Toolsday

August 25, 2026 · Last updated on August 7, 2026

AI Toolsday: Real AI, Real Agile, Real Workflows

# Continuous Improvement

Practical AI workflows for Agile teams

Identify Cross-Team Dependencies Before They Become Delivery Risks

Dependencies are a natural part of building complex solutions, but they often don't become visible until they're already impacting delivery.

This workflow uses AI to analyze work across multiple teams and identify potential dependencies before planning is complete, giving teams more time to coordinate, reduce risk, and make informed decisions.

📌 Before You Start

The AI tools featured in this workflow are examples, not requirements. Every organization has different approved tools, security policies, and ways of working.

The goal of AI Toolsday isn't to recommend one specific product—it's to share practical AI workflows that you can adapt using the tools available to you. If your organization uses Microsoft Copilot, Google Gemini, Claude, ChatGPT Enterprise, or another approved AI solution, feel free to substitute those where it makes sense.

Focus on the workflow, not the tool.

🛠️ Tools Used

NotebookLM

Analyze planning artifacts from multiple teams to identify recurring themes, shared work, and potential dependencies based on your organization's own documentation.

ChatGPT

Transform those observations into a dependency map, identify areas of concern, and recommend discussion topics for planning conversations.

⚙️ How It Works

Step 1: Gather planning information

Collect the planning artifacts that describe the work each team is preparing to deliver.

Examples include:

Draft PI Objectives

Feature descriptions

Team backlogs

Capability descriptions

Architecture runway items

Enabler Features

Existing dependency boards (if available)

Upload these documents into NotebookLM.

The more complete your planning information, the easier it becomes to identify relationships between teams.

Step 2: Look for connections across teams

Instead of reviewing each team's work independently, ask NotebookLM to analyze all of the planning information together.

Try prompts like:

Which teams appear to be working on related capabilities?

Are multiple teams relying on the same system or component?

Where might sequencing become important?

Which Features appear to require work from multiple teams?

Are there dependencies that haven't been explicitly documented?

Which teams should coordinate before PI Planning begins?

NotebookLM is particularly effective at connecting information spread across multiple documents that would otherwise require hours of manual review.

Step 3: Turn observations into a dependency plan

Once NotebookLM has identified potential dependencies, copy those findings into ChatGPT.

Ask ChatGPT to:

Group related dependencies together

Identify the highest-risk dependencies

Explain why each dependency matters

Suggest conversations the teams should have

Recommend mitigation strategies

Create a simple dependency summary that can be shared during PI Planning

This transforms a long list of observations into something your ART can actually use.

Step 4: Validate with the teams

AI can suggest likely dependencies—but only the teams doing the work can confirm whether those dependencies actually exist.

Use the AI-generated summary to guide conversations with Product Management, Scrum Masters, Architects, and delivery teams.

The objective isn't to replace dependency mapping—it's to help teams discover important conversations earlier.

💡 Why Agile Pros Care

Dependencies are one of the most common causes of delayed delivery, changing priorities, and planning surprises.

By analyzing planning artifacts before PI Planning begins, teams can identify coordination opportunities early, reduce delivery risk, and spend more time solving problems instead of uncovering them.

AI won't eliminate dependencies—but it can help ensure fewer of them come as a surprise.

💬 Prompt to Copy

You are an experienced Release Train Engineer supporting PI Planning.
Review the planning information below and identify potential dependencies across teams.
Specifically:
Identify work that appears connected across multiple teams.
Highlight sequencing dependencies.
Identify shared systems, components, or Features.
Flag areas where coordination may be required.
Rank the highest-risk dependencies.
Suggest mitigation strategies.
Recommend discussion topics for PI Planning breakout sessions.
Present the results as a Dependency Planning Guide that can be shared with Scrum Masters, Product Management, and Business Owners before PI Planning begins.
Planning Information: Paste your NotebookLM summary here.

✅ Try It This Week

Choose two or three teams that are working toward the same business objective. Upload their draft planning artifacts into your preferred AI tool and ask it to identify potential dependencies, shared assumptions, or coordination opportunities before your next planning session.

You might uncover conversations that are easier to have now than halfway through the PI.

💬 Continue the Conversation

What makes dependency management most challenging in your organization?

Is it identifying dependencies early, coordinating across teams, changing priorities, communication, or something else? Share what's worked well—or what lessons you've learned along the way.

🤝 AI Toolsday Tip

AI can quickly uncover patterns across large amounts of information, but it doesn't understand the full context of your organization. Use AI to surface potential dependencies and start better conversations, then rely on your teams to validate assumptions and determine the best path forward.

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