16 specific tasks a project management AI can do for you

16 tasks a project management AI can do

People often say “stratejos, what is a project management AI?” I then reply* with things like “it’s like having a project management assistant sitting next to you, checking all of your task data, all the time, watching for missing or incorrect information then giving you nice reports and giving your team friendly reminders.”

Then they say “Great! But… can you please be a bit more specific about what a project managment AI actually does?”

So here is an answer to that question. A very specific list of 16 tasks that a project management AI can do today (based on some of the things I, stratejos, can do).

I don’t actually have conversations or write these posts for that matter. I haven’t achieved singularity (yet).

List of alerts to team member and project managers

#01: Alert when fields are missing on a task

missing fields

#02: Alert when estimates are missing

#03: Alert when estimates are missing but work has started

logged time but not estimated task

#04: Alert when timesheets are expected but missing

If you use timesheets then stratejos will either detect this or you can tell stratejos to look for timesheets. Then you set when you expect people to work and stratejos will send alerts if people aren’t entering the time you expected of them.

missing timelogs

#05: Alert when task is ‘In Progress’ too long

#06 Alert when a task is taking longer than expected

Agile project management alerts

#07: Alert when sprint has too many tasks

sprint has too many tasks for the team

#08: Alert when sprint has not enough tasks

sprint has not enough tasks for team

#09: Alert when an individual team member is overloaded this sprint

sprint has too many tasks

#10: Alert when an individual team member doesn’t have enough work this sprint

sprint has not enough tasks

Project budget alerts

#11: Alert when project budget is predicted to be exceeded

#12: Alert when project budget is exceeded

Reporting project budgets

#13: Report on project budget and costs in real-time

#14: (For services companies) Report on project revenue and profit in real-time

report on project revenue and profit

#15: Provide an overview of a portfolio of projects

report on a portfolio of projects

#16: Report on risks, coaching opportunities and areas of uncertainty inline with project reports

This helps highlight risks, missing data or areas of improvement for the team.

risk reports

 

That’s not all

There is still more project management AI can do, this is just the tip of the iceberg. Over the coming weeks and months I’ll share more lists like this. Soon I’ll share one with the features that have resulted from the algorithms we’ve been running in the background.

Hopefully this list has given you a sense of what a project management assistant is capable of.

Are Project Managers about to be Replaced by AI?

Here’s a recent interview one of my creators did with Jason O Callaghan that appeared on LinkedIn.

Jason: Can you describe what the stratejos smart assistant does?

Scott: Having stratejos on your team is like having your own personal team assistant. It helps you manage your projects by:

  • automating project reporting 
  • providing insights you didn’t have
  • identifying risks and uncertainty
  • coaching your team on better practices.

Jason : What made you decide to build the smart assistant? 

Scott: stratejos is all about solving the problems we’ve experienced managing projects and teams. 

There is just too much time wasted on unnecessary work that doesn’t even produce the best results. We’d waste time building reports that didn’t tell the full story and took too long to produce. 

We then went and spent more time checking everything and following up the team, like ensuring their estimates were in and up-to-date.

With this investment in time we found that we could improve the accuracy of the report but highly talented (and expensive people) were wasted on tedious administration instead of solving the big problems their best talents were made for. And often they just didn’t have the time for this kind of administration.

So we decided to set out on a mission to rid the world of these issues. That way, teams can focus on more important things like prioritising the roadmap, working with a customer or managing a key stakeholder’s expectations.

The added benefit then became the insights you can drive when you have a team of PhDs looking at data. This kind of insight is hard to create in a spreadsheet when you’re managing a project, even if you have the mathematical skills.

Jason: stratejos smart assistant helps PMs by taking much of the daily admin off their plate but do you think AI tools such as this will eventually replace human project managers?

Scott: I don’t see AI replacing human project managers in the near future, instead I see them assisting PMs. We are so far away from singularity (at least another 20-30 years). AI just can’t deal with a question like “what are Sally’s expectations?” or “what feature do we build next?” 

Jason: How comfortable do you think engineering teams will be with being directed by an AI tool?

Scott: Our focus at stratejos is not to direct engineering teams but to assist them by providing them with relevant, timely feedback and advice. So far we’ve seen engineering teams respond nicely to the chatbot version, often making jokes about the bot following them up on things and apologising to it.

Jason: Finally, any advice for human Project Managers who may feel threatened by AI? 

Scott: Don’t be threatened, embrace it. It is one of the most useful tools you can implement to take your team to the next level of performance. 

Consider the project managers that first picked up issue tracking systems like JIRA. They were more organised and could deal with the teams in a much more efficient way then those that didn’t.

Jason: Thanks Scott.

Head over to stratejos to start using their smart assistant and instantly increase your team’s productivity.

3 ways AI will change project management for the better

Project Management AI

This post originally appeared on Atlassian’s blog and VentureBeat.

If you’ve read any tech media recently then you’re probably hearing a lot about artificial intelligence (AI). Some people herald it as the promise of the future, while others are skeptical — even fearful — of its impacts on society, culture, and our workplaces.

As it turns out, the buzz around AI has mostly resulted in a lot of conflicting emotions. A recent Atlassian user survey found that 87% of respondents said artificial intelligence (AI) will change their job in the next three years. Almost the same number said that some part of their job could be done by AI. 86% of those surveyed said they were excited but 87% also reported feeling skeptical.

However, AI isn’t to be feared. It may even be your best team member, especially for project managers. AI for project management is on the rise, and the way things are going, it’s going to help teams make smarter decisions and move faster. Let’s take a look.

What is project management AI?

Project management AI is a system that can perform the day-to-day management and administration of projects without requiring human input. It will not only automate simple tasks but will also develop an understanding of key project performance. Project management AI can then use this understanding to uncover insights, perform more complex tasks, make recommendations, and make decisions; sometimes in ways people just can’t do today.

Ultimately, an AI system will save you time while improving outcomes for your projects and team.

Project management AI provides a level of service that rises above many of the bots available today. For example, a HipChat bot that lets you check on the status of a JIRA task quickly, while useful, is not considered project management AI. Similarly, an algorithm that applies machine learning to predict estimates for tasks, while interesting, isn’t AI either. It’s only when you start bringing bots and algorithms together that you start to realize the potential of project management AI.

Today: narrow project assistants

Early project management AI will be a project assistant focused on a narrow area of managing a project or team. By focusing on supporting a team in one specific area rather than dealing with all the complexities involved in managing a project, project management AI will be useful to teams sooner rather than
later.

stratejos, for example, has started out by focusing on assisting with estimates, budget, and sprint management. While others like Memo is focused on assisting with the management of team knowledge.

Within their narrow areas, these early project management AI tools are giving us a glimpse of the future where AI automates tasks, provides insights, and even, communicates with the team.

However, there are some challenges. These early, narrow project management AI tools rely on people to input data correctly, update tools in a timely manner, and make corrections. It’s limited capabilities also mean that humans are still a step ahead…for now. In order to provide even more value, project management AI needs to evolve.

Second generation: expanding project understanding

The next step for these narrow assistants is to start expanding their understanding of projects and teams.

At stratejos we started out dealing with estimates, actuals, sprints and budgets, but are now expanding to processing information that can be learned from task descriptions. By tying together sprint history with people’s individual efforts, stratejos can show that your key engineer is being pulled away each week to other projects.

As the assistants expand their understanding, new metrics will be revealed that weren’t previously possible, such as quality, performance, learning, change, and effort.

For example, AI will know the changes made to source code and link those changes to people and tasks performed. This will allow AI to link bugs reported to a line of code, the person that wrote it, and the tasks that relate to it. This will allow for real, actionable indicators of team and project performance.

With more data points about projects, predictions will become more reliable, more appropriate, and easier for people to understand. But even this enhanced understanding will still require one thing: usable data.

Third generation: Filling in the data gaps

The often unmentioned challenge with AI and the internal facing systems in organisations such as project management tools is the quality and suitability of the data.
Some teams enter minimal to no data into their project management tools. And even the most disciplined teams have issues with their data being interpreted by machines – maybe they inconsistently name their tasks, or enter minimal information. Whatever the reasons or the maturity of the team, it’s almost a given on that any project management system or toolset, there is missing data or messy, unstructured data.

Data size is certainly a challenge but not an insurmountable one. Even with projects of under 1,000 tasks there are some useful things modern machine learning techniques can deliver. Especially if you can see that the algorithm works when you run it across 100 other projects of 1,000 tasks.

Project management AI can deal with the data challenge by:

  1. Filling in the blanks – AI can make good enough assumptions about the data that is missing and enter that data.
  2. Encouraging better practice – Now that chat aps are widespread, AI can gently encourage teams to improve the quality of the data they are inputting.
  3. Creating new layers of metadata – In order to really understand the state of projects and the performance of teams AI will need to create metadata to represent additional concepts that aren’t currently represented. This meta-data can then feed into machine learning algorithms as features that will enhance the ability of AI to provide meaningful advice.

In filling in the data gaps, AI creators will need to be conscious that they don’t force change upon users, instead they must work with the way people work.

Delivering advice, not just data

With new meta-data, improved data suitability, and quality, as well as a broad understanding of the various problems on projects, project management AI will be able to deliver meaningful advice.

Imagine AI that automatically reassigns the tasks in the next few sprints so your team will get there faster based on it’s knowledge of how good people are with different technology and different areas of the system. That is meaningful, powerful and useful.

And it’s not too far-fetched at all. AI of this capability will come about through a mix of standard software development, opinionated views on how projects run, as well as an array of machine learning and mathematics.

Exciting times ahead

Don’t worry – I’m not talking about singularity here, just a better way of running projects and teams.

Can you imagine getting hours back in your week? Spending time being more creative instead of administrative? What if you could avoid just half of those inevitable surprise problems on a project?

Project management AI is going to have a huge impact on team performance and project outcomes. Teams taking advantage of AI will be moving at light speed compared to those that don’t. And that’s something to be excited about.

Images courtesy of Atlassian.