What is Behind PMOtto.ai Your AI-Powered Assistant for All Things Project Management

Summary

PMOtto, a generative AI-powered project management tool, is introduced in its beta version. Developed with machine learning, it integrates a large language model (GPT-4) with extensive project management knowledge, aiming to serve as a virtual assistant for project managers. The tool offers a three-layered approach, with the top layer being customizable for corporate use, ensuring security and integration with internal systems. The platform is designed to save time on learning, updating, and reporting, and encourages user feedback for continuous improvement.

Takeaways

• 🚀 PMOtto is a beta version project management tool powered by generative AI, aiming to assist project managers in delivering better projects.
• 🤖 The core of PMOtto is based on the GPT-4 model from OpenAI, which serves as the foundation for its natural language processing capabilities.
• 📚 A second layer has been built on top of GPT-4, incorporating knowledge from Antonio Nieto Rodriguez, a leading expert in project management, to enhance the tool's project management insights.
• 🔍 PMOtto is designed to go beyond simple searches, training its language model with project management documents to provide integrated, knowledgeable responses.
• 📈 The tool's database is continuously updated with new information, aiming to become a comprehensive learning resource for project management.
• 🤝 PMOtto seeks collaboration with associations, authors, and content creators to enrich its project management knowledge base.
• 🏢 A third layer is being developed for corporate use, allowing companies to integrate their specific project management methodologies and internal data for a tailored experience.
• 🔒 Security is a priority in the corporate layer, ensuring that only authorized individuals have access to sensitive company information.
• 🎯 The interface is user-friendly, allowing users to interact with PMOtto via natural language and receive practical project management advice and tools, such as work breakdown structures and risk analyses.
• 🔧 PMOtto is currently in beta and不断完善, with plans to offer recommendations for videos, documents, and even internal company templates in the future.
• 📝 Users are encouraged to provide feedback to help improve PMOtto's accuracy and usefulness, acknowledging that AI-generated information may require human verification and adjustment.

Q & A

What is PMOtto and what does it aim to achieve?

PMOtto is a virtual assistant for project management, powered by generative AI and designed to help project managers deliver better projects. It combines the knowledge of project management with the capabilities of large language models like GPT-4, aiming to provide a powerful source of project management information.

When was PMOtto created and by whom?

PMOtto was created in 2017 by a group of friends, including the speaker in the transcript, with the goal of utilizing machine learning to assist project managers.

What is the role of GPT-4 in PMOtto?

GPT-4 serves as the core of natural language for PMOtto. It is the foundation upon which the project management layer is built, enabling users to interact with PMOtto using natural language queries and commands.

What is the significance of Antonio Nieto Rodriguez's contribution to PMOtto?

Antonio Nieto Rodriguez, a leading figure in project management and author of 'The Harvard Business Review Project Management Handbook', provides the knowledge base for PMOtto's project management layer. His insights, articles, presentations, and other content are used to train the AI, ensuring that PMOtto is well-informed on project management principles and practices.

How does PMOtto differ from other large language models?

PMOtto differentiates itself by having a second layer, the Project Management layer, which is built on top of the foundational language model. This layer integrates project management knowledge, making PMOtto a specialized tool for project management tasks and queries.

What is the purpose of the third layer in PMOtto's structure?

The third layer is designed for corporate use, allowing companies to add company-specific information and methodologies. This layer enables users to access and combine internal project data with the AI's knowledge, providing tailored insights and recommendations.

How does PMOtto ensure security and privacy for corporate users?

PMOtto uses robust security measures to protect the corporate layer. Companies can define access permissions and control who can view or upload documents. The system is designed so that even the developers do not have access to this layer, ensuring that sensitive company data remains secure.

What are the benefits of using PMOtto for project management?

PMOtto offers significant time savings by automating tasks such as generating work breakdown structures, identifying risks, and analyzing stakeholders. It also reduces the need for manual reporting and allows for quick access to project management knowledge and company-specific data.

How does PMOtto's learning database continue to grow and improve?

PMOtto's database is updated daily with new information, agreements, and content from various sources. The aim is to include as much relevant project management content as possible, making the tool increasingly comprehensive and effective over time.

What is the current status of PMOtto?

At the time of the transcript, PMOtto is in its beta version, with the second layer being released for testing. The tool is being actively improved, with the aim of expanding its capabilities and refining its performance based on user feedback.

What advice does the speaker give to users of PMOtto and similar AI tools?

The speaker advises users to be mindful of the limitations of AI, such as the potential for inaccuracies or 'hallucinations'. Users should not rely entirely on the tool but use it as a support. They should also provide feedback to help improve the system and ensure it evolves to meet their needs more effectively.

Outlines

00:00

🚀 Introduction to PMOtto and its AI Framework

The first paragraph introduces PMOtto, a project management tool leveraging generative AI, specifically GPT-4, to enhance project management capabilities. Developed since 2017, it combines machine learning and extensive project management knowledge, notably from Antonio Nieto Rodriguez, to offer a unique product aimed at improving project delivery. PMOtto is distinguished from other AI models like GPT-4 or Bard by integrating a project management layer on top of the AI core, utilizing a vast array of resources to train its model, ensuring a tailored and specialized assistant for project management.

05:03

🛠 Features and Capabilities of PMOtto

The second paragraph delves into the functionalities of PMOtto, describing its multi-layered architecture. The core layer is powered by AI, providing natural language processing, while the second layer contains project management content, updated daily from various sources. A unique corporate layer, customizable and secure, allows companies to integrate their specific project information, ensuring a tailored experience. PMOtto offers a simple, user-friendly interface, and demonstrates its utility in project management tasks like creating work breakdown structures and identifying project risks.

10:07

🌐 Advanced Integration and Corporate Utility of PMOtto

The third paragraph explains PMOtto’s advanced features and its corporate utility. It showcases how PMOtto merges its AI and project management layers with company-specific information to provide tailored advice and reporting. This seamless integration allows users to access a comprehensive knowledge base and corporate-specific data, facilitating informed decision-making. The paragraph also emphasizes PMOtto's ongoing updates and the intention to support users with efficient, AI-powered project management solutions.

15:10

🔍 User Guidance and Future Development of PMOtto

The final paragraph provides guidance for beta testers of PMOtto, stressing the importance of cautious use regarding confidential information and the necessity of user input for accuracy. It outlines the tool’s approach to continuous improvement based on user feedback and highlights the iterative nature of AI tools in project management. The narrative concludes with a call for feedback to refine PMOtto, illustrating the commitment to evolve and enhance the tool’s functionality in aiding project management.

Keywords

💡Generative AI

Generative AI refers to the use of artificial intelligence to create or generate new content, such as text, images, or music. In the context of the video, generative AI is utilized to enhance project management by providing a virtual assistant capable of understanding and producing human-like text based on the input from users. The video emphasizes the integration of generative AI with project management knowledge to offer unique insights and assistance.

💡Project Management

Project management is the practice of initiating, planning, executing, controlling, and closing the various phases of a project. It involves defining the scope, assembling the right team, allocating resources, and ensuring timely completion of project goals. In the video, project management is the core area of expertise that is being supplemented by AI technology to improve efficiency and decision-making for project managers.

💡Language Model

A language model is a type of machine learning model that is trained to understand and generate human language. It is capable of predicting the next word in a sentence or generating coherent text based on a given input. In the video, the language model, specifically GPT-4, serves as the foundation for PMOtto's natural language processing capabilities, allowing it to interact with users and provide project management advice.

💡Beta Version

A beta version of a product is a pre-release version that is tested by users to identify and fix any issues or bugs before the final release. It represents a stage in the software development process where the product is feature-complete but may still have some polishing or stability improvements to be made. In the video, the beta version of PMOtto is being showcased to demonstrate its capabilities and gather feedback for further refinement.

💡Virtual Assistant

A virtual assistant is a software program or AI system that provides assistance through digital interactions, such as text or voice commands. It can perform tasks, answer questions, and offer guidance, often simulating a human assistant's capabilities. In the video, PMOtto is described as a virtual assistant for project management, designed to help managers with tasks like risk analysis, stakeholder engagement, and project planning.

💡Machine Learning

Machine learning is a subset of artificial intelligence that involves the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data. It is a key component of AI, enabling systems to improve their performance over time as they are exposed to more data. In the video, machine learning is the foundational technology that powers PMOtto's ability to assist with project management tasks.

💡Natural Language Processing (NLP)

Natural Language Processing is a field of computer science and AI that focuses on the interaction between computers and human languages. It involves enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. In the context of the video, NLP is crucial for PMOtto's ability to communicate effectively with users and provide relevant project management advice.

💡Work Breakdown Structure (WBS)

A Work Breakdown Structure is a hierarchical decomposition of a project into smaller, more manageable components or tasks. It is a key deliverable in project management that helps in planning and organizing the work required to complete a project. In the video, the speaker demonstrates how PMOtto can assist in creating a WBS for a home construction project, breaking down the project into its essential components.

💡Stakeholder Analysis

Stakeholder analysis is the process of identifying and evaluating the interests, influence, and impact of individuals or groups who are involved or affected by a project. It is a critical step in project management for effective communication planning and ensuring that the project's outcomes align with the stakeholders' expectations and needs. In the video, the speaker illustrates how PMOtto can aid in stakeholder analysis by generating a table that includes the level of interest and communication approach for each stakeholder.

💡Risk Management

Risk management is the process of identifying, assessing, and prioritizing uncertainties and potential problems that could affect a project's objectives. It involves planning and implementing strategies to mitigate or avoid these risks to ensure the project's success. In the video, risk management is discussed as one of the key areas where PMOtto can assist project managers by generating a list of detailed risks associated with different types of work in a project.

💡Subscription Model

A subscription model is a business strategy where customers pay a recurring fee to access a product or service over a period of time. This model provides continuous value to the customer through regular updates, support, and access to features. In the video, the subscription model is mentioned as the future way for project managers to access the advanced features of PMOtto, ensuring they have constant access to the AI-powered project management assistance.

Highlights

PMOtto is a beta version of a project management tool powered by generative AI, aiming to assist project managers in delivering better projects.

The tool was created in 2017 with the purpose of leveraging machine learning to enhance project management outcomes.

PMOtto is built around the GPT-4 language model from OpenAI, which serves as its core for natural language processing.

A second layer, the Project Management layer, has been developed using insights from Antonio Nieto Rodriguez, a leading figure in project management.

This Project Management layer combines knowledge from various sources, including podcasts, articles, books, and presentations, to train the AI model.

PMOtto is not just a search tool; it melds the knowledge from the documents into a single entity, enhancing the capabilities of the language model.

The tool's database is updated daily with new information, aiming to become a comprehensive learning resource for project management.

PMOtto is designed to collaborate with associations, authors, and content creators to enrich its project management knowledge base.

The future三层结构 will include a subscription-based access, allowing project managers to have constant support from a machine learning-based natural language assistant.

A unique third layer will be added for corporate use, allowing companies to integrate their specific project management methodologies and internal data.

The third layer ensures security, with companies defining access levels and keeping data on their own servers.

PMOtto is part of the Microsoft Startup program and is built to be compatible with future language models beyond GPT-4.

The interface is designed to be clean and simple, with a focus on natural language interaction for ease of use.

Users can request various project management tasks, such as creating a work breakdown structure or analyzing stakeholders, in a conversational manner.

PMOtto can suggest improvements or missing elements in a project plan based on the input provided by the user.

The tool can generate detailed risk assessments for projects, complete with triggers and impact analysis.

PMOtto is intended to save time for project managers by streamlining the learning process, updating project information, and reducing the need for extensive reporting.

The beta version of PMOtto is being tested and improved continuously, with plans to add more features such as document and video recommendations.

Users are encouraged to provide feedback to help fine-tune the AI and improve the accuracy and usefulness of the tool.

Transcripts

PMOtto beta version is out there.

And I want to share with you some of the features,

some of the ideas behind what we aim to be.

The most powerful source of project management information,

using generative AI.

Let's see.

I created PMOtto with some friends in 2017.

And the aim was to use the power at that time

of machine learning to help project managers

to deliver better projects.

With all the growth of generative AI,

we included all aspects of generative AI

with the knowledge of project management,

to create a unique product that will be the

virtual assistant for all things project management.

But before I go into the demo,

I want just to draw and explain a little bit of the rationale.

First, PMOtto is based on a large language model,

and we are using today the GPT-4 provided by OpenAI,

as I would say, our core of natural language.

So when I think about that, I think that this center,

or this nucleus of PMOtto is the GPT-4.

And you may ask me, so why?

Why should I need to use PMOtto and not

GPT-4 or not Bard or not, for example,

any other large language model?

It's because we built on top of this first layer,

a second layer, and this second layer,

is the Project Management layer.

In this beta version, we are using all

the knowledge provided by Antonio Nieto Rodriguez,

one of the leading names in project management, the author of

The Harvard Business Review Project Management Handbook.

So we are using his information, articles,

insights, presentations, videos, and also

all my content from podcasts, articles, books, and everything.

And we combine it.

But when you do this search, and you use the PMOtto,

you are not searching for a document like you do

on Google, for example, or on my personal website.

No, what we did do? We used these documents

to train the large language model.

So to make sure that these two areas,

they meld themselves into one piece.

So it's like the ChatGPT with the turbocharge

of project management knowledge.

So someone that will have some specific knowledge around

project management and around this very

specific topic on how we manage projects

and all the interconnections of managing projects.

And the second layer will be updated on a daily basis.

Every new thing, every new agreement that we make,

we will bring into this database.

So this database, we aim to be a massive learning database.

Of course, ChatGPT will bring all the natural language,

on top of all this specific.

So we want to collaborate with associations,

we want to collaborate with authors,

we want to collaborate with anyone

producing interesting and relevant content

on project management to build this second layer.

And this is why we are still in beta

because we are today we probably have around 2000, 2500

different documents including podcasts,

podcasts transcripts, videos, and everything.

But our aim is to build pretty much

everything that is available for us to use in this tool.

And this brings us to our second layer.

This second layer in the future will be accessed by subscription.

So normal people, anyone, junior, senior project managers,

can just subscribe and they will have 24 times seven

a machine learning in natural language,

that will help them with any questions,

any content, anything they may need at their fingerprints.

But there is a third layer.

And this third layer is what will bring the uniqueness

of PMOtto to the corporate world.

And this third layer will add company-specific information.

And this third layer means you are a company.

And on this third layer, this blue layer,

you will be able to put the PMO methodology,

reports, and information of projects.

You can extract information from your internal systems,

and these people with

access to this third layer will be able to

combine the third with the second and the first.

So you can, for example, go on PMOtto and say,

what should I concern today in my portfolio of projects?

Then what will happen?

This information will come here and it will say,

what people should matter?

This is the project management content.

We'll translate this into natural language.

And it will check here in your database checking

which projects have that issue or not,

do not have that issue to present to you.

So it's the end of your need for reporting is the end.

You just say, "what should I be concerned

about this week with this project?"

And what is the most important?

We are building this using

all the security that is required in this layer.

So if you are a company,

you can define who will have access,

Even us. We do not have access to this blue area.

It's it's you. You will have the part where

you can upload your documents and these will

stay in your own server.

What we will do? We will combine and meld this

into machine learning and you will ask this in a seamless,

you will navigate on layer three, layer two, and layer one

in a natural language.

This is our aim.

Today we are releasing.

This layer two.

And this is exactly what I want to show you now with PMOtto.

So when you see here, this is the basic screen.

Remember, we are improving this as we speak. Okay?

So what we did, just to make the use very simple,

we created a very clean interface.

And here in this area is where you type your message.

There is a button here you can create a new chat.

You can select GPT-3.5 or GPT-4.

And I want just to stop here.

Today we are using the Microsoft platform.

PMOtto is part of the Microsoft Startup program.

So they are giving us all support.

But the concept that we aim at PMOtto is that

it doesn't matter how this evolves,

we will be always able to connect with a large language model.

And today we are using GPT-4.

If the future is a different one,

we will be able to connect pretty much the same way

and the user will not even know.

So probably in the future,

you will not select any more GPT-3.5 or 4.

But we are doing this because GPT-3.5 is very fast

and for simple tasks it's quite good.

And GPT-4 is more complex and more elaborated as we speak,

these things will, will change.

And this is mostly for the beta versions

because we have limits. So the better users will have

a limit of tokens that they can use over time.

And this is just to know if you are

exceeding that limit or not.

So just to give you an idea, one question and one answer,

it's roughly 1500 tokens.

And if you want to have more information,

you know, you can just click on the

information here and you can see how the

tokens, okay? For you to understand a little bit better.

These tokens can be a letter can be a phrase.

And this is the way GPT-4 measures performance

and measures the usage of the tool.

And here is my login and this is the

chat history that I will show you in a minute.

So let me use it a little bit.

For example, I'm building a house.

Let me do a very simple example. I'm building a house.

So what do I want? I want the PMOtto to answer me.

I want the PMOtto to suggest the first level of

a work breakdown structure of my new house,

that I'm building for my family. Okay?

And I want them to split the work

into types of work required in the house.

So I will just send this to PMOtto.

And here it is.

So it's explained to me, it's suggesting to me these seven items.

And remember, this is a high-level WBS,

for each of these tasks, okay?

It's explaining a little bit that is the concept

behind all these large language models.

And I can do it in a different way.

I can say I created the WBS.

And the WBS has these foundation walls

ceiling, plumbing, electrical works,

carpentry, painting, and finishing.

I just want to know, please PMOtto, am I missing something?

There is something I need to be concerned about?

And then I just send this.

So it's answering me. Okay, you have this,

but it's suggesting me heating and ventilation. Okay?

Permits and Legal. It's suggesting me other things.

If I don't like it, I have the thumbs-down button,

I have the thumbs-up, and I can copy this to wherever I want.

Just continue on the same thread.

I can also make another request.

I want, for example, a list of risks of this project.

So I want to ask.

PMOtto, I'm building a new home.

Prepare a list of detailed risks of this project based

on the different types of work, and use the following structure:

What the risk is? What's the trigger for the risk to happen?

What is the impacted area if the risk happens?

Present it in a unique sentence for each risk. Okay!

Then I send.

Here, you can see look all the risks here,

the eight risks it suggested to me.

And of course, I can come here and create a new chat.

And it claimed me my screen and I can work on,

for example, a different topic.

Now I want to analyze my stakeholders

and then I want to say: okay, I'm building

the new house, I knew the main stakeholder.

I want also you to put the level of interest

in the project of its stakeholder.

And I want to do it in the low, medium, high, power,

and influence, suggesting the communication approach.

So I want to prepare a stakeholder and a

communication plan at the same time,

and I want this in a table format. Okay?

To help me with that. And then I just press send.

And here we are. You can see the table, everything,

local government, suppliers. Okay?

And it gives me some hits.

So, look, I can save hours and hours of work using this.

And this is being updated as we speak,

with different stakeholder matrices, templates, and everything,

more generic templates on this layer.

And if you are using the corporate,

your templates on this, and what is important?

That is a complete blockage from, for example,

anyone using this level to use this,

this is your corporate, this will be access

with your login information inside your company.

And you can define all the policies

on how people can access this information and

what type of information based on the Active Directory

and all the protections of your network.

This here will be access to all subscribers.

So all this database.

And here in the middle, we have the GPT-4.

That will be the engine of this natural language.

Because for me, the key aspect of PMOtto and what

makes PMOtto easier, saving you time on learning,

saving you time, for example,

when you update anything on your project and

saving time on reporting because you just use natural language.

And if you come here on the chat history,

you can have your chat history,

you can, for example, put on a favorite

and then you will access the favorite chats

because you can have, for example,

a favorite chat that you are preparing for a certification,

or a chat about a report,

or a chat about one specific project.

So you can, and remember as a large language model,

it learns as we speak.

So if you use more and give more context,

it will improve the answers.

And of course, we can see here 97.300, tokens.

So it will update all the time.

What I'm showing you is the beta version.

This is the beta, and we are aiming to test it.

But what you will see very soon?

That on the top of this it will recommend to you

the videos, it will recommend to you the documents,

it will recommend the books you should use,

or even if you are on this third,

the methodology or, for example, something in

your internal file system that will explain

how to do that or a specific procedure.

For example, one thing that for me it's like magic.

Let's suppose you need to prepare a report.

And you know, you always think:

Okay, what is the template for this?

What is the template for that?

And if you use PMOtto on this third layer,

you can say, I want to use our corporate template.

And it will be presented inside the corporate template

with the fields of the corporate template.

And this will be not only a time saver,

but it will help you to padroniz information.

It will help you to make sure that people get

the information as fast as they can.

They just need to ask that.

So I hope you enjoyed this video. Okay?

For the beta tester, it's super important

that you have two things in mind.

Is that you should not today use confidential information,

or rely 100% on this because this is machine learning.

It can have hallucinations. We are working on that diligently.

But you need just to be mindful.

One thing that I want just to wrap up this video,

it's I always suggest anyone using

artificial intelligence engines and

generative AI to always keep in mind this, look here.

This is a line that goes from zero information

to 100% information.

You need to understand that:

from zero to, I would say 5%. It's you. You.

It means you need to make the right prompt.

This is exactly what we write in this message area here.

This is exactly what we write here.

If you write in a poor way,

all the engines will be in trouble.

So you need to give the context.

You need to be specific, you need to show

your intent and you need to present the format.

This is why I call all this CSIF context, specific,

or specification, intent, and format.

You always need to prepare a prompt this way.

This is your 5%.

Then the engine brings you from five.

Up to, I would say 80%.

Then comes back to you again.

To verify.

I cannot take this and copy this table of

stakeholders that I'm seeing here.

And just paste and put this in my report because maybe

in the middle here there is something I need to adjust.

And this is exactly why we do this 20%.

What we are doing now?

And we are working 24 times seven to improve it.

To improve this red, to help you to provide

a little bit less and brings you closer to the endpoint.

But please, every time you use this, understand this.

So when you use this in production,

you need to rely on yourself for this last mile of information.

And this is not PMOtto.

This is every single generative AI tool you may use.

And the second request is, please give us feedback.

If something is not right,

just be patient and provide us feedback.

Tell: this is not working and send it to us saying,

Look, I didn't like this and this.

Because we will receive and we will compile this,

so we can understand what is working,

what is not working to fine-tune the engine

because our main work is to fine-tune these connections here.

So I hope you enjoy using PMOtto,

and please provide feedback, and let's build together

the future of project management.

Thank you.