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Hello, my name is Andre Petaja.

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Let me show you how I use boards that I develop that use capabilities of ChatGPT in order to perform

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project management operational activities.

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Right now, I want to show you the scenario, how I use board with ChatGPT to boost my sprint planning

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and sprint preparation activities.

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If you are a project manager who has experience with Scrum methodology, you know that during spring

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planning it is better to know teams capacity for the next sprint in order to be more accurate during

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the planning.

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There are different approaches how to calculate teams capacity, but one of the approaches that I use

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is calculation of sprint capacity based on the average velocity in story points.

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So usually I take amount of story points delivered in the last 2 or 3 sprints and calculate average

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velocity.

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Then, based on the average velocity, I define teams capacity in order to plan sprint.

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In the current example you can see that I created fake sprints with fake data in order to have some

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data for the use case that I want to demo.

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I closed three sprints with the user stories and I received the following data in the sprint.

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Number one, my team delivered 16 story points in the sprint.

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Number two, my team managed to complete 14 story points and in the sprint three also 14 story points.

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We are going to use this data in our example in order to review end to end scenario.

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And now imagine that I have a conversation in Slack Messenger with my team members or with my Scrum

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Masters and I just want to get more information about the average velocity.

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I need this information to hold planning.

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I can just ask my bot in chat.

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What is the average velocity based on the previously completed three sprints and receive a response.

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14.67 story points, which is correct based on the data we received from Jira.

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I can clarify the average velocity based on the previous two sprints and the answer is 14 story points

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is it is also correct because in Sprint two and Sprint three, my team delivered 14 story points per

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each sprint and I know that I can open velocity charts in Jira and take information from there.

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But what if this is a live chat and I want to receive information faster?

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And this scenario is very simplified demo because in reality you can integrate these commands into more

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complex scenarios like email preparation and its auto generation reports generation and many other different

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generation scenarios.

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But even using it in chat is pretty comfortable knowing this information.

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I can proceed with planning, but in order you can understand the use case, let me show you how my

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backlog looks like now.

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In my backlog, I already have estimated workitems that by the way, also may be estimated with the

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help of board and ChatGPT based on the analysis of the text description of the previous user stories.

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Or we can train and fine tune our model in order to teach it how to define complexity based on the requirements.

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But that case will be described and shown in the separate demo.

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So here in the backlog, you can also see that user stories have priorities.

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And when I plan Sprint, I want to take into consideration priorities and plan the user stories with

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highest priorities and then with lowest, you can see that there are two heavy user stories with high

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priority eight story points each.

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That means, unfortunately, both of them doesn't fit into Sprint.

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If our capacity is 14 story points and in this case, remaining space in Sprint should be filled out

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by user stories with lower priorities.

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Does it sound logical for you?

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So let me ask now our board to plan Sprint four Considering that our capacity is 14 story points, I

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decided to use average velocity based on the previous two sprints to calculate our capacity for the

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next sprint.

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And after a few moments my boss replies me that sprint was planned and such stories are included into

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the sprint for the plan was done with respect to teams capacity.

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Let me open Jira now and make sure that these changes are reflected.

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So I refresh the page and I can see that Sprint four is planned with top priority items for certain

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story points, and the only thing that is left is to start the sprint.

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Another user story was high priority for eight story points, unfortunately doesn't fit into the sprint

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and will be left for the following sprint planning.

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That's how using chat interface I can check average velocity in story points and plan the following

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sprint.

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And just keep in mind that this is just one example and variety of other combinations or options for

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how you can build your own board with chat.

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GPT integration is unlimited.

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Okay, let's review another example.

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Now, I would like to show you how my board that is integrated with ChatGPT helps me to manage risks

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and work with risks on daily basis.

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Risk management.

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That is something what is not specific only for scrum projects.

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That's why no matter whether you worked with Scrum before or no, this example should be clear for you.

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In case you are familiar with project management techniques according to PMI.

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Let me start from the preconditions.

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In our specific example, I configured separate board for risk management.

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On this board I have only items with risk issue type.

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This is a custom type that I created.

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There are six risks.

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Each risk has its own impact and probability.

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Configure it and imagine that I want to streamline our conversation and have a chat about risks directly

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in the messenger with my colleagues so I can use my board to ask questions about risks that we have

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on our project.

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For example, let's start with simple questions.

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How many risks we have in total?

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And when Jira is connected as a data source to the board.

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I use ChatGPT linguistic capabilities to understand my request and provide me with the response.

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In case you set priorities to risks and you need to review risks by priorities, I can ask board to

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provide me with the requested information and you can see that I have one ticket with the highest priority,

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one with high priority, and the rest of risks have the medium priority on the Kanban board.

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You can check how accurate our board is and you can see that both provided us with correct information

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about priorities.

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That is because it is smart enough to process the data that is provided to the board on request.

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ChatGPT identifies what information is needed and asks my board to provide necessary information.

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My board fetches required information and provides it back to the GPT for the further analysis.

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That is what is happening in the background.

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If we want, we can ask to group all risks by statuses and a few moments after I receive the response,

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we can see here clear breakdown of risk items by statuses.

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I can request to group risks by impact and again, I receive information that I need that was analyzed

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and processed by ChatGPT.

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And you can ask to analyze any data you need.

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If you need to group risks by probabilities.

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You can also ask what to do.

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So believe me, result will be correct.

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And instead of this, imagine that you want to identify responsible team members who are in charge of

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risks that are in backlog state.

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I just want to know names of these heroes and team members in order to follow up with them and ask them

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to process risks as soon as possible and ask this question to my board.

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And it replies me that Andre Petaja is assigned to two risks that are in the backlog state now.

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Now I know the name with whom I need to follow up.

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I can tag the risk owner here or configure cron job with reminder.

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For example, each morning responsible people are attacked in case they have risks that are still not

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processed.

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This would be ideal for big programs and it will help to boost the communication inside the team significantly.

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Any team member, even without opening a Jira, can ask a follow up questions.

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Like, for example, we can request additional details about these two risks in order to remember what

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are they about and what provides me with only necessary information In order I can get better context

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of the risks.

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In response, I can find information about assignee, risk, description, impact and probability.

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GPT also knows that each of these risks has its own mitigation plan and response strategies.

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So let me then clarify these details and ask for additional details.

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And this time I see information about mitigation plan and response strategies included.

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The interesting thing to remember about is that ChatGPT sometimes generates additional details.

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For example, in our specific case response strategy is just a single value from the dropdown.

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But instead of writing that response strategy is mitigate.

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We can see that additional text is added like in this example, implement measures to mitigate risk

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instead of just mitigate.

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The main idea is delivered correctly, but be aware that GPT can generate additional wording sometimes.

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In the previous video I already showed you how we can create Jira issues using chat interface.

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The same things may be applied here.

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We can use our bot together with a chat.

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GPT capabilities to generate risk Description.

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Ask GPT to suggest US mitigation plan and put this data directly into the ticket.

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I just don't see the reason to demo all seamless scenarios.

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The main thing that I wanted to show you in this video is that you are not bound to the default types

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in Jira.

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You can customize your bot and take advantage of ChatGPT capabilities to build any customized business

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logic or business flow that you need.

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That's all for this video.

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In case you are interested in the topic, check video description and feel free to ask your questions

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in comments to the video.

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I hope you enjoyed the video.

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Put your thumbs up, leave the comments and follow the channel to not miss other interesting videos.

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Have a great day.

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Bye.