and progress. Define reporting mechanisms for project updates.
Next Steps: Summarize the findings and outline the next steps for the AI project, including a Go/No-Go decision.
The output from the pre-study are typically (1) a Pre-study Report, (2) Excel file with the cost-benefit analysis, (3) Sometimes a prototype.
A thorough pre-study helps ensure that the AI project is well-planned and aligned with the organization’s goals, increasing the likelihood of a successful implementation.