Using AI for Agile Project Management - Dude, Where’s My PM?

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Software is evolving. The way we develop software is evolving, too. And, soon, how we manage our entire SDLC will evolve as we’ll start using AI for Agile Project Management. How will AI  change project management? In a lot of ways, but perhaps the greatest impact will come through how fast we can gather and interpret data to make decisions that make it easier for all software development stakeholders to perform at their best. But, let’s start with Agile’s most important value, what it means to be a project manager vs. an Agile Project Manager, and then get into the benefits of AI.

People Come First

Recently, we covered how AI Fits with Agile’s Values and Principles. The first, and arguably most important, of Agile’s values, emphasizes “Individuals and Interactions Over Processes and Tools.” This is not the least bit difficult to reconcile with the goals of successful software delivery.

Successful software projects are delivered on time, on budget, and according to specifications. 

All three criteria relate directly to the people who are developing the software – to their skill and aptitude for teamwork, but also to how they are led and managed. That brings us to the Project Manager – often (but not always) the one person who puts every project into motion!

The Project Manager’s Role - What Do They Do?

In software development, Project Managers have several functions:

  • Due diligence on a project’s feasibility and establishing its characteristics.
  • Cost, Time, and Effort Estimations – how much it will cost to build the software, how long it will take, how many people, and how much of their time will be needed. 
  • Scheduling the effort with Gantt Charts and the like.
  • Select the team/s that will be involved which may include in-house developers, freelancers, contract workers, outsourced development agencies, augmented teams via IT staffing agencies, etc., based on skills matching project requirements.
  • Managing the risks that may impact the schedule, budget, the quality of delivered software, issues that may impact the team, awareness of changes in the industry, etc.
  • Plans for the smooth flow of operations in all areas and contingency plans for any disruptions. 
  • And, of course, A) other tasks as assigned, and B) meetings. 

Project managers are in demand, as we will get to shortly. It’s important to note that there is a difference between being a Project Manager and a Certified Project Manager Professional. 

Hoity-Toity? Maybe – but the PMP certification isn’t easy to get

And then… not all Project Managers or PMPs are necessarily Agile Project Managers. This raises a few questions, like…

Do You Need an Agile Project Manager?

What’s the safe answer here? Technically – no. Agile defines three main roles – product owner, scrum master, and individual contributors. But then, we can find some (or a lot of) overlap in the individual job descriptions of product owner and scrum master depending on organizational size, structure, and maturity. And then, there’s Agile’s focus on self-organizing teams. And then, in some teams, everyone takes turns as the scrum master.

Ok, time for a breather…

If you’d like to look deeper into the role of Project Managers for Agile Projects, check out the following:

That’ll keep ya busy… but there’s more to consider as to whether you really need an Agile project manager. And then… even if you don’t actually need one, you might want one. 

No vs Experienced vs Certified Project Managers

Standish Group’s 2020 CHAOS: Beyond Infinity Report finds that Agile software projects led by experienced project managers are just as likely to fail as they are to succeed, 18% vs 20%. They even go so far as to assert that Agile teams without a dedicated project manager actually succeed more often – 58%!?!?!

As we’ve noted previously, Certified Project Management Professionals, per the Project Management Insitute, do have dramatically higher project success rates (and 22% higher pay). 

Details can be found in their online pdf Pulse of the Profession 2021: Beyond Agility (page 6), suggesting success rates of ~60% or so (averaged across all regions). Certified PMPs have very challenging criteria to meet, and in either case, as with the rest of the IT world, there’s a pervasive shortage of them – and a need for 25 million more by 2030.

Note, as yet, we have not compared the performance of PMPs with a PMI, Prince2, or other certification. PMI certification is regarded as the gold standard, though most certified PMPs have Prince2 certifications.

Project Manager Success Rates

 No PMExperienced PMCertified PMP
Success Rate58%18%~60%

Why the Wild Disparity in Project Success Rates?

The disparity in success rates is counter-intuitive, but there may be some logic to the madness. The core issues here are decision latency and decision quality.

Self-organizing Agile teams tend to have faster decision-making processes. Individual team members can take the initiative and make decisions as relates to their areas of expertise without depending on a project manager. Per Standish Group’s findings, a team able to make decisions on average in one hour has success rates 3x of a team averaging five hours.

Conversely, Certified PMPs have the training and experience to anticipate and have contingencies for a wide range of risks. It’s also likely they’ll factor in some of the more subtle issues that less experienced PMs may overlook. 

Thus, it follows, that a centralized process involving a project manager with less experience culminates in slower, lower-quality decisions. Some of the subtle issues a more experienced PM would consider include:

  • Engaging with business users and other stakeholders to better understand their business requirements and increase their awareness of available options (like what kind of data can be collected) to avoid feature creep during development. And yes, Agile seeks to be adaptable to changes, even late in development, but reasonable due diligence saves a lot of stress and sweat. 
  • Hand-select team members for new projects with the right mix of skills and experience, relative to project specifications. This extends to understanding the risks inherent to a team’s overall composition – as having a high junior to senior developer ratio, shortage of specific skills or knowledge (like external licensing or approval requirements with financial or medical apps, etc.).   
  • How to organize available personnel into teams for optimal efficiency while also planning for rapid growth. 
  • Having contingencies for turnover and team disruptions including sourcing options, costs, and provisions for onboarding delays. 

The list goes on, suffice that it is highly desirable to seek the means for fast, high-quality decisions. 

Using AI for Agile Project Management

So, here’s the good news – AI can help with all three scenarios, whether you have no PM, a certified or non-certified PM. How so?

  1. AI constantly analyzes all of your team’s data so you can make faster decisions.
  2. AI provides “Continuous Awareness” with real-time alerts for any issues that may threaten delivery.
  3. Users are provided with easy-to-understand summaries and Next Best Actions to proactively manage risks and improve developer, team, and project performance.

Using AI for Agile Project Management amplifies the effectiveness of all stakeholders. 

  • Certified PMPs spend less time on manual research and tracking so they can focus more on resolving issues, improving cost performance, and developing their team’s potential. 
  • Non-certified PMPs have an “advisor” constantly at their side, providing them with awareness of delivery or cost performance issues and guidance on how to improve them. 
  • Self-organizing teams benefit from improved cross-functionality through access to team data, advising each member on performance issues they can improve (and how), who they can assist or be best assisted by as relates to a programming language or performance metric, etc.
  • Everyone also benefits from an objective feedback loop about the performance of each developer, team, and project, and even their overall organization – which, overall, should improve over time. 

As we covered in Massive and Rapid Team Scaling, for cost performance, a PM optimally manages 3-4 projects. Some PMs are tasked with managing 9 or more projects, and they don’t sound too happy about it, but AI would help reduce their data burdens exponentially.

Adaptive vs. Predictive Project Management

Gitential inherently focuses on Agile which aims to be adaptive to ongoing changes, even when they come late during development. This does not preclude the use of predictive analytics. Agile software projects are subject to a range of parameters defined by software specifications and your development team. 

Specifications will eventually define what the software needs to do, what functions it needs to have, along with how it must perform. Decisions will be made as to which programming languages, technologies, and resources will power it. Ostensibly, these should match your developer’s skills and experience.

Any change in your team will most likely have an impact on delivery, sprint planning, code pairing and mentoring, and so forth. Some of this can be reasonably predicted. 

If a senior developer leaves mid-project, bottlenecks in the workflow can be expected, possibly an uptick in defects, those picking up the slack are likely to be less efficient as they’ll need to multi-task more, etc. 

Adding lots of new/junior developers? Expect efficiency to drop hard, code complexity and defect rates to increase, and a slew of other issues.

The question is though… How much will the impact be? And then, there’s the question of how quickly you can help the team improve performance. Benchmarking helps to set realistic goals. There’s no danger in making comparisons between teams and projects, only when setting expectations, goals, and being able to factor them into release planning.

Will Managers and Stakeholders Need Training to Use AI?

A five-minute orientation should be sufficient for even non-technical users to get a grasp on how Gitential’s AI works and begin getting valuable insights from using it. No specialized training is required. It functionally requires only:

  1. Making Google-like text queries
  2. Reviewing the data and recommended Next Best Actions
  3. Deciding whether to implement the Next Best Actions

AI serves only as an advisor. Real people remain the decision-makers for having real-world awareness that AI doesn’t. You may have a bottleneck in your work processes that could be solved by hiring another senior or mid-level developer. A real person will need to approve that.

Presuming that recommendation is not followed, AI can provide you with the “next best” Next Best Actions for your specific scenario. The advantage of AI is its ability to examine your entire team’s composition to determine which option is likely to have the greatest impact with the least effort.

Further recommendations to minimize bottlenecks could be to adjust your team size or composition, reduce story point size/complexity, allocate more time for code reviews, and focus on improving test coverage and quality, among a range of other possibilities. What works for Team A might not be viable or it may already be in effect for Team B or C.

The core issue though is that while AI presents no need for specialized training, it does warrant getting into a “continuous improvement” mindset. This shifts away from needing to know how things work to actively questioning how things work and how they can work better. Many, maybe even most, organizations are well on their way to doing this.

Implementation of AI Recommendations

The real impact of AI comes in sharing insights with all of your project’s stakeholders. Less time researching the data allows for more time interacting with and sharing the data with your team members. 

There are ample opportunities to share valuable data when using AI for Agile Project Management: 

  1. Standup meeting “Tips of the Day”
  2. Sprint planning
  3. Peer Code and PR Code Reviews
  4. Mentoring and walkthroughs
  5. Resource library examples
  6. One-on-One Meetings
  7. Objective and Key Results (OKR) Meetings
  8. Internal workshops and training programs

It’s recommended for those in management and leadership positions to spend an hour on one-on-one meetings every 1-2 weeks with each of their direct reports. As it takes two to make a one-on-one meeting, that’s ~100 hours a year per team member. 

And then… well.. there are plenty of other meetings to attend. 

And then… there’s the question of how much time is spent in preparation for each meeting. 

Manually digging through analytics to find useful insights to help each developer can be time-consuming. AI gives you the ability to do that on the fly, to generate insights “on-demand”, plus automatically track and measure the ROI of improvements.

Article posted: May 04, 2022

AI-Powered Chatbots in Customer Service and Engagement

Using AI for customer service in your company is a definite method to save time and money. If you’re like most business owners, you’re constantly searching for fresh, creative ways to improve your enterprise. We’re here to inform you that improving AI customer service is a simple and rapid win.

Read More »

January 2023 Release Notes

Here’s what’s new in our January 2023 Release Notes:

* Tables Columns Sorting Improved
* Reconcile Commits Count Between KPI Card and the Table
* Efficiency Tab Improvements and Efficiency KPI Cards Align

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