- Scoping Data and AI Projects
- Setting Your Data and AI Projects Up for Success: A Strategic Guide to Timelines and Metrics
- The Hidden Key to Data and AI Project Success: Aligning Teams and Stakeholders
- Defining Data and AI Capabilities and Technology Requirements: Building a Foundation for Project Success
- The Data Detective: How Quality Assessment Shapes Successful Data and AI Project Scoping
- The Foundation of Data and AI Project Success: Understanding Project Objectives and Business Goals
- From Vision to Reality: Why Effective Scoping is the Make-or-Break Factor for Data and AI Projects
Setting Your Data and AI Projects Up for Success: A Strategic Guide to Timelines and Metrics
After working with organizations on data and AI engagements, one pattern emerges consistently… the projects that succeed aren’t necessarily those with the most sophisticated algorithms or the largest datasets. They’re the ones that establish clear timelines and measurable success criteria from day one. Whether you’re tackling customer retention challenges or optimizing operational workflows, the difference…
The Hidden Key to Data and AI Project Success: Aligning Teams and Stakeholders
Why technical excellence isn’t enough—and how to build the collaboration framework that actually delivers results After working many data and AI engagements, I’ve learned a hard truth: the most sophisticated algorithms and cleanest datasets won’t save a project if your stakeholders aren’t aligned. In fact, poor stakeholder alignment is one of the top reasons why…
Defining Data and AI Capabilities and Technology Requirements: Building a Foundation for Project Success
When scoping Data and AI projects, the technical element can determine the difference between transformative success and costly failure. This isn’t just about choosing the right tools, it’s about building a comprehensive understanding of what your organization can realistically achieve and what infrastructure you need to get there. I’ve observed that projects with clearly defined…
The Data Detective: How Quality Assessment Shapes Successful Data and AI Project Scoping
This is the second post in my series on scoping data and AI projects for success, based on insights from over 80+ real-world engagements. Picture this: Your team has crafted the perfect problem statement, stakeholders are aligned on objectives, and everyone’s excited about the transformative AI solution you’re about to build. Three months later, the…
The Foundation of Data and AI Project Success: Understanding Project Objectives and Business Goals
After leading over 80 data and AI engagements, I’ve learned that the most critical factor determining project success isn’t the sophistication of your algorithms or the complexity of your data model. It’s how well you understand and articulate your business objectives from the very beginning. Too often, organizations rush into Data or AI projects with…
From Vision to Reality: Why Effective Scoping is the Make-or-Break Factor for Data and AI Projects
According to industry research, the majority of data, machine learning, and AI projects never make it to production. While technical complexity often takes the blame, the real culprit frequently lies earlier in the process in how these projects are scoped and planned from the very beginning. After working on over 80 data and AI engagements…
Ross McNeely 