Over the last couple of years, AI has captured the attention of the tech world, with new advancements seemingly erupting on a weekly basis. As AI has matured and gone from research topic to real-world product, there’s been a buzz about the potential for AI to disrupt the world of work, from making humans 10x more capable to robots stealing all of our jobs. No matter which way you think AI will take it, it is undoubtedly a powerful technology that has the capability to transform our work lives even starting now. One of the areas ripe for improvement with the availability of AI is Project Management.

Project management stands on the brink of a transformative era, heralded by the rise of Generative AI (GenAI). GenAI has the potential to automate many of the small, nuisance-like tasks of moving cards on a board and writing up summaries and unlock the potential for Project managers to focus on process and output. This blog delves into the essence of GenAI, particularly focusing on Large Language Models (LLMs), and explores their pivotal role in redefining project management practices—both today and in the future.

What is Generative AI? What is an LLM?

Generative AI is transforming the landscape of digital creation, offering a new paradigm where machines not only understand, but also generate human-like content. At the heart of this revolution lies Large Language Models (LLMs), sophisticated AI systems trained on vast datasets to comprehend and produce text that mimics human language and reasoning. Unlike traditional AI, which responds based on predefined patterns, LLMs like GPT (OpenAI), Bard (Google), and others, leverage deep learning to predict and generate coherent, contextually relevant text.

Imagine Large Language Models (LLMs) as vast libraries in the minds of AI, where each book represents a piece of knowledge from the training data it's fed. These models use neural networks, akin to a simplified, computer-based version of our brain's networks, to understand and generate language. As they're trained on diverse and extensive datasets, they learn patterns, nuances, and the intricacies of human language. This training allows them to predict and produce text that feels surprisingly human-like, making them invaluable tools across various applications, even for those without a technical background.

LLMs, while powerful, operate as "black box" models, meaning the exact way they come to a particular output isn't transparent. This opacity arises from their complex neural networks, which, much like the intricate workings of the human brain, make it challenging to trace the precise path from input to output. This means that we have to train GenAI how to think like us. Because we can’t go and prescribe specific types of logic or understand how it generated particular text, the best way to work with AI today is to try and teach it like you would a human. Give it lots of examples and feedback about whether it’s getting closer or further away from the right answer. This opacity makes setting up AI more challenging than many products, but also enables its limitless potential for creation and generation.

This capability opens a new realm of possibilities, from drafting articles and coding to creating art and music, pushing the boundaries of AI's creative potential. For project managers, understanding Generative AI and LLMs is crucial, not just for leveraging these tools in streamlining workflows and enhancing productivity but also for navigating the ethical and practical challenges they pose. As we delve deeper into the capabilities and applications of these models, it becomes evident that Generative AI is not merely a tool but a transformative force in project management and beyond, redefining what machines can achieve and how we interact with them.

Major Generative AI Models

In the realm of Generative AI, several major models stand out for their innovative capabilities and contributions to the field. Among these, GPT by OpenAI, Bard by Google, Claude by Anthropic, and Llama by Meta are particularly noteworthy. Each of these models is designed with unique focuses and strengths, propelling forward the boundaries of what AI can achieve in terms of understanding and generating human-like text.

GPT, developed by OpenAI, has gained significant attention for its broad applicability, from creative writing to solving complex logical queries. Its latest iteration, GPT-4, has made headlines for its even more refined understanding and generation of human-like text, showcasing OpenAI's commitment to pushing the boundaries of AI technology.

Google's Bard, leveraging the vast data from the internet, is designed to simplify complex topics and engage users in natural, informative dialogues. It's part of Google's broader AI strategy, emphasizing the integration of AI into everyday information access.

Anthropic's Claude is built with a focus on ethical AI use, reflecting the company's dedication to creating AI that's safe and aligned with human values. This commitment is particularly crucial in an era where AI's impact on society is under close scrutiny.

Llama, Meta's contribution to the GenAI field, stands out for its open-source approach, allowing broader access and innovation within the AI community. This model underscores Meta's strategy to democratize AI technology, making it more available for research and development across industries.

Understanding these models and their distinct capabilities allows project managers to choose the right tool for their specific needs, whether it's enhancing team communication, automating content creation, or streamlining decision-making processes. All these models have pros and cons, with technical capabilities and UI consideration being one of the largest for product managers. ChatGPT and Bard are the most accessible models (today) because of their free and available UI. However, some Project Managers might consider using Llama or Claude if they have access to technical resources and a specific use case that fits those models better. Understanding these LLMs lays the groundwork for exploring the major GenAI tools that are shaping project management today.

Creating Value with GenAI

Generative AI is revolutionizing project management with its ability to automate and optimize a range of tasks, enhancing efficiency and freeing up project managers to focus on higher-level strategic decisions. Here’s a few ways we’ve seen Project Managers leverage AI to supercharge their productivity:

Highly Structured Content Creation

GenAI excels in generating structured content such as reports, proposals, and forms. For Project Managers that have clearly defined document structures and reporting requirements, AI can help fleshing out documents. Have a PRD but need to develop risks and mitigation plans? AI can provide great starting content and help kickstart the process. AI can go a long way towards generating content that might feel tedious.

Let’s say that your company is engaging with several similar RFP processes that all have their own format. After filling out just one of them, PMs can train AI to understand the basics of the company’s proposals and re-format the content to fit the others. AI does a great job at generalizing and repurposing existing text to fit a new, specific prompt.

Content creation can also be useful internally for process management. Integrating AI generated content into project management tooling can be a great way to cut down on overhead. Let’s say your company has a PRD that needs to be turned into a series of cards corresponding to project steps. Project managers can use tools like ChatGPT to turn their PRDs into process steps and move them into their tooling. Some modern project management tools like Dart incorporate AI straight into the UI, allowing for seamless generation of cards straight to the source.

Content Summarization

In the fast-paced world of project management, efficiency is key. Generative AI (GenAI) is revolutionizing how project managers handle extensive documentation, making the summarization of complex materials more accessible than ever.

Take, for example, the challenge of sifting through voluminous project reports. A project manager can leverage GenAI to condense a hefty compliance document into a digestible summary, pinpointing critical areas and action items. This not only saves time but ensures focus on essential compliance issues.

Alternatively, GenAI can also be used to keep everyone on the same page. It could take everyone’s notes from a series of meetings and distill key points and directives from hours of meeting content. This approach guarantees that every team member is informed and aligned with the project's goals, without the need to wade through hours of transcripts or videos.

The ability to take a large volume of information and concentrate it into the critical points that are relevant to team members enables a more engaged, directed project management process that keeps everyone on the same page without requiring extensive document review.

Automating Repetitive Tasks

‍ GenAI also plays a pivotal role in automating repetitive administrative tasks, from scheduling to standard email responses. This automation not only increases operational efficiency but also allows project teams to allocate more time to strategic tasks and creative problem-solving, driving projects forward more effectively. Weekly updates can be automated with AI-summarized meeting notes and analysis of project movement.

Process Review

‍Project Managers can use AI to review projects after the fact, getting summaries about deadline compliance, scope creep, and other project changes mid-flight. By getting automated retrospective data, PMs have a great, objective resource to start reviewing their own work and provide feedback from their teams.

Implementing AI in Project Management

Incorporating GenAI into project management can significantly enhance efficiency and effectiveness. But you don’t have to fully transform your project management process to get started, here’s a few small ways to start using AI in your workflow:

Automate Small Writing Tasks

GenAI can be employed to handle routine writing tasks such as drafting emails, preparing meeting agendas, or creating project updates. This automation saves time and ensures consistency in communication.

Try Out AI-Enabled Task Management Tools Tools like Dart, powered by AI, can assist in building and managing process flows. These tools can intelligently assign tasks, predict potential bottlenecks, and suggest optimal workflows, making project planning more dynamic and responsive. Try out a new tool by importing your current workflow and playing around with the AI capabilities. For example, you can use Dart AI’s intelligent planning feature to take a complicated project, break it up, and plot it out on a roadmap, helping you to determine a realistic time frame to complete the project in.

Weekly Check-In Summaries

Utilize GenAI to generate weekly summaries based on meeting notes. This can provide clear and concise overviews of project progress, key decisions made, and next steps, ensuring everyone on the team is on the same page. Ask everyone on the team to combine their meeting notes and let AI do the summary work for you.

PRD Management

‍GenAI can break down complex Project Requirement Documents (PRDs) into manageable tasks and automatically assign them to appropriate team members based on their expertise and workload. This not only accelerates the project initiation process but also enhances the allocation of resources and task tracking. Tell AI about the different team members and their functions, and then give it a real document to see how it allocates tasks. It probably won’t be perfect the first time, but it can be valuable to get a first draft done quickly and adjust from there.

The Future of AI powered Project Management

The integration of Generative AI (GenAI) in project management heralds a shift towards strategic process optimization. This change allows project managers to elevate their role from overseeing task execution to focusing on innovation and strategic planning. By automating routine tasks, GenAI frees up time for managers and their teams to concentrate on the core objectives of their projects and devise creative solutions to challenges. Moreover, GenAI's ability to analyze project data for process improvement enables a more dynamic and responsive project management approach. This adaptability ensures that projects can be completed more efficiently and effectively, even as conditions change. Project Managers should be spending their time focused on key metrics and process improvements. AI gives them tools to spend less time on task management and unlock the focus on what really matters.