Can AI be used in project management?

In the past year, there has been a lot of talk about artificial intelligence and how it changes the world. There’s been much hype that AI is taking over everything. We’ll take a look at how AI fits into project management software and tools. We’ll explore what it is and what it can do. Is it really useful for project management or just a gimmick and crash crab feature that doesn’t provide any real value.

What is AI?

AI, or Artificial Intelligence, is a broad term that refers to the intelligence exhibited by machines. In simpler terms, it's the ability of machines to perform tasks that typically require human intelligence. This can include things like:

• Learning and problem-solving: AI systems can analyze data, identify patterns, and use that knowledge to make decisions and solve problems.
• Reasoning and perception: Some AI systems can understand and respond to the world around them, like interpreting visual information or understanding human language.
• Adaptability and creativity: AI can learn and adapt to new situations, and even generate creative text formats or images.

There are different approaches to achieving AI, but a common thread is the use of algorithms and data. AI systems are often trained on vast amounts of data, which allows them to learn and improve their performance over time.

What types of AI are there?

There are many ways to classify AI, but here's a breakdown based on capability and function:

By Capability:

• Artificial Narrow Intelligence (ANI) or Narrow AI: This is what we see most of today. It’s designed to do one thing really well but lacks general intelligence. Examples are chess playing programs, spam filters and facial recognition software.
• Artificial General Intelligence (AGI) or General AI: This is the hypothetical future AI that has human-like intelligence, can learn and apply knowledge across domains. AGI is still theoretical.
• Artificial Superintelligence (ASI) or Super AI: This is even more advanced AI that surpasses human intelligence in all aspects. Like AGI, ASI is still theoretical.

By Function:

• Machine Learning (ML): ML is a core technology for many AI applications. It allows machines to learn from data without being explicitly programmed.
• Deep Learning: A subset of ML that uses artificial neural networks for complex tasks like image and speech recognition.
• Natural Language Processing (NLP): This is the field that enables computers to understand and process human language. It’s used in chatbots, machine translation and sentiment analysis.
• Computer Vision:Gives machines the ability to interpret visual data from the real world. It’s used in self-driving cars, medical imaging analysis and robotics.
• Reinforcement Learning: Trains AI through trial and error in a simulated environment. It’s used in game playing and robot control..
• Generative AI: This type of AI can create new data, like text, images, or code.
• Expert Systems: These are knowledge-based systems that mimic human expertise in a specific domain. They rely on encoded knowledge and rules to solve problems.

It's important to remember that these categories are not always mutually exclusive. For instance, deep learning can be used for generative AI tasks, and machine learning is a foundation for many other AI functionalities. The type of AI chosen depends on the specific problem it's designed to address.

What type of AI is used in project management software

Machine learning (ML) is used behind many project management AI features. More specifically, large language models (LLM) with generative AI, which are a subset of machine learning. Generative AI analyzes massive datasets of project information, including historical data, task details, communication records, and more.

What Can Generative AI do:

• Content Creation: Generate different types of text formats like blog posts, marketing copy, articles, scripts, poems, musical pieces (lyrics), email drafts, letters, etc.
• Content Editing: Rephrase sentences, improve clarity and flow, suggest alternative phrasings and check for grammar errors.
• Translation: Translate languages accurately while preserving the meaning and style of the original text.
• Image Creation: Generate unique and creative images based on text descriptions. This can be helpful for creating illustrations, concept art or even photorealistic visuals (depending on the tool).
• Code Completion: Suggest lines of code, entire functions and even boilerplate code to help programmers in their development process.
• Music Generation: Compose original music pieces in different styles based on user prompts or starting compositions.
• Customer Support: Assist contact centers by generating responses to common customer inquiries, providing agents with real-time information, and helping to streamline customer service processes.

Popular Generative AI Tools:

• OpenAI GPT: One of the most popular tools, known for generating human quality text, translating languages, writing different types of content and answering questions in an informative way.
• Google Gemini: A powerful generative AI from Google AI, can do similar things to GPT, text generation, translation and question answering.
• Microsoft Copilot:A generative AI tool from Microsoft powering its Bing search engine. Bing AI provides comprehensive and relevant answers to user search queries, often combining factual information with AI generated explanations.
• Midjourney: An AI art studio that allows users to generate amazing and creative images based on text descriptions. It’s known for producing dreamlike and artistic visuals.
• Runway: A creative video editing platform that allows users to incorporate AI powered tools for generating video content, adding special effects and enhancing visuals.
• GitHub Copilot: An AI code completion tool that suggests lines of code, entire functions and even boilerplate code to help programmers in their development process.

Right now generative AI based solutions are used in project management tools. As mentioned earlier, generative AI is a subset of machine learning, so other AI solutions are possible.

The most common way to use AI in project management software is to integrate it with one of the publicly available AI tools and customize it for your needs. OpenAI and Google Gemini are the most popular ones.

Looking more closely at the project management software AI features then we sometimes discover that it’s not really an AI but a simple workflow automation solution. AI and automation are not the same thing but are usually used together.

What's the difference between AI and automation in project management context

In the context of project management, both AI and project automation can be beneficial, but they serve different purposes:

In essence, automation is like a light switch - it can turn tasks on or off (complete or incomplete), but it can't adjust itself based on the situation. AI, on the other hand, is more like a dimmer switch. It can not only complete tasks but also analyze data and suggest intelligent adjustments based on the specific context of a project.

The key difference is that AI can learn and improve over time, offering more complex assistance based on data analysis, while automation simply follows predefined rules and procedures without the ability to learn or adapt.

Very good examples of automation tools are Zapier, Make, Workato, and others. These tools can be used to automate all project management tasks. You can connect different tools, create triggers and actions, and more.

Let’s take a look at some real world examples of AI implementation in project management software.

Notion AI review

Notion AI was launched in a private alpha phase in November 2022. After spending a few months gathering user feedback, Notion made Notion AI generally available to its tens of millions of users in February 2023.

The exact details of Notion AI's underlying technology are not officially confirmed by Notion. However, based on several sources, there's strong evidence suggesting Notion AI likely uses OpenAI's GPT

Notion AI features

Notion AI acts as your writing and project management companion within the Notion workspace. The AI works by allowing users to highlight text and either select "Ask AI" or type "/AI" to receive AI-generated suggestions. It offers features that can help you throughout your workflow:

• Content Creation and Editing: Brainstorm ideas, draft content (like emails, blog posts), and improve your writing with suggestions for grammar, style, and clarity.
• Summarization and Information Extraction: Quickly grasp key points from lengthy documents or research proposals with Notion AI's summarization capabilities.
• Task Management and Automation: Notion AI can help organize your tasks by suggesting content formatting or prioritizing based on deadlines.

Notion AI limitations

Notion AI, while offering a helpful AI assistant within your Notion workspace, has some limitations and potential drawbacks to consider:

• Accuracy and Reliability: Since Notion AI likely relies on OpenAI's GPT, it can inherit its shortcomings.These include potential for factual errors, biases reflecting the training data, and occasional nonsensical outputs.
• Limited Reasoning and Understanding: Notion AI excels at identifying patterns in text but struggles with deeper understanding or complex reasoning. Don't expect it to solve problems requiring critical thinking or applying knowledge to new situations.
• Over-reliance on User Input: Notion AI functions best with clear prompts and guidance. Without proper direction,it might generate irrelevant or inaccurate outputs.
• Privacy Concerns: While Notion claims user data is anonymized for AI training, some users might have concerns about their data being used to improve the AI model.
• Integration and Workflow Issues: Notion AI is still under development, and its integration with other Notion features might not be seamless. Additionally, it can introduce a new element to your workflow that requires adjustment.

Notion AI user reviews

Let’s see what real users have to say about Notion AI.

"Almost entirely useless. Could be decent if you’re a writer or need simple things summarized like meeting notes or something. Completely useless for anything involving databases or other complex uses. If I need AI help, I use Microsoft’s Copilot or I use perplexity AI."

reddit.com

"Notion QA / search (which is an extension of AI under the hood ) is insanely good, it has materially changed the utility of search functionality in the product. The regular AI writer is not useful, I’d rather just use chatgpt"

reddit.com

"All it does for me is add annoy when I accidentally hit key for it on the keyboard"

reddit.com

"Wish they didn’t jump on the hype train, there’s a lot better AI’s out there for AI things. Their app has suffered in the last few months because of all this bloated gimmicky stuff. Just give us the features that can make using notion better, let us figure out how to do our stuff though. Even notion calendar was super disappointing."

reddit.com

Based on the reviews it looks like most users have tried it but don’t see any real value in it. The pricing is too steep and functionality limited. Notion AI doesn’t provide any real value over dedicated generative AI tools like ChatGPT, Gemini, Copilot and others.

Because Notion AI uses ChatGPT for their AI then it has the same limitations - it can be incorrect, hallucinate, it can’t do calculations or solve numeric problems.

ClickUp AI review

ClickUp AI, branded as ClickUp Brain , for project management was officially launched on May 4th, 2023. It’s a transformative AI solution integrated into the ClickUp platform, designed to enhance work across various roles.

There's no official confirmation from ClickUp regarding the specific technology powering ClickUp AI. They might use a mix of cloud based services like ChatGPT, Google, Amazon and their own proprietary AI model.

ClickUp Brain features

ClickUp AI supercharges project management with AI features like:

• AI-powered writing assistant: Draft, edit, and improve project documents.
• Actionable insights: Summarize documents, track progress, and get suggestions for better project execution.
• Communication & collaboration: Craft smart replies, analyze team sentiment, and automate tasks.
• Role-based functionalities: Tailored suggestions for project managers, team members, and others.

ClickUp AI is a powerful tool to streamline workflows, but human expertise remains crucial.

ClickUp Brain limitations

ClickUp AI, despite its innovative approach to project management, has some limitations to consider:

• New Technology: As a relatively new technology (launched May 4th, 2023), ClickUp AI is still under development. This means it might encounter issues like bugs or limitations in functionality compared to more mature AI solutions.
• Accuracy and Reliability: Like many AI systems, ClickUp AI's outputs might not always be correct.. There's a potential for errors, factual inconsistencies, or biases reflecting the training data. Human oversight and review remain crucial.
• Limited Reasoning and Understanding: ClickUp AI excels at identifying patterns and automating repetitive tasks. However, it might struggle with complex problem-solving or tasks requiring deep understanding of project context.
• Over-reliance on User Input: ClickUp AI performs best with clear instructions and guidance. Vague prompts or unclear expectations can lead to irrelevant or inaccurate suggestions.
• Integration Challenges: As a new addition to the ClickUp platform, there might be occasional hiccups in integration with existing functionalities.
• Cost Limitations: ClickUp AI usage comes with limitations on the free plan, and paid plans are required for extensive use. This can be a barrier for some users.

ClickUp AI can automate tasks, suggest ideas, and streamline workflows. However, it's not a replacement for human expertise, critical thinking, and the ability to navigate complex situations.

ClickUp Brain user reviews

Let’s see what real users have to say about ClickUp AI.

"I am underwhelmed. Without exception, ChatGPT's responses were miles ahead of the ones Clickup's AI gave me. By orders of magnitude. Without sharing any concrete examples, if I had to describe it, I would say Clickup is way too generic and doesn't seem to fully get what I am trying to achieve. Zero responses of it actually helped me; 100% of ChatGPT's responses were spot on and absolutely nailed it - every single time."

reddit.com

"very low level compared to chatGPT. cashgrab"

reddit.com

"It does some things good, like summarizing, creating action items etc. - But if you want to create some deeper knowledge content, it's not very good. It doesn't really seem to understand context. Like when you ask ChatGPT multiple different questions in a row, it understands the context and responds correctly. Clickup AI doesn't."

reddit.com

"I can't image who would wanna use AI in clickup. Who would even benefit from that? You are using this software to organize people, you don't need any generated AI texts as far as I know. If you write task description, you have to write it precisely yourself, you don't need some hallucinating GPT api making chaos in it. Clickup has FOMO."

reddit.com

The user reviews on Reddit are mostly critical and negative. Users don’t see any value in the AI, it makes ClickUp slow. It’s just another extra feature in the long list of ClickUp features that users didn’t ask. It feels like ClickUp added the AI from fear of missing out on the AI hype.

ClickUp AI also uses ChatGPT for their AI and it has the same limitations and issues like other generative AI tools.

Is AI in project management gimmick or real deal?

AI in project management looks to have impressive capabilities but in real life has limitations. Here's a breakdown of what it generally can't do:

1. True Creativity and Understanding: While AI can create new text formats, tasks, to-dos, images, or code, it doesn't possess genuine creativity or understanding. It can't grasp the nuances of human language, emotions, or concepts. Think of it like creating a collage of existing elements, not truly original ideas.
2. Reasoning and Problem-solving: AI struggles with complex reasoning and problem-solving tasks. It can identify patterns in your projects, but it can't understand the underlying cause-and-effect relationships or apply that knowledge to solve new problems.
3. Independent Learning: AI requires vast amounts of data for training and can't independently learn new things outside their training data set. They improve through human-guided training and adjustments.
4. Counting or Numerical Reasoning: Counting often requires understanding context. For instance, if you ask a generative AI to "count the apples in a picture", it might struggle to differentiate apples from oranges or other round objects. It can't apply reasoning to distinguish between them.
5. Fact-Checking and Avoiding Bias: AI is susceptible to biases in its training data. The generated content may contain factual errors or perpetuate biases if not carefully monitored. Human oversight is crucial to ensure accuracy and fairness.

Here's an analogy: Imagine generative AI as a project manager. The AI can generate all sorts of tasks and to-dos, but it takes a human project manager to link them together to create a meaningful project.

Overall, AI can be a powerful tool for content creation and data analysis, but it's important to be aware of its limitations. It should be used alongside human expertise to achieve the best results.

Conclusion

AI has the potential to be used in project management tools, but its current applications are still limited.

When OpenAI first came out with their tool there was much hype about it but after trying it out people seem to understand its limitations. Is the hype over?

Overall, generative AI has the potential to be a valuable tool for project management, but it's still in its early stages. For now, human expertise remains essential for ensuring the accuracy, relevance, and effectiveness of AI-generated content in project management contexts.

Remember, AI in project management assists with tasks but doesn't replace human expertise. Project managers still play a crucial role in interpreting AI insights, making crucial decisions, and leading their teams.