Agile Project Management in the Age of AI and Automation: Driving Innovation and Efficiency

Agile project management is a methodology that revolves around the very mindset of continuous adaptation and changing the project's stages based on the current needs. The word agile emphasises the practice of cycling through the practices of the project so as to make some circles of sub-successes that collectively take the project from start to the final goals.

Therefore, in agile project management (or simply agile mindset), achievements are not delayed until the very end of the project, when there are final delivered products or services, but the achievements are distributed locally or partially with respect to different stages.

What is Agile Management?

Agile management has its roots back in software management in the early 2000s, when agile software development was introduced by a group of software developers, called 'The Agile Alliance.' The first documentation, or guidelines if you like, was the document published by these companies under the name Manifesto for Agile Software Development. New practices, including Scrum, the dynamic system development method, adaptive software development, and extreme programming, influenced the new methodology.

Agile project management was introduced as the opposite principle of waterfall project management, a heavyweight method with so many regulations, plans, and divisions in the management processes and positions. The main aim of introducing agile project management was to start as soon as possible, let the new members join quickly, adapt to new needs without changing the whole mindset of the project management, and not postpone the taste of success to the end of the project.

You can also think of agile project management as the principle that takes its infrastructure from lean manufacturing and agile manufacturing. Agile manufacturing is an attempt to adapt production quickly to the market demand and trend; Lean manufacturing is an attempt to reduce waste in favour of increasing productivity. Therefore, agile management or agile project management in today’s management world is a combination of these two principles.

Agile Project Management and Uncertainty

You can look at the agile project management methodology from the non-deterministic nature of the activities, behaviours, and unknowns that directly or indirectly influence the result and level of success for any project.

Existing uncertainty weighs down all the defined tasks, weights that can slow down the processes or even make them unachievable. Agile project management acts like leverage that reduces the pressure caused by uncertainty and drives the project forward by defining the tasks (to-dos) and team members’ roles to be more adaptive to change in the fastest possible way.

How is it done? Simply by applying an iterative approach, where the complex projects are broken down into smaller ones with manageable increments. These small projects are easier to assess and are redesigned based on the feedback and results. Therefore, not only is the uncertainty managed (what is also called risk management), but the continuous achievement culture replaces the traditional project management methodologies that focus on success just at the end of the project.

Agile project management uses tools that are easier to master, as the whole project is divided into sub-projects with milestones and achievements. Therefore, newcomers to the teams find it easier to understand the principles and aims within the project and they automatically discover and join the agile mindset and feedback nature of the tasks.

Artificial Intelligence in Agile Project Management

Like the era of computers and following tech projects evolved around the Internet, now we are in the era of AI, and almost everything in technology tries to have a taste of it. In the concept of project management, artificial intelligence acts like a pusher that eventually takes all the methodologies into a new frame of doing.

By pusher, we mean a design that not just captures the demand but defines new demands and trends so that the other principles, services or products, and methodologies need to follow. Agile project management, by nature, is adaptable to any upcoming changes, and now the change is AI and the way it is used in all processes and practices.

AI is adaptable, as it learns and redesigns itself by taking more up-to-date data and redefining its algorithms accordingly. Therefore, it is a very applicable tool for agile project management; a tool that not only leads a process to be more adaptive but can also “understand” and define new trends, needs, and solutions.

Why do we need AI in project management?

The short answer is that it can help a lot with automatic risk management. AI can be structured as an analyser that takes the bulk of data and produces actionable insights. It can even take advantage of online data and remove the need for supervision steps in its own algorithm construction.

In project management, time is the driver of all success in tasks and the most important parameter when assessing progress. AI is the helper that buys you time by taking on some parts of tasks, speeding up the processes, and reducing errors.

How can AI be used in agile project management?

Notice that AI is a very general concept; it is a combination of mathematical algorithms used for analysing, predicting, understanding, and applying functions to data, plus the power of software engineering and the fast-evolving hardware to speed up the AI procedures.

The first application of AI in project management is like a consultant who has more information than you and can take advantage of specific data, combine it with already accepted facts, and advise you on the given tasks. AI is a great listener, and the most important part of using it is learning how to communicate with it efficiently.

You should always remember the first principle in using AI tools: if you rush AI to a conclusion, it will provide a brief and not even a beneficial answer. On the contrary, if you make a step-by-step communication by digesting data and insights into the algorithm, you will get much more beneficial and applicable answers. This is where we get to the point of defining AI prompt engineering.

What is prompt engineering?

• Simply put, prompt engineering, or AI prompt engineering, is the art of asking the right questions to the right AI-powered tool. By crafting effective prompts (conversations in the box), users can get more out of AI tools, rather than just asking questions on the fly and confusing the algorithm with irrelevant information.
• AI can be confused, as it is a simulation of the human brain; it takes data as a bulk of inputs with correct or approved output and uses them to create logic so as to be able to deal with requests that have some sort of connection with the given data. Therefore, if the user, here the project manager, misleads the AI tool or just emphasises too much the parameters that are not important to the basic request, the result will not be satisfactory at all.
• Therefore, the first step in using AI in project management is learning prompt engineering. The next step would be finding the AI tool that is designed (trained by data) for the area that project management is interested in. More specifically, you get a better result for the task of data analysis if you ask the relevant question to an AI-powered data analysis platform than to an AI-powered content writing platform.

In advanced applications, prompt engineering may also involve techniques such as:

• Few-shot learning: Provide the model with a few examples within the prompt to teach it how to respond to similar inputs.
• Chain-of-thought prompting: Encouraging the AI to break down its reasoning step-by-step to improve logical accuracy.

Instruction tuning: Designing structured prompts that mimic training objectives for enhanced comprehension by the model.

How AI and Automation are Transforming Agile Project Management

The very final aim of AI would be automating the tasks that can be handled better with machines. These tools focus on the computer power of following step-by-step structures with way lower possible errors than human agents. The following are the potential uses of AI in agile project management:

• AI-Driven Decision-Making in Agile Projects: Advanced data analysis and predictive forecasting are the most valuable gifts offered by AI-powered tools to agile project management processes. During each sprint planning, these AI tools analyse the historical data to identify the trends, suggesting optimal resource allocation, timelines, potential risks, and how to deal with them.
• Automation for Repetitive Tasks in Agile Workflows: Agile is about repetitive tasks, and AI can perfectly participate in this cycle of tasks. Tasks like testing, report generation, and backlog management can be handled efficiently by automated systems. This AI involvement reduces human errors, accelerates delivery cycles, and leaves more time for innovative actions by team members.
• Enhanced Collaboration with AI-powered Tools: AI-powered tools like chatbots, virtual assistants, and communication platforms increase the level and quality of communications in agile environments. These tools facilitate instant updates, as the most needed task in agile project management, automate the status tracking, and provide some real-time insights that ensure all team members and stakeholders stay aligned.

AI Tools for Agile Project Management

Besides ChatGPT, which is a general tool for all sorts of AI applications, there are AI tools that are specially designed for project management. The IPM article 'The 6 Best AI Project Management Tools to Help You Succeed' describes these AI-powered agile project management tools and their benefits.