AI in Project Management: Ricardo Vargas Shares Six Trends That Will Disrupt Our Work

Summary

The video script discusses the transformative potential of AI in project management, highlighting its ability to improve the success rate of projects which is typically around 35%. It outlines six key aspects where AI can be leveraged: project selection and prioritization, automating PMO functions, methodology selection, compliance monitoring, stakeholder management, and improvements in project planning. The script emphasizes AI's role in enhancing decision-making, automating repetitive tasks, and providing data-driven insights, suggesting a shift towards more sophisticated project management practices.

Takeaways

• 📈 AI can significantly improve the success rate of projects, which is traditionally around 35%.
• 🤖 The use of AI in project management can help select and prioritize projects based on extensive data analysis, beyond just financial metrics.
• 📊 AI enables organizations to automate reporting processes, improving efficiency in project management offices.
• 🚀 AI can suggest the best methodologies for projects, such as Agile, Scrum, or Kanban, based on historical data and outcomes.
• 🔍 Compliance monitoring is enhanced with AI, which can flag discrepancies between reported progress and actual progress.
• 💡 AI helps in stakeholder management by tracking behavior in previous projects and assessing their impact on project success.
• 🛠️ AI assists in defining and planning project work, improving user stories and reducing ambiguity and omissions.
• 📅 AI-driven scheduling provides more accurate estimates based on historical data and learnings from previous projects.
• 🤔 The future of traditional reports and dashboards may be replaced by AI-driven real-time data and insights.
• 👩‍💼 Virtual assistants, like chatbots, can handle many operational tasks, freeing up project managers for more strategic work.
• 🧠 AI's role in project management is to augment human skills, allowing project managers to focus on creativity and complex problem-solving.

Q & A

What is the typical success rate of projects as mentioned in the transcript?

The typical success rate of projects mentioned in the transcript is 35%.

What does the speaker doubt about the 35% success rate?

The speaker doubts that the success rate might even be smaller than 35%.

What was the topic of the article written by the speaker and Antonio Nieto for the Harvard Business Review?

The topic of the article was how AI can transform project management.

What concerns do people usually have when they think about AI?

People usually worry about whether AI will take over their jobs, replacing roles such as project managers, team members, or PMOs.

How can AI help improve the success rate of projects?

AI can help by selecting and prioritizing projects more effectively, automating reporting processes, assisting in the selection of the best methodologies, ensuring compliance, managing stakeholders, and improving the definition and planning of project work.

What are some of the traditional criteria used by organizations to select projects?

Traditional criteria include financial metrics such as return on investment (ROI) and net present value (NPV).

How does AI enable better project selection?

AI can analyze internal data from sources like ERP systems and previous projects, using machine learning algorithms to suggest projects that are more likely to deliver better results.

What is the role of AI in project methodology selection?

AI can automatically suggest the most suitable methodology for each project, such as Agile, Scrum, or Kanban, based on historical data and project characteristics.

How can AI assist with compliance in projects?

AI can monitor progress and compare it against reported data, automatically flagging discrepancies and ensuring that compliance standards are met.

What is the potential impact of AI on the future of reporting and dashboards in project management?

AI could potentially replace traditional reports and dashboards with real-time, data-driven insights that continuously update and provide decision-makers with immediate information on project status.

What are some tasks that virtual assistants, powered by AI, could perform in project management?

Virtual assistants could help with tasks such as pre-populating project plans, automating payments through smart contracts, and managing resource allocation.

What advice does the speaker give to project managers regarding the future impact of AI?

The speaker advises project managers to adapt and leverage AI to enhance their work, focusing on using soft skills and addressing complex situational problems that require human creativity and decision-making.

Outlines

00:00

🚀 Transforming Project Management with AI

This paragraph discusses the low success rate of projects and how AI can revolutionize project management. It highlights the common concern about job security due to AI but emphasizes that AI can actually improve the success rate of projects. The speaker mentions an article written with Antonio Nieto for the Harvard Business Review, which explores the use of AI in project management. The main intent is to show how AI can help increase the success rate of projects by using internal data, machine learning, and algorithms to select and prioritize projects more effectively than traditional financial criteria.

05:04

📊 Enhancing PMO and Compliance with AI

The second paragraph focuses on the application of AI in the project management office (PMO) and compliance. It talks about how AI can automate reporting processes and improve the selection of methodologies for projects. The paragraph also discusses the use of AI in compliance, where it can flag discrepancies between reported progress and actual progress through analysis of photos and measurements. Additionally, it mentions the potential of AI in stakeholder management and risk assessment, suggesting that AI can pre-populate risks based on historical data and learned decisions.

10:04

📈 Improving Project Planning and Scheduling with AI

This paragraph delves into how AI can enhance the definition and planning of projects. It talks about the use of AI in improving user stories, reducing ambiguity and omissions, and scheduling tasks more accurately. The speaker suggests that AI can analyze millions of users and tasks in an automated and cost-effective way, leading to a continuous learning process that improves project planning over time. The paragraph also raises the question of whether traditional reports and dashboards will still be necessary with the advent of AI, as AI can provide real-time, data-driven insights directly to stakeholders.

15:08

🤖 Virtual Assistants and AI in Task Management

The fourth paragraph discusses the role of virtual assistants and AI in managing various tasks related to projects. It introduces the concept of chatbots that can interact with users in a human-like manner to gather project information and pre-populate plans. The speaker also talks about the potential of AI and blockchain in automating processes such as paying suppliers through smart contracts. Additionally, the paragraph touches on AI's role in resource allocation, management, and conflict resolution, highlighting the growing investment in virtual assistants to free up project managers from operational tasks.

🧠 The Future of Project Managers in an AI-Driven World

The final paragraph contemplates the future of project managers in the context of AI advancements. It acknowledges that project managers who focus on operational tasks like report preparation, data collection, and administrative duties may face challenges as AI takes over these roles. However, it also presents an opportunity for project managers to leverage AI to enhance their work, focusing on soft skills, supporting teams, and solving complex problems. The speaker advises project managers to adapt and transform their skills to benefit from AI, emphasizing that AI cannot yet match human creativity and the nuanced aspects of connecting ideas to reality.

Keywords

💡success rate

The term 'success rate' refers to the percentage of projects that are completed successfully, achieving their intended goals and outcomes. In the context of the video, it is mentioned that the average success rate for most projects is 35%, indicating a significant room for improvement. The video discusses how AI can potentially increase this rate by improving various aspects of project management.

💡AI in project management

AI, or Artificial Intelligence, is the integration of computer systems with the ability to perform tasks that would typically require human intelligence, such as learning, problem-solving, and decision-making. In the video, AI is presented as a transformative tool for project management, capable of enhancing processes like project selection, risk assessment, and stakeholder management by leveraging data and machine learning algorithms.

💡project prioritization

Project prioritization is the process of determining which projects to undertake based on their alignment with strategic goals, potential impact, and available resources. The video emphasizes the challenge of selecting from numerous candidate projects and suggests that AI can aid in this process by analyzing internal data and historical project performance to recommend projects with the highest potential for success.

💡machine learning

Machine learning is a subset of AI that involves the development of algorithms and statistical models that allow computers to learn from and make predictions or decisions based on data. In the video, machine learning is presented as a critical component of AI's potential to transform project management by enabling the analysis of large datasets to identify successful project patterns and automate decision-making processes.

💡project management office (PMO)

A Project Management Office (PMO) is a group or department within an organization that is responsible for overseeing and standardizing the project management processes, methodologies, and documentation. In the video, the PMO is highlighted as a key beneficiary of AI, where AI can automate reporting, suggest the best methodologies for projects, and improve compliance monitoring, thus enhancing the efficiency and effectiveness of the PMO.

💡stakeholder management

Stakeholder management refers to the process of identifying, analyzing, and engaging with individuals or groups who have a vested interest in a project's outcome. In the video, AI is presented as a tool that can track and analyze stakeholder behavior from previous projects, providing insights into which stakeholders are most critical and how to engage them effectively for project success.

💡compliance

Compliance refers to the adherence to rules, regulations, standards, or specifications in the execution of projects. In the context of the video, AI is shown to play a role in ensuring compliance by automatically flagging discrepancies between reported progress and actual conditions, such as comparing reported completion rates with AI-analyzed photos.

💡virtual assistant

A virtual assistant is an AI-powered system that can perform tasks, answer questions, and provide support in a manner similar to a human assistant. In the video, virtual assistants are discussed as tools that can help project managers by automating various tasks, providing real-time support, and even handling complex processes like resource allocation and payment execution through smart contracts.

💡software testing

Software testing is the process of evaluating a software application to detect any defects or errors. In the video, AI is presented as a means to enhance software testing by conducting more extensive and reliable stress tests, reducing test expenditures, and minimizing human bias through the analysis of large, clean datasets.

💡risk assessment

Risk assessment is the process of identifying potential risks, analyzing their likelihood and impact, and determining the best strategies to mitigate them. In the context of the video, AI can be used to pre-populate risks based on historical data and learned patterns, thus providing a more informed and proactive approach to managing project risks.

💡project manager

A project manager is an individual responsible for planning, executing, and closing projects, ensuring they are completed on time, within scope, and to the required quality standards. The video discusses the potential impact of AI on the role of project managers, suggesting that while operational tasks may be automated, the need for human skills like creativity, problem-solving, and people management will become even more critical.

Highlights

The success rate of most projects is typically 35%, indicating a significant room for improvement.

AI has the potential to transform project management and increase the success rate of projects.

AI can help in selecting and prioritizing projects by analyzing internal data and suggesting which projects could deliver better results.

AI enables the automation of reports, making the reporting process almost automatic and less time-consuming.

AI can assist in choosing the best methodology for each project, increasing the sophistication of the approach.

AI helps in compliance by raising flags when discrepancies are found between reported progress and actual progress.

AI can track the behavior of stakeholders in previous projects and predict their impact on future projects.

AI can improve the definition and planning of work related to the project, enhancing the quality of user stories.

AI reduces ambiguity and omissions in user stories, leading to more accurate project planning.

AI can optimize scheduling by learning from past projects and providing more accurate task duration estimates.

The future of reports and dashboards may be replaced by AI-driven data analysis and real-time insights.

Virtual assistants, like chatbots, can help in project management by automating tasks and providing 24/7 support.

AI can automate processes like paying suppliers using blockchain and smart contracts, reducing the need for manual intervention.

AI can aid in resource allocation and management, offering data-driven advice for efficient use of resources.

Advanced AI testing in software and systems can increase reliability and reduce test expenditures.

Project managers should focus on leveraging AI to enhance their soft skills and solve complex situational problems.

Transcripts

35%.

35% is usually the success rate of most projects.

And these number is devastating.

And I need to be honest with you,

I doubt even if the number is 35.

Maybe it's even smaller.

It means 65% of all the projects we do

just do not deliver the benefits.

And recently, Antonio Nieto and I had the chance

to write an article for the Harvard Business Review.

The article is here,

and you can download it through the link above.

And in this article, we were talking about AI,

and how AI can transform project management,

and what was our intent?

When people think about AI, they think about technology.

Usually, will I lose my job?

Will I replace my job as a project manager or

as a team member or as a PMO?

And I want just to start

by explaining that AI can be one of

the biggest ways we can help that number,

that 35 to go up and deliver better results.

And this is exactly what I plan to talk about in this video.

I will share with you six aspects

that we can use and understand in terms of AI

in project management.

The first aspect is

using AI to help us to select and prioritize projects.

This is a big challenge.

Most organizations have hundreds,

hundreds of candidate projects.

And most of the time they use only financial,

like return on investment or net present value,

as the too to decide, I should do this project

or that project.

With AI, we can go above and beyond that.

Your company can use internal data to

combine these internal data from the ERP,

from previous projects, from the previous business,

and using machine learning and using algorithms,

can suggest to you which the projects

that could deliver a better result for you

in an automatic way.

Instead of using three criteria, four criteria or a

financial criteria that are in any way a

projection of the future, right?

Because for example, when you calculate, for example, a

net present value of a project that is

happening in the future,

you are automatically making a guess

that you will deliver that benefit.

But with AI, you can analyze thousands,

millions of projects that have some type of similarity.

And by using AI

you can understand which kind of pattern that

are driving projects to be more successful or less successful.

And you can use these AI and these kinds of mechanisms

to help you to select much faster

with a much more reliable way

the projects your organization should do.

And this will drive your organization

to make a much sharper decision using

billions of criteria instead of one, 2, or 3.

The second aspect is the support of the

project management office, and it's all about

what you can automate with that, so you can

automate reports.

So the reporting process becomes almost automatic.

So you don't need to go and try to

find and crush the data to automate the process.

Another aspect that is key,

it's about the selection of the best methodology

and this is a big discussion.

Which kind of approach should I use for each project?

With AI, you can do this almost automatically and

these will increase dramatically the sophistication

of your approach because it's so easy to do:

oh, for this project you should use Agile,

and for this project, you should use,

for example, Scrum or Kanban.

You know, with that you will have a far more reliable result

to trust in order to define what is the best tool I should use.

Remember always the concept of the Swiss army knife.

Another aspect is about compliance.

For example, let's suppose you receive a

report that says that 20% of that wall is ready.

But then the photo that was taken by AI and the measurement.

Does not agree with that.

It looks much more like 10%.

And these will raise a compliance flag

saying, oops, some information here may not be right.

In all of these using AI, you will

trigger some exceptions and some compliance.

That will come naturally, not through human-based decisions.

And last but not least at all, it's

all this stakeholder management I will see

and track the behavior of stakeholders in

previous projects.

And I will combine this with AI saying,

Look, you said that stakeholder.

is not as critical as you think, but I disagree with that.

That stakeholder is very critical.

Risk assessment.

Imagine you pre-populating the risks based

on historical information and other AI-learned decisions.

So all of these will change dramatically the

way the PMO will operate.

The third one is all about improvements in

definition and planning in all work related to the project.

And this is also a big thing

you can improve user stories.

Today we can see tools like ChatGpt creating

user stories for you, so you can understand

different aspects and have a far better

sophistication in analyzing,

how you should approach your project.

Another aspect is the

reduction of ambiguity, omissions, and all

failures related to these user stories.

It's like you analyzing millions of users using

this in an automated, quite cheap, and effective way.

Another aspect is all these

scheduling being enabled by AI.

So you don't tell anymore of that task will

take ten days or five days or two weeks.

No, AI will tell you a look based on the

learnings we face, you cannot do these five

tasks in ten days. It will not work because

it never worked before. You are using the

same process and it's learning.

And every time you do a new project, a new sprint, all

of these will be fed and will become a new

set of data that will improve.

So it's like a permanent learning process.

And this makes me think.

Will reports continue to exist with AI?

Because why I do a report I do to communicate to give

people the information they need.

But now, with this GPT processes together and

combine it with my information process.

I can have a GPT-like server inside my organization

that will crash all the data from the finance,

everything together with all these external data.

And will allow me to go as a sponsor saying,

please tell me what projects should I be concerned about.

And then you press enter. And suddenly you say,

Oh, this project is very it's in very

big trouble because of this and that.

But this is not a human thing. This is data.

This is data and artificial intelligence

helping you to decide. So I don't know

honestly if reports or dashboards even exist in the future.

The fourth one is a virtual assistant.

Imagine you having someone on your side

that is ready 24 times seven to help you

with many different tasks. One example that

I'm super proud of, is the concept of a chatbot.

I was one of the founders of the PMOtto, and

these at that time were truly disruptive.

What is the idea behind the chatbot is that

instead of you going to slack, for example,

trying to figure out the, you know, the work

you need to do or a Trello board or Microsoft project?

No, you just talk to a chat in a

very human-like way and you say, I have this project

and then the chat will ask you questions,

say, but Ricardo, when do you plan to start this project?

Then you answer, Well, maybe next month.

And then which kind of resource?

I'm planning to use John I'm planning to use Anna.

I'm planning to use Gabriela.

And then that chatbot will pre-populate your plan.

So imagine you have a template, but it's not

a template anymore. It's an unlimited template.

This is exactly, absolutely, exactly what is coming.

And it's coming fast in terms of virtual assistants.

Another aspect of virtual assistants.

Can be automated processes.

Paying suppliers, using, for example, a combination

between AI and blockchain and all

these smart contracts to make payments automatically.

So you don't need to have an

accounts payable area to do that.

It will be all executed by smart contracts.

So this is another example.

A third example is all the concepts of resource allocation,

management, conflict of resources, and all of this.

It's like you have an advisor on your side

that basically knows everything on the

data you already have.

So all these concepts of the virtual assistants

will come and will come heavily.

Most companies today are investing heavily in that.

Because it will free up us.

To do different things.

The fifth one is

advanced testing in software and systems.

By using AI to do these tests, you can do many

stress tests. You can reduce your test expenditures,

you can increase the reliability of your test,

and you can do all of this automatically.

And if you have a really clear and clean data set,

there is a chance that you can reduce one of the biggest

challenges we have which is bias. Bias.

And I'm not saying that AI will come

without bias, because it's just impossible,

because AI is what humans make of it.

So if you have any prejudice in real life and

you feed the data with this prejudice, there

is no such way that I will say,

No, I don't have this prejudice, and this is such a

polemic area.

But with that, you can have a more clear set of data.

For example, if you want to analyze photos,

if you want to analyze data,

if you want to analyze performance, you can easily,

with these big sets of data, remove some of

the data that could conduct a human bias.

And last but not least,

What about the project manager?

And this is not an aspect and I don't have a

crystal ball to tell, but my advice is that

if you work as a project manager or as a

scrum master or as a product owner,

I don't care how do you call yourself.

But if your work is to prepare, report, collect data,

write reports, write emails. pay bills.

Approve expenditures.

Then your work may be in trouble, may be in trouble.

Because all these operational things will be done by AI.

But what is the biggest opportunity?

It's, if you know how to use this, if you implement

and if you transform this into your benefit,

you can leverage your work to another level.

You can leverage your work.

To use soft skills and support people

to deliver their projects.

You can use your work to solve complex situational problems.

It's like today when you see ChatGpt.

ChatGpt is an amazing thing.

To write, simple text to do copywriting.

But I don't believe that the current state

that ChatGpt has the creativity even close to

a human being. I have a good friend that is

an author and an extremely creative author

and he said that most of the time the current

ChatGpt is like the pasteurization of language.

You know it produces a decent result that

supports most of the traditional communication.

But it does not disrupt, if you ask it to write poetry.

It will not be poetry that will, you know,

breakthrough, you know, change how society behaves,

At least not yet.

And this is the niche where there is a

massive space for project managers, for team members,

for Scrum masters, for anyone connecting ideas to reality,

to make an absolutely wonderful experience.

So I hope you enjoyed this video. And please

subscribe and join us in a family that aims

to discuss project management, risk management,

crisis management, artificial intelligence,

and all aspects of connecting ideas to reality.

See you next time.