Throwback Thursday: Memorable Confluence page? #TBT

[#1]

It's Thursday!

I once worked at a company where the person responsible for the service delivery owned Jira and Confluence. That company went all out with employing these two tools as the solution to any problem or anything that needed a "system." He made a pretty cool, elaborate Confluence page for the weekly service quality meetings. SLA charts, lists from Jira, info from Klipfolio, customer feedback on work items, uptime, critical work items, CRM info from SugarCRM, etc. That page gave great information at a glance with quite a bit of detail. Some of the graphs and info came directly from Jira via the integration, other things were copied from other systems. Thinking back to it, that page was quite the thang!

(Not sure why I chose this giphy but I did so there you go...)

Soooo, is there a Confluence page (or space) from your past that really stands out that you want to tell us about? Maybe because of a big project, a fun team wiki, or even a mistake that turned into a learning moment or something memorable?

Let’s make it a great Thursday!
KGM

[Stephen_Lugton]

I have a Confluence page with a list of training resources for Agile Delivery Managers, created by ChatGPT and reviewed by Rovo:

Note: as with all links online, use these links with care and preferably with a safe search / malicious link blocker tool

Foundational AI Understanding

1. AI for Everyone – Andrew Ng (Coursera)

Link: https://www.coursera.org/learn/ai-for-everyone
Why useful for an ADM: Builds a clear, non‑technical mental model of what AI can and cannot do, helping you set realistic expectations in delivery, planning, and reporting conversations.
Estimated time: 2–3 hours (selected sections)

2. What Artificial Intelligence Can and Can’t Do – Harvard Business Review

Link: https://hbr.org/2019/01/what-artificial-intelligence-can-and-cant-do
Why useful for an ADM: Provides executive‑grade language for pushing back on unsafe or unrealistic AI use in delivery decisions.
Estimated time: ~10 minutes

3. AI vs Automation – Plain English (Video)

Link: https://www.youtube.com/watch?v=H0BRt7Z0yDk
Why useful for an ADM: Clarifies why AI outputs are probabilistic rather than deterministic—critical when managing commitments and forecasts.
Estimated time: 10–15 minutes

Responsible & Enterprise AI

4. Human‑in‑the‑Loop AI – IBM

Link: https://www.ibm.com/topics/human-in-the-loop
Why useful for an ADM: Defines how humans remain accountable for AI‑assisted decisions, directly supporting delivery ownership and governance.
Estimated time: ~10 minutes

5. AI Risk & Governance in the Enterprise – World Economic Forum

Link: https://www.weforum.org/stories/2023/06/ai-risk-governance-enterprise/
Why useful for an ADM: Provides enterprise‑level guidance you can reference when designing AI‑assisted Agile workflows that must pass audit or risk review.
Estimated time: 10–15 minutes

6. NIST AI Risk Management Framework (Overview)

Link: https://www.nist.gov/itl/ai-risk-management-framework
Why useful for an ADM: Offers a formal structure for identifying, managing, and monitoring AI risks in delivery environments.
Estimated time: 15–20 minutes (overview)

7. Why AI Needs to Be Monitored – Harvard Business Review

Link: https://hbr.org/2021/10/why-ai-needs-to-be-monitored
Why useful for an ADM: Reinforces why AI must be inspected and adapted like any delivery system, not treated as “set and forget.”
Estimated time: ~10 minutes

8. Model Drift – IBM

Link: https://www.ibm.com/topics/model-drift
Why useful for an ADM: Explains how AI outputs degrade over time, helping you define stop rules and monitoring for delivery use cases.
Estimated time: ~10 minutes

Prompt Engineering & Human–AI Collaboration

9. Prompt Engineering for Developers – http://DeepLearning.AI

Link: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/
Why useful for an ADM: Teaches how to structure prompts so AI outputs are predictable, reviewable, and safe to use in Agile ceremonies.
Estimated time: 1.5–2 hours (core lessons)

10. The Prompt Engineering Guide

Link: https://www.promptingguide.ai/
Why useful for an ADM: Serves as a practical reference for prompt patterns you can reuse across planning, reporting, and risk analysis.
Estimated time: 15–30 minutes (reference use)

11. Why Large Language Models Hallucinate – http://DeepLearning.AI

Link: https://www.deeplearning.ai/the-batch/why-llms-hallucinate/
Why useful for an ADM: Explains why AI can sound confident but be wrong—critical when reviewing sprint summaries or delivery narratives.
Estimated time: 5–10 minutes

Agile & Delivery‑Specific AI Use

12. Using AI in Agile Planning – Atlassian

Link: https://www.atlassian.com/blog/artificial-intelligence/ai-agile-planning
Why useful for an ADM: Shows realistic, low‑risk ways to use AI for planning prep without replacing team judgment.
Estimated time: 10–15 minutes

13. Using AI for Agile Backlog Summarization – Atlassian

Link: https://www.atlassian.com/blog/artificial-intelligence/ai-agile-backlog
Why useful for an ADM: Demonstrates how AI can reduce backlog admin while preserving Product Owner and team accountability.
Estimated time: ~10 minutes

14. Using AI to Summarize Sprint Reviews: What Can Go Wrong – Medium

Link: https://medium.com/agileinsider/using-ai-to-summarize-sprint-reviews-what-can-go-wrong-9b2a9a7c9c6a
Why useful for an ADM: Highlights common failure modes that can quietly damage trust if AI outputs are shared unreviewed.
Estimated time: ~10 minutes

Data Literacy & Analytics

15. Google Data Analytics – Foundations: Data Quality (Coursera)

Link: https://www.coursera.org/learn/foundations-data
Why useful for an ADM: Builds intuition for spotting bad or misleading delivery data before it informs AI or forecasts.
Estimated time: 45–60 minutes (selected sections)

16. Pandas “10 Minutes to pandas” (Official Documentation)

Link: https://pandas.pydata.org/docs/user_guide/10min.html
Why useful for an ADM: Provides just enough Python literacy to inspect and sanity‑check delivery datasets without deep coding.
Estimated time: ~30 minutes

17. Storytelling with Data – Blog

Link: https://www.storytellingwithdata.com/blog
Why useful for an ADM: Helps you present AI‑ or data‑derived delivery insights honestly and clearly to stakeholders.
Estimated time: 15–20 minutes (per article)

18. Atlassian – Agile Project Management Metrics

Link: https://www.atlassian.com/agile/project-management/metrics
Why useful for an ADM: Grounds AI and ML insights in Agile‑native metrics, reducing misuse of velocity or forecasts.
Estimated time: ~15 minutes

19. Correlation Does Not Imply Causation – Towards Data Science

Link: https://towardsdatascience.com/correlation-does-not-imply-causation-why-this-is-so-important-16cbbe33e6a9
Why useful for an ADM: Prevents misinterpretation of AI‑surfaced delivery trends as causes or commitments.
Estimated time: ~10 minutes

Machine Learning & Forecasting Concepts

20. Machine Learning for Everyone – Andrew Ng (Coursera)

Link: https://www.coursera.org/learn/machine-learning-for-everyone
Why useful for an ADM: Explains ML concepts at decision‑maker level so you can challenge forecasts without building models.
Estimated time: 60–90 minutes (selected sections)

21. Why Data Science Projects Fail – Harvard Business Review

Link: https://hbr.org/2020/11/why-data-science-projects-fail
Why useful for an ADM: Identifies organizational and judgment failures that often derail AI initiatives in delivery contexts.
Estimated time: ~10 minutes

22. scikit‑learn Linear Models (Read‑Only Reference)

Link: https://scikit-learn.org/stable/modules/linear_model.html
Why useful for an ADM: Demystifies what regression models actually do so you can interpret outputs responsibly.
Estimated time: ~15 minutes

Deep Learning & LLM Concepts

23. Generative AI with Large Language Models – http://DeepLearning.AI

Link: https://www.coursera.org/learn/generative-ai-with-llms
Why useful for an ADM: Provides a conceptual understanding of how modern GenAI works and fails—without technical depth.
Estimated time: 2–3 hours (conceptual sections)

24. The Illustrated Transformer – Jay Alammar

Link: https://jalammar.github.io/illustrated-transformer/
Why useful for an ADM: Visually explains the architecture behind LLMs, helping you reason about their limitations.
Estimated time: ~15 minutes

25. When Not to Use Generative AI – Harvard Business Review

Link: https://hbr.org/2023/07/when-not-to-use-generative-ai
Why useful for an ADM: Gives authoritative backing for declining unsafe GenAI use in delivery decisions.
Estimated time: ~10 minutes

Enterprise Tooling Awareness

26. Atlassian Intelligence

Link: https://www.atlassian.com/software/artificial-intelligence
Why useful for an ADM: Shows how GenAI is embedded directly into Agile tooling, where guardrails are essential.
Estimated time: ~10 minutes

27. Microsoft Copilot for Work

Link: https://learn.microsoft.com/en-us/copilot/
Why useful for an ADM: Helps you understand how AI may influence documentation, reporting, and planning across the delivery ecosystem.
Estimated time: 10–15 minutes

[Samia]

Great example 🙂

I haven’t personally experienced this kind of setup yet, but I’m honestly surprised by how powerful a Confluence page can be — to the point of almost matching a real dashboard.

It really shows how far you can go in centralizing information and making it actionable in one place.

[Mikael Sandberg]

At a previous job we created a JSM portal in Confluence with the help of some marketplace apps. This was before you could create landing pages in JSM and CSM. The reasoning was that we have all the KBs in Confluence and the way the JSM portal works didn't do a good job for self serve. By doing it in Confluence we could link to common request types, allow the user to search our KBs, present news and tips, and show data via Analytics on how busy the team was all in one place.

[Barbara Szczesniak]

At a former job, they used a Confluence space for Change Control. Teams posted their proposed changes for the next release with an explanation for approval. When updating the documentation for the new release, this was a great place for me to get an overview of the changes and the reasoning behind them.

[John Funk]

Personally, I haven't done a lot in Confluence. However, all of our company data is in there and the various team constantly create new pages and keep things up to date. So, that's cool!

[Martin Runge]

I loved seeing a few weeks, that one of our smaller customers (acomm) has all, I mean really every customer documentation nicely organized in Confluence, even though the customer often only receives the PDF export.

Internally, we have great budget overviews for some our customers and it is amazing how much data can be pulled out and summarized. Amazeballs!

[Tomislav Tobijas]

I believe the first cool thing I did in Confluence was the onboarding page I initially created in my previous workplace. I did create LI article about it if you wanna know more 👀

I would need to refine it a bit and maybe create a newer version, but it's a pretty decent template.

[Anwesha Pan]

In my org, we have Knowledge Base wiki in Confluence created & maintained by me and my team. Such a resource can significantly enhance productivity and self-service capabilities within our organization.

I was pleased to see all the information coming together in this wiki.

[Kristján Geir Mathiesen]

Takk for you comments and...

[Matt Doar]

Greatest Page Ever was the "Employee of the Month" page at a previous employer. How it was used:

1.  Ask the newish employee if they have seen the page yet

2. Watch them as they see their own name appear in great, big letters at the top of the page

3. Even better if they leave a comment on the page saying "thank you, this is so unexpected"

A bit later on, explain how some twisted person created that page with a user macro to display the name of the current user as this month's winner.

I certainly did the third step!