You walk out of PI Planning feeling confident.

Boards are full. Risks are raised. Dependencies are mapped. Teams shook hands across the room. Everyone said, “We’re aligned.”

Then two sprints later, the same problems pop up again.

• “We’re blocked waiting on Team B.”
• “That API isn’t ready yet.”
• “Security approval didn’t come through.”
• “We didn’t realize this feature touches three other systems.”

And suddenly your carefully planned PI looks shaky.

Here’s the thing. Dependencies don’t resurface because teams are careless. They resurface because complex systems behave differently once real work begins.

This article breaks down why this happens and how leaders, Product Owners, Scrum Masters, and Release Train Engineers can prevent it without turning PI Planning into a six-hour dependency debate.

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What PI Planning Actually Solves (and What It Doesn’t)

Program Increment Planning is powerful. It aligns strategy, teams, and execution across the Agile Release Train. It creates visibility and shared ownership.

But PI Planning is still a forecast. It’s not a crystal ball.

During those two days, teams make the best decisions using the information available at that moment. After execution starts, new facts appear:

• Hidden technical constraints
• Changing priorities
• New stakeholders
• Legacy system surprises
• External vendor delays

What this really means is simple. Dependencies aren’t eliminated at PI Planning. They’re only exposed at a high level.

Fine-grained dependencies show up later when code meets reality.

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Why Dependencies Keep Coming Back

1. Features Hide Technical Coupling

Teams plan around features and capabilities. Engineers work with services, databases, interfaces, and infrastructure.

That mismatch causes blind spots.

A single feature might require:

• Backend service changes
• Shared authentication updates
• DevOps pipeline changes
• Data migration

None of this is obvious during sticky-note planning.

So dependencies quietly sit under the surface until Sprint 2 or 3.

2. Teams Plan Optimistically

see this in almost every ART.

During planning, teams assume:

• “They’ll deliver on time”
• “Integration should be simple”
• “We’ll manage somehow”

Optimism feels good in the room. Reality later feels different.

When even one upstream team slips, the entire chain breaks.

3. Late Discovery During Implementation

No amount of planning replaces learning through delivery.

Developers discover real dependencies only when they:

• Open the code
• Hit integration tests
• Deploy to staging

Architecture drawings rarely capture everything. Legacy systems especially love surprises.

4. Changing Priorities Mid-PI

Business changes. Stakeholders change direction. New compliance requirements show up.

Once priorities shift, planned dependency chains break. New ones form.

Teams suddenly rely on work that wasn’t even in the original plan.

5. Missing Continuous Dependency Management

Many ARTs treat dependency mapping as a PI Planning activity only.

That’s the core mistake.

Dependencies evolve every sprint. If teams don’t inspect them weekly, they drift out of sync.

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The Hidden Cost of Recurring Dependencies

When dependencies resurface repeatedly, the damage compounds:

• Missed PI Objectives
• Unplanned carryover work
• Team frustration
• Constant re-planning
• Leadership losing trust in forecasts

Velocity might look fine at the team level. System flow suffers badly.

This is exactly why the Scaled Agile Framework guidance emphasizes system thinking and cross-team alignment, not just local team success.

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How Mature ARTs Prevent Dependency Surprises

1. Break Work into Smaller Vertical Slices

Large features create large dependencies.

Smaller slices reduce coupling and enable independent delivery.

Ask this during backlog refinement:

“Can this be shipped without waiting for another team?”

If the answer is no, slice again.

2. Make Dependencies Visible Every Sprint

Don’t rely only on the PI board.

Maintain:

• Dependency Kanban
• Cross-team syncs
• Weekly ART Scrum of Scrums

Treat dependencies like risks. Review them constantly.

3. Shift from Commitment Thinking to Flow Thinking

Instead of asking, “Did Team B finish by Sprint 3?”

Ask, “How quickly does work move across the system?”

Track lead time and blocked time. These metrics reveal dependency pain early.

4. Use Architecture Runway Intentionally

Teams often skip enabler work because it feels less exciting than features.

But a weak architecture runway creates endless dependencies later.

Invest early. Reduce coupling. Create shared services properly.

5. Empower Product and Flow Leadership Roles

Dependency management isn’t just engineering work.

It needs strong coordination across:

• Product strategy
• Backlog sequencing
• Capacity allocation

This is where well-trained leaders make a visible difference.

Many organizations build this capability through structured programs like Leading SAFe Agilist certification training, which focuses heavily on ART-level alignment and cross-team flow.

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Role-Based Actions That Actually Work

For Product Owners and Product Managers

Prioritize fewer parallel features. Sequence work intentionally. Reduce cross-team handoffs.

Deep product ownership training such as the SAFe Product Owner Product Manager (POPM) certification helps teams design roadmaps that minimize systemic dependencies instead of amplifying them.

For Scrum Masters

Don’t wait for blockers to explode.

• Surface cross-team risks early
• Escalate at Scrum of Scrums
• Track dependency aging

Skill development through the SAFe Scrum Master certification strengthens facilitation and coordination at the team level.

For Advanced Facilitators

When systems grow complex, regular facilitation isn’t enough. You need systemic coaching.

Programs like the SAFe Advanced Scrum Master training prepare leaders to handle multi-team impediments and dependency storms effectively.

For Release Train Engineers

You own the flow of the whole train, not individual teams.

• Run dependency reviews weekly
• Balance capacity
• Reduce cross-team contention

Many RTEs sharpen these skills through the SAFe Release Train Engineer certification training, which focuses on orchestration at scale.

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Modern Twist: Using AI to Detect Dependencies Early

Some ARTs now use analytics tools to identify dependency patterns automatically.

AI can analyze:

• Backlog links
• Historical delays
• Blocked tickets
• Code ownership maps

This creates early warnings before humans notice.

Research communities like the Agile at Scale discussions by Martin Fowler show how system-level insights help reduce coordination waste significantly.

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A Simple Mental Model That Helps

Think of dependencies like traffic.

You don’t solve traffic by planning one perfect day. You solve it with signals, rules, and continuous adjustments.

PI Planning sets direction. Ongoing flow management keeps the road clear.

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Key Takeaways

• Dependencies are inevitable in complex systems
• PI Planning only surfaces the obvious ones
• Continuous visibility prevents surprises
• Smaller slices reduce coupling
• Strong roles and facilitation matter more than bigger plans

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Final Thoughts

If dependencies keep resurfacing in your ART, it doesn’t mean your PI Planning failed.

It means your system is complex and alive.

The goal isn’t to predict everything. The goal is to detect early and adapt quickly.

When teams combine strong planning, continuous inspection, and capable leaders, dependencies stop feeling like fire drills. They become manageable signals.

And that’s when delivery starts to feel smooth again.

 

Also read - How Small Architectural Decisions Create Big Delivery Delays

Also see - What Happens When Teams Optimize Locally Instead of Systemically