3 Project Problems and How Generative AI Can Help (New Stuff!)

Introducing Generative AI and Visual Modularity for Projects

In this article, we’ll introduce three key project problems, solutions needed to solve those problems, and how generative AI and project modularization can help you with the solution. This article also introduces the new AI features of Pie that will tie with these solutions.

Let’s start with the big news — Pie has built in GPT-4 to bring generative AI to your projects for better planning, task generation, and detailed how-to knowledge helping team members execute tasks. We also enhanced our modularization of Pie to bring more Lego-like approaches for reusability throughout Pie's patented visual structure.

Why is this important?

With a staggering 99.5 percent of complex projects experiencing budget overruns, delays, and failing to deliver expected benefits, there's an urgent need for innovative solutions. In this post, we'll introduce key project failure problems and introduce solutions using generative AI and modularity.

In this post, we'll introduce key project failure problems and introduce solutions using generative AI and modularity. Start with the following summary video:

This article has three more videos on how Pie can revolutionize project management for consulting firms, new product development teams, and other groups who desire to improve efficiency and end-project result success.

Project Problems

In the book "How Big Things Get Done," by Bent Flyvbjerg and Dan Gardner, the authors researched over 16,000 projects from around the world and found something pretty shocking: almost all of them, 99.5 percent, ended up costing more, taking longer, and not delivering the business value that was promised. There are a few other research papers from other researchers, specifically on IT projects, that also show extreme failure rates. Whatever the numbers are, we can all agree they are way too high and this systemic problem needs to be solved.

In the IT project space, “fat-tailed” distribution is where far more extreme outcomes happen. As the authors describe in the book, "18% have cost overruns above 50%, and for them the average overrun is 447%." Alok Kejriwal reviewed this book on LinkedIn and wrote, "Another IT blowout happened to the legendary jeans maker Levi Strauss: Originally forecast to cost $5 million, the project forced the company to take a $200 million loss and show its CIO the door."

What Fails?

According to Professor Bent Flyvbjerg, there are a number of problems that cause high failure rates, such as slow thinking during planning, commitment fallacy, marginalizing experience, politics, uniqueness bias, and missing modularity for repeatability efficiencies.

So what? The ramifications of project failures extend beyond financial losses, which can be huge, encompassing damaged relationships and reputations. Budget overruns, missed deadlines, and failing to meet business value objectives not only strain organizational resources but also erode trust with stakeholders and end clients.

In the following sections, I will focus on three key problems and how we help mitigate them with the new Pie AI and extended visual modularity capabilities.

Problem 1) Thinking fast, acting slow

Companies rush through their planning phase neglecting to thoroughly analyze potential risks and contingencies. Bent Flyvbjerg calls this approach, “think fast, act slow.” By speeding through the planning, they later end up with the extra need of fixing issues, or worse, restarting. This kills the budget and timeline targets, and in many cases, lowers delivered business benefits.

Solution — Think slow, act fast

Taking your time and being thoughtful during the project planning phase will enhance the speed of the execution phases. A quote from Bent Flyvbjerg’s book is, “Slow isn’t good in itself…If the plan is narrowly focused, it may not reveal fundamental flaws and gaps in the plan, much less correct them…In contrast, good planning explores, imagines, analyzes, tests, and iterates. That takes time.” Part of taking that time would be reflecting on methods and tactics used in similar previous projects.

One practice is to contain your project processes and continue to improve them over time (as described in the modularity section below). Another is to get a leg up with planning knowledge by leveraging generative AI as your copilot to give you a quick draft of ideas. For example, AI could introduce planning and execution tasks with detailed descriptions that you may have missed previously. Even writing the prompts for AI is a good way to be thoughtful. It’s the time to ask questions.

The benefits of slow thinking during the planning phase would lead to less problems during the execution phase, and therefore keep the project running on time, on budget, and towards ends results that bring value.

The following video is on how Pie is helping think through the planning phase with using generative AI for creating a new project or recipe:

For more Pie information, check out the details on how to use Pie’s new AI for creating projects from scratch.

Problem 2) Missing knowledge

We all have a high demand for projects along with fewer available and experienced people due to movements to other areas or simply retiring. This means knowledge walks out the back door, while new inexperienced people come in the front door. When new people come on, how do we get them up to speed with good knowledge?

Solution — Keep the experienced, share knowledge, and use generative AI

The best approach is to keep the experienced people for future like-projects. Bent Flyvbjerg observed a key attribute of architect Frank Gehry’s success was his ability to keep the same team as he moved on to new projects. Pixar project managers successes included collaboration and sharing early drafts with peers. If you can’t keep them, then at least capture the knowledge of the experienced folks before they move on.

Pie’s foundation is around knowledge capturing, access, and sharing with its recipe structures and task how-to content. There are many times when defining a set of tasks or identifying how to execute a task is just not in the experience bucket. This is where using generative AI could help get ideas flowing. Pie now has generative AI to help build starter content for process box components and the how-to content for tasks. The benefits of better and more robust how-to content for team execution, the less likely you would run into issues that places the budget at risk.

The following video is on how Pie is helping with content creation with generative AI at the process box and task levels, and also generative AI at the task how-to description content level.

For more Pie information, check out the details on how to use Pie’s new AI for creating task lists under slices and process boxes.

Problem 3) Lack of reusability and lessons learned

Many companies look at projects as a whole endeavor, or as Bent Flyvbjerg describes it, “one huge thing”, which ends up as a big list of tasks to get done in order to deliver some final output. This mindset tends to miss the opportunity to leverage what was done last time, and it makes it more complex to keep track of what worked, what didn’t work, and new ideas. Since most projects are repeatable-type projects (either within the organization or within the industry), there’s an amazing failure of using components for reusability.

Solution — Establish modularity and turn projects into components

Visual modularity represents a paradigm shift in project management. The idea is to establish project planning and execution phases, sub-phases, and tasks as components that contain the how-to knowledge. These are smart structures with thoughtful content that can be plugged in, pulled out, or dragged around as you would with Lego blocks. It gives ultimate flexibility and reusability.

Unlike traditional linear approaches, visual modularity enables project managers, team members, and stakeholders to conceptualize complex projects as interconnected modules, each representing a distinct aspect of the project lifecycle.

Turning a one huge project into small components, such as multiple small projects, reduces complexities that hide things that turn into issues until it’s too late to act.

Using agile sections that correspond well with process flows, making it a hybrid project, is also helpful, such as integrate iterative methods that can be dropped in as needed at different parts of a complex project or program.

The modular model also works well for constant improvement with the capture and re-use of lessons learned. When lessons learned activities are well defined as objects, they can then be easily slotted into multiple parts of the project. This would keep them running in real time for an ongoing improvement that make future projects more successful.

In a Wall Street Journal book review by Marc Levinson, he writes, “The best way to bring the costs of large projects under control, Mr. Flyvbjerg says, is to break them into small steps that can be repeated.”

Pie has been built from the ground up to be a visual modular system. This includes its visual project and subproject layer components, project phase models (pie slices), process boxes, and hybrid agile boards that can run as a project phase component. The most important part of modularity in Pie is its recipe library where your can define your framework or blueprint for future projects, using them on-demand as needed.

Recently, Pie added a new push and pull copy component feature set for process box and slice components where a project manager can quickly copy an object of tasks from any project or recipe within the organization for reusability.

The modularity benefits include making complex projects easier to understand and execute and leveraging past successes with constant improvement.

The following video is on how Pie is helping project success with visual modularity:

For more Pie information, check out the details on how to use Pie's modularity functions for process boxes, agile boards, and task lists.

Conclusion

In summary, Pie’s integration of Generative AI and Visual Modularity represents a significant advancement in project management technology, offering organizations a comprehensive solution to navigate the complexities of modern projects. Organizations can now optimize project planning and process content creation with generative AI to help mitigate risks and enhance knowledge for all team members. With Pie's innovative approach with visual modularization, complex projects can be turned into easier to consume components and project managers can confidently lead their teams towards success, delivering projects on time, within budget, and with maximum business impact.

Again, the so what?

Using new technologies such as generative AI and clever modularity approaches for content creation and knowledge sharing is not the end all, but rather one of many factors to help improve success. As Bent Flyvbjerg's and Dan Gardner's book describes, projects are complex and there's a myriad of considerations why projects fail. But, we all know that if we do nothing to make change, we'll still be at a 99.5% failure rate, losing $trillions, killing client and customer relationships, and hurting companies' and individuals' reputations.

Let's talk about making change.

Written by: Paul Dandurand, Pie Founder
Top banner photo on left by: Andrey Zvyagintsev
Top banner AI image on right created with Midjourney