Make the Case for Predictive Planning Poker

Make the case to leadership

From: you@company.com
To: name@company.com
Subject: We're losing throughput to unpredictability we've never measured
 

Hey [Name],

What if we could pinpoint exactly where we're losing throughput—
and prove it with numbers?

I've been looking into why sprints keep slipping, and found
something worth discussing. Many teams run Planning Poker every
week—but they have no visibility into whether their process can
deliver predictably. A 2-point story might take 1 day or 11
days, and without the data, we can't see why.

Here's a concrete example I came across:

Two teams, same size, same sprint length—but one has 3-day code
reviews while the other turns them around in hours. The
difference? 70% more throughput. Same people, same skills. The
slower team would need to hire an entire second team—a $500K+
expense—to match the output that process improvement alone could
unlock.

The numbers compound quickly:

- Teams with high estimate variability (CV above 50%) find that
  forecasts become essentially unreliable—sprint plans are just
  guesses dressed in story points
- A team whose 3-point stories range from 0.3 to 3.6 days isn't
  estimating—they're averaging across completely different types
  of work
- Context switching from unpredictable work costs teams dozens of
  days annually in lost focus
- Every sprint that spills over erodes stakeholder trust and
  forces scope reductions the following sprint

The question is: where is our unpredictability?

I found a tool that connects Planning Poker estimates directly to
delivery data—so you can see exactly where variation creeps in
and fix it:

Predictive Planning Poker & Flow Intelligence

It shows which story point sizes are predictable and which aren't,
identifies bottleneck stages, and has an AI coach (Noesis) that
analyzes our specific patterns and tells us what to focus on—not
generic advice.

I'd like to install the free trial and see what it finds. Any
reason not to?

[Your name]
 

Leadership discussion points

When they ask "How is this different from the Jira board?" The board shows what's in progress. Smart Guess shows whether your estimates match reality. A board tells you a story is "In Progress." Smart Guess tells you that your 5-point stories take anywhere from 2 to 14 days—and that the variation is driven by code reviews backing up every second Thursday. That's the difference between a status display and a diagnostic tool.

When they worry about reporting overhead Smart Guess runs entirely inside Jira with zero manual logging. Planning Poker happens from the issue panel—no separate sessions, no invitations, no context switching. Flow Intelligence analyzes data that Jira already captures. The AI coach reads patterns automatically and suggests experiments. There is nothing for the team to fill in.

When they ask about ROI A team whose estimates have a Coefficient of Variation above 50% is functionally unable to forecast. Every slipped sprint cascades: scope gets cut, deadlines move, stakeholders lose trust, and the team spends the next sprint explaining instead of building. One predictability improvement—say, discovering that 8-point stories are three times more variable than 5-point stories and slicing accordingly—can stabilize an entire quarter of delivery. The tool pays for itself the first sprint it prevents a spillover conversation.

When they ask "Can't the Scrum Master just track this?" A Scrum Master who says "I think reviews are slow" gets pushback. A Scrum Master who says "our review stage averaged 2.8 days last sprint, and adding 5 items mid-sprint extended our cycle time by 32%" gets action. Smart Guess gives Scrum Masters the data to back up what they already sense—turning intuition into evidence that leadership acts on.

Make the case to your team

From: you@company.com
To: your-team@company.com
Subject: What if our estimates actually predicted delivery?
 

Hey Team,

Quick question: do we actually know if our estimates match
reality?

We run Planning Poker, we plan our sprints—but when a 2-point
story takes anywhere from 1 day to 11 days, something's off. And
when retro rolls around and someone asks "why did this take so
long?"—do we have actual answers, or just opinions?

I found a tool that connects our estimates to actual delivery
data. Here's what it does:

Predictive Planning Poker & Flow Intelligence

During estimation:
- Shows what stories of this size actually cost us historically:
  "Likely 2.1d, Plan for 2.9d, Worst 3.4d"—right in the issue
  panel, no separate tool needed
- After 5+ stories, generates a session debrief highlighting
  which stories carry the most uncertainty before sprint kickoff
- Supports real-time Planning Poker, async Planning Poker, and
  Bucket Estimation—all from the same interface

After the sprint:
- Flow Intelligence breaks down predictability by story point
  size and work type—so we can see if our 5-pointers are all
  over the place while our 3-pointers are solid
- Identifies bottleneck stages: are code reviews the constraint,
  or is QA the real issue?
- Tracks trends across sprints so we can see if we're actually
  improving
- AI coach (Noesis) analyzes our specific patterns and suggests
  concrete experiments—not generic agile advice

One important note: this measures our process, not individuals.
There's a big difference between "your review took 3 days"
(surveillance) and "our 5-point stories have 87% cycle time
variability—what's causing the scatter?" (process improvement).
The data helps us ask better questions as a team.

I'd like to run it for a sprint or two and see what it reveals.
Any reason not to give it a try?

Best,
[Your Name]
 

Team discussion points

When someone worries about surveillance Smart Guess measures systems, not people. It shows that "our review stage averaged 2.8 days" or "our 8-point stories are three times less predictable than 5-point stories"—not who caused the delay. The goal is to identify process constraints so the team can fix them together.

When someone mentions tool fatigue Planning Poker runs directly from the Jira issue panel. One click to start estimating—no separate app, no session setup, no team invitations. Flow Intelligence reads data Jira already tracks. Zero support tickets in the last twelve months despite record user growth. It works because it stays out of the way.

When someone worries the data will look bad That's actually the point. If 5-point stories take anywhere from 2 to 14 days, knowing that is better than guessing about it. The data gives the team ammunition to push back on unrealistic sprint plans, request dedicated review time, or agree to slice stories differently. Evidence replaces opinion-based arguments—and that protects the team.

When someone asks "Can't we just use the Jira board?" The board shows current status. Smart Guess shows whether your process is predictable. A story "In Progress" on the board tells you nothing about whether it will take 1 day or 11. Smart Guess shows you the historical range, flags the outliers, and helps you understand why they happened.

Make the case to security

From: you@company.com
To: it@company.com
Subject: Smart Guess for Jira — Fortified Security, Atlassian Infrastructure
 

Our team has been evaluating Smart Guess, an AI-powered Agile
Coach that analyzes our Jira data to identify bottlenecks and
improve team performance:

Predictive Planning Poker

We understand that security is a top priority—rightfully so.
Here's why Smart Guess meets our strict security standards:

1. Atlassian Cloud Fortified (July 2024): Smart Guess Pro holds
   Cloud Fortified status, meaning it meets rigorous security,
   reliability, and support benchmarks with enterprise-grade
   validation.

2. Cloud Security Participant: Smart Guess Pro and Smart Guess
   Free are part of the Atlassian Marketplace Cloud Security
   Participant Program, with a $5,000 USD Bugcrowd reward pool
   since 2022 and ongoing testing through Atlassian's Bug Bounty
   Program.

3. Data residency: All persistent data resides exclusively on
   Atlassian Forge Storage. User names and avatars are retrieved
   on-demand via API and never persisted.

4. Built on Atlassian Forge: All runtime, storage, and
   communication managed by Atlassian's platform (AWS-hosted).
   No local servers or independent data centers.

5. AI data handling: Only aggregated workflow metrics (cycle
   time, throughput, WIP limits, issue keys, state transitions)
   reach the AI coach. No summaries, descriptions, comments, or
   personal information are transmitted.

6. Security practices: Mandatory two-factor authentication,
   Snyk Code (SAST) and Snyk Open Source CI/CD scanning,
   critical vulnerabilities resolved within 2 weeks, high-
   priority within 4 weeks, full disk encryption, prohibited
   external storage, same-day credential disablement upon
   termination.

7. Incident response: Six-step process (investigate, notify
   Atlassian, contain, remediate, notify customers, post-
   incident review). Security contact: security@smartguess.is

8. Policy documentation:
   - Information Security
   - Incident Management

9. Permissions Smart Guess requires and why

Since Smart Guess runs entirely on the Atlassian platform, it
inherits the same infrastructure security as our Jira instance.
Should I go ahead with the trial, or is there anything specific
you'd like me to check first?

Best,
[Your Name]
 

Security discussion points

When they ask about data residency All persistent data stays on the Atlassian platform infrastructure via Forge Storage. Smart Guess operates no local servers and maintains no independent data centers. User data inherits Atlassian's enterprise security and recovery features.

When they ask about AI / LLM data Only aggregated workflow metrics reach the AI coaching features—cycle time, throughput, WIP limits, issue keys, and state transitions. Issue summaries, descriptions, comments, and personal information are never shared with the AI model.

When they ask about vulnerability management Snyk Code (SAST) and Snyk Open Source scanning run continuously in CI/CD. Critical vulnerabilities are resolved within 2 weeks; high-priority within 4 weeks. The Atlassian Bug Bounty Program maintains a $5,000 USD Bugcrowd reward pool for ongoing external security testing.

When they ask about compliance certifications Smart Guess leverages Atlassian platform-level certifications. The Cloud Fortified badge (awarded July 2024) independently validates enterprise-grade security, reliability, and support standards. This is Atlassian's highest tier of marketplace app validation.

Why Smart Guess? The evidence.

These resources provide supporting detail for any stakeholder conversation:

• "Why Is My Sprint Unpredictable? (And How to Fix the Spillover)" walks through Flow Intelligence step by step—dashboard, trends, predictability analysis, AI-guided root cause identification, and data-backed retrospectives.

• "How to Run Planning Poker Refinement in Jira" shows how estimation works from the Jira issue panel: organizing stories, revealing estimates, reading historical delivery data, and using the session debrief to catch uncertainty before sprint kickoff.

• "The Scrum Master Role Is Evolving—And Only Data-Backed SMs Will Survive" explains how flow metrics turn intuition into evidence—with real examples of Scrum Masters using data to protect their teams and prove their value.

• "Why Agile Teams Love Smart Guess" covers what makes Predictive Planning Poker different: estimates connected to delivery history, one-click estimation, multiple estimation styles, and the philosophy that predictability starts before the sprint begins.