Hello, my name is Andre Petaja.
Let me show you how I use boards that I develop that use capabilities of ChatGPT in order to perform
project management operational activities.
Right now, I want to show you the scenario, how I use board with ChatGPT to boost my sprint planning
and sprint preparation activities.
If you are a project manager who has experience with Scrum methodology, you know that during spring
planning it is better to know teams capacity for the next sprint in order to be more accurate during
the planning.
There are different approaches how to calculate teams capacity, but one of the approaches that I use
is calculation of sprint capacity based on the average velocity in story points.
So usually I take amount of story points delivered in the last 2 or 3 sprints and calculate average
velocity.
Then, based on the average velocity, I define teams capacity in order to plan sprint.
In the current example you can see that I created fake sprints with fake data in order to have some
data for the use case that I want to demo.
I closed three sprints with the user stories and I received the following data in the sprint.
Number one, my team delivered 16 story points in the sprint.
Number two, my team managed to complete 14 story points and in the sprint three also 14 story points.
We are going to use this data in our example in order to review end to end scenario.
And now imagine that I have a conversation in Slack Messenger with my team members or with my Scrum
Masters and I just want to get more information about the average velocity.
I need this information to hold planning.
I can just ask my bot in chat.
What is the average velocity based on the previously completed three sprints and receive a response.
14.67 story points, which is correct based on the data we received from Jira.
I can clarify the average velocity based on the previous two sprints and the answer is 14 story points
is it is also correct because in Sprint two and Sprint three, my team delivered 14 story points per
each sprint and I know that I can open velocity charts in Jira and take information from there.
But what if this is a live chat and I want to receive information faster?
And this scenario is very simplified demo because in reality you can integrate these commands into more
complex scenarios like email preparation and its auto generation reports generation and many other different
generation scenarios.
But even using it in chat is pretty comfortable knowing this information.
I can proceed with planning, but in order you can understand the use case, let me show you how my
backlog looks like now.
In my backlog, I already have estimated workitems that by the way, also may be estimated with the
help of board and ChatGPT based on the analysis of the text description of the previous user stories.
Or we can train and fine tune our model in order to teach it how to define complexity based on the requirements.
But that case will be described and shown in the separate demo.
So here in the backlog, you can also see that user stories have priorities.
And when I plan Sprint, I want to take into consideration priorities and plan the user stories with
highest priorities and then with lowest, you can see that there are two heavy user stories with high
priority eight story points each.
That means, unfortunately, both of them doesn't fit into Sprint.
If our capacity is 14 story points and in this case, remaining space in Sprint should be filled out
by user stories with lower priorities.
Does it sound logical for you?
So let me ask now our board to plan Sprint four Considering that our capacity is 14 story points, I
decided to use average velocity based on the previous two sprints to calculate our capacity for the
next sprint.
And after a few moments my boss replies me that sprint was planned and such stories are included into
the sprint for the plan was done with respect to teams capacity.
Let me open Jira now and make sure that these changes are reflected.
So I refresh the page and I can see that Sprint four is planned with top priority items for certain
story points, and the only thing that is left is to start the sprint.
Another user story was high priority for eight story points, unfortunately doesn't fit into the sprint
and will be left for the following sprint planning.
That's how using chat interface I can check average velocity in story points and plan the following
sprint.
And just keep in mind that this is just one example and variety of other combinations or options for
how you can build your own board with chat.
GPT integration is unlimited.
Okay, let's review another example.
Now, I would like to show you how my board that is integrated with ChatGPT helps me to manage risks
and work with risks on daily basis.
Risk management.
That is something what is not specific only for scrum projects.
That's why no matter whether you worked with Scrum before or no, this example should be clear for you.
In case you are familiar with project management techniques according to PMI.
Let me start from the preconditions.
In our specific example, I configured separate board for risk management.
On this board I have only items with risk issue type.
This is a custom type that I created.
There are six risks.
Each risk has its own impact and probability.
Configure it and imagine that I want to streamline our conversation and have a chat about risks directly
in the messenger with my colleagues so I can use my board to ask questions about risks that we have
on our project.
For example, let's start with simple questions.
How many risks we have in total?
And when Jira is connected as a data source to the board.
I use ChatGPT linguistic capabilities to understand my request and provide me with the response.
In case you set priorities to risks and you need to review risks by priorities, I can ask board to
provide me with the requested information and you can see that I have one ticket with the highest priority,
one with high priority, and the rest of risks have the medium priority on the Kanban board.
You can check how accurate our board is and you can see that both provided us with correct information
about priorities.
That is because it is smart enough to process the data that is provided to the board on request.
ChatGPT identifies what information is needed and asks my board to provide necessary information.
My board fetches required information and provides it back to the GPT for the further analysis.
That is what is happening in the background.
If we want, we can ask to group all risks by statuses and a few moments after I receive the response,
we can see here clear breakdown of risk items by statuses.
I can request to group risks by impact and again, I receive information that I need that was analyzed
and processed by ChatGPT.
And you can ask to analyze any data you need.
If you need to group risks by probabilities.
You can also ask what to do.
So believe me, result will be correct.
And instead of this, imagine that you want to identify responsible team members who are in charge of
risks that are in backlog state.
I just want to know names of these heroes and team members in order to follow up with them and ask them
to process risks as soon as possible and ask this question to my board.
And it replies me that Andre Petaja is assigned to two risks that are in the backlog state now.
Now I know the name with whom I need to follow up.
I can tag the risk owner here or configure cron job with reminder.
For example, each morning responsible people are attacked in case they have risks that are still not
processed.
This would be ideal for big programs and it will help to boost the communication inside the team significantly.
Any team member, even without opening a Jira, can ask a follow up questions.
Like, for example, we can request additional details about these two risks in order to remember what
are they about and what provides me with only necessary information In order I can get better context
of the risks.
In response, I can find information about assignee, risk, description, impact and probability.
GPT also knows that each of these risks has its own mitigation plan and response strategies.
So let me then clarify these details and ask for additional details.
And this time I see information about mitigation plan and response strategies included.
The interesting thing to remember about is that ChatGPT sometimes generates additional details.
For example, in our specific case response strategy is just a single value from the dropdown.
But instead of writing that response strategy is mitigate.
We can see that additional text is added like in this example, implement measures to mitigate risk
instead of just mitigate.
The main idea is delivered correctly, but be aware that GPT can generate additional wording sometimes.
In the previous video I already showed you how we can create Jira issues using chat interface.
The same things may be applied here.
We can use our bot together with a chat.
GPT capabilities to generate risk Description.
Ask GPT to suggest US mitigation plan and put this data directly into the ticket.
I just don't see the reason to demo all seamless scenarios.
The main thing that I wanted to show you in this video is that you are not bound to the default types
in Jira.
You can customize your bot and take advantage of ChatGPT capabilities to build any customized business
logic or business flow that you need.
That's all for this video.
In case you are interested in the topic, check video description and feel free to ask your questions
in comments to the video.
I hope you enjoyed the video.
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Have a great day.
Bye.