Scaling AI Projects: A Framework for Real Impact

How to Scale AI Projects with Agile

In recent years, nearly every organization has tested generative AI in some way. But according to MIT’s “GenAI Divide: State of AI in Business 2025”, 95% of AI investments are still delivering zero measurable return. It’s a staggering number, and one that highlights a deeper issue.

Most AI projects don’t fail because of technology. They fail because of shifting priorities, organizational silos, and lack of structure.

Companies often launch pilots without a clear business strategy, ROI targets, or visibility into how those initiatives connect to broader goals. Teams experiment in isolation, and lessons from successful pilots rarely scale across departments.

At e-Core, we’ve seen that what AI projects need most isn’t just better models, it’s a structured approach that connects experimentation to outcomes, and strategy to execution.

From pilot chaos to measurable impact: a real-world example

To understand what scaling AI projects looks like in practice, let’s look at one of our current partnerships with a large healthcare insurance provider.

Their call center team faced a problem that many of us can relate to. Policyholders who needed treatment approvals waited weeks for coverage confirmation. 

Representatives had to manually review rules, agreements, and policies, a time-consuming and error-prone process that achieved only about 60% accuracy.

By implementing an AI-driven coverage validation solution, the organization completely changed that experience. Today, call center employees use AI to interpret complex contracts and policies in real time. 

What once took weeks now takes minutes, and accuracy jumped from 60% to as high as 98%.

The result? Faster service, happier customers, and empowered employees who can finally focus on what matters: helping people.



This story isn’t just about better tech, it’s about aligning AI investments with business strategy so that experimentation leads to real, measurable outcomes.

A framework for scaling AI projects

To help organizations move from experimentation to impact, we use a practical framework based on three pillars: Prioritize with Confidence, Enable Agile Execution, and Connect Strategy to Tools.

1. Prioritize with Confidence

Traditional agile teams focus on features with predictable ROI. AI, however, doesn’t always play by those rules. Outcomes are uncertain, timelines are less predictable, and many projects never reach adoption.

That’s why prioritization must shift from features to experiments and outcomes.

A strong intake funnel helps collect creative ideas across the business. From there, each idea should be evaluated for data availability, feasibility, and business value. 

Ethical and regulatory considerations also need to be part of this early-stage evaluation.

Most importantly, data scientists and ML engineers must be involved early in this process, not just as executors but as strategic contributors who can assess data readiness and model viability.

If you’re exploring how to get your data foundations ready for AI work, our article on AI data preparation provides a helpful overview.

2. Enable Agile Execution

AI development doesn’t fit neatly into two-week sprints. It’s iterative, uncertain, and often nonlinear.

To keep teams aligned while allowing room for discovery, we recommend a hybrid execution model. Agile rhythms, such as stand-ups and retrospectives, remain essential, but experimentation needs flexibility.

At the healthcare insurance company, the AI team introduced real-time working sessions, allowing developers, QA, and product teams to collaborate and test live. 

This reduced rework, improved feedback cycles, and built a stronger bridge between technical and business roles.

Product owners, for example, became deeply involved in the experimentation phase, providing ongoing feedback and steering direction dynamically rather than through static requirements.

For more on how to structure and deliver complex AI work efficiently, check out our guide on AI delivery best practices. 

3. Connect Strategy to Tools

Finally, scaling AI projects depends on visibility. Tools like Jira or portfolio management solutions can connect business strategy to technical execution.

While agile teams track epics and features, leaders need a higher-level view — the initiative level, that spans departments and connects to outcomes.

By linking experiments and technical work to strategic initiatives, organizations can finally answer critical questions:

• Are these projects creating measurable business impact?
  
• Are we improving profitability or efficiency?
  
• Should we continue, pivot, or stop investing?

This transparency is what turns AI portfolios from a collection of pilots into a cohesive, data-driven strategy.

From Strategy to Systems

To make this framework more tangible, our Solutions Architecture Specialist, Vando Gonçalves, walked participants through what these principles look like in action, from mapping strategic initiatives in Jira to creating visibility across teams and systems.

Rethinking what success looks like

Scaling AI projects successfully means changing how we define progress. Unlike traditional software, AI work is nonlinear, it advances through experimentation, breakthroughs, and iteration.

Organizations that thrive with AI are the ones that balance structure with flexibility:

• They prioritize outcomes instead of outputs.
• They protect research time while maintaining agile discipline.
• They align strategy, tools, and people to create visibility and accountability.

If your organization is investing in AI, it’s time to look beyond pilots and toward sustained value creation.

To explore how e-Core can help align your AI portfolio for measurable outcomes, visit our insights on digital transformation with Atlassian’s system of work or learn from our healthcare AI transformation case study.