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 as a consultant, I am frequently called in to turn these project around. It is always amazing how proper scoping can transform ambitious concepts into business-critical solutions, while poor scoping sends even the most technically sound projects into endless cycles of scope creep, missed deadlines, and stakeholder frustration.

This is why I’m excited to share my upcoming presentation, “Scoping Data and AI Projects,” where I’ll dive deep into the frameworks and methodologies that separate successful initiatives from costly failures.

The Hidden Challenge of Data and AI Projects

Unlike traditional software development projects, data and AI initiatives face unique challenges that make effective scoping both more critical and more complex:

Uncertainty by Design: Data projects often begin with hypotheses rather than certainties. You’re exploring what’s possible with your data, not just building predetermined functionality.

Evolving Requirements: As you uncover insights, business requirements naturally evolve. What seemed important at the start may become secondary to unexpected discoveries.

Cross-Functional Complexity: These projects typically involve data engineers, data scientists, business analysts, domain experts, and executive stakeholders—each with different perspectives on success.

Technology Constraints: AI capabilities are rapidly evolving, but organizational data infrastructure, governance policies, and technical debt create very real limitations.

A Framework for Success

In my presentation, I’ll walk through a comprehensive framework that addresses these unique challenges head-on. Rather than theoretical concepts, this is a battle-tested approach refined through dozens of real-world implementations.

Understanding Project Objectives and Business Goals

Every successful data project begins with crystal-clear alignment between technical possibilities and business imperatives. I’ll share techniques for translating vague directives like “use AI to improve customer experience” into specific, measurable outcomes that technical teams can actually deliver.

Identifying Data Sources and Assessing Data Quality

Data availability and quality issues kill more AI projects than any technical limitation. We’ll explore systematic approaches for data discovery, quality assessment, and honest evaluation of what’s actually feasible with your current data landscape.

Defining AI Capabilities and Technology Requirements

Not every problem needs machine learning, and not every dataset can support sophisticated AI models. I’ll provide frameworks for matching business problems with appropriate technical approaches, from simple analytics to complex deep learning solutions.

Aligning Teams and Stakeholders for Collaboration

Technical brilliance means nothing without organizational buy-in. Learn practical strategies for building consensus across departments, managing expectations, and maintaining momentum throughout project lifecycles.

Establishing Timelines and Success Metrics

Traditional project management approaches often fail with data projects due to their exploratory nature. Discover how to create realistic timelines that account for uncertainty while still providing stakeholders with the predictability they need.

Beyond the Framework: Real-World Application

This session goes far beyond theoretical principles. Through interactive case studies, we’ll examine how this scoping framework applies to common scenarios like customer retention analysis, operational workflow optimization, and predictive maintenance initiatives.

You’ll leave with practical tools you can immediately apply, including:

• Templates for crafting precise problem statements
• Checklists for data readiness assessment
• Stakeholder alignment worksheets
• Project milestone frameworks designed for data initiatives
• Risk mitigation strategies specific to AI projects

The Cost of Poor Scoping

Many organizations invest months of development effort only to discover their carefully crafted models can’t be deployed due to data governance issues they never considered. I’ve watched brilliant data scientists build sophisticated algorithms that solve the wrong business problem because requirements weren’t properly defined upfront.

These failures aren’t just costly in terms of wasted resources—they erode organizational confidence in data initiatives and make future projects harder to approve and fund.

Transforming Your Approach

Effective project scoping isn’t about constraining creativity or innovation. Instead, it’s about channeling that innovation toward outcomes that matter. It’s about building confidence through transparency, managing risk through preparation, and delivering value through focused execution.

Whether you’re a Data or AI Manager looking to improve project success rates, a business leader seeking better ROI from AI investments, or a project manager navigating the unique challenges of data initiatives, this presentation will provide you with immediately actionable insights.

Join the Conversation

I’ll be presenting “Scoping Data and AI Projects” at SQL Saturday Minnesota 2025 (#1124) on September 27th in Saint Paul. This interactive session will include time for discussion, questions, and sharing experiences with fellow practitioners.

The insights I’ll share have been refined across industries ranging from healthcare and finance to manufacturing and retail. But more importantly, they’re designed to be practical tools you can start using immediately to improve your own project outcomes.

Data and AI have tremendous potential to transform how organizations operate and compete. But realizing that potential requires more than just technical expertise—it requires the discipline and frameworks to scope projects effectively from day one.

I look forward to seeing you there and helping you transform your next data or AI vision into measurable business reality.

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Ross McNeely will present “Scoping Data and AI Projects” at SQL Saturday Minnesota 2025 on September 27, 2025. For more information about the event and registration, visit sqlsaturday.com.