AI challenges in agile project management – with Dan Stelian Roman
Tune in to this webinar and discover the hurdles of integrating AI into projects using agile delivery frameworks. Uncover effective strategies for navigating these challenges while highlighting the importance of agile methodologies.
Artificial Intelligence (AI) is crucial in project management and is often integrated with agile methodologies in digital transformations, such as robotic process automation. However, despite its potential, harnessing AI’s full capabilities presents challenges that could impact the value delivered to the business. Despite the advantages of AI, people remain pivotal for successful project delivery.
Watch this webinar to navigate the challenges of using AI within agile frameworks. The webinar aims to offer practical insights into effectively leveraging AI to enhance project delivery.
About Dan Roman
Dan Roman is a Senior Project Manager with expertise in AI, agile methodologies, and project management. Starting out in Computer Aided Design and Manufacturing (CAD/CAM), his career journey encompasses impactful research ventures, spearheading manufacturing process enhancements and software innovations, and global digital transformations.
Dan specialises in business transformation projects as an independent project management consultant. Holding a master’s in production management and certifications in Lean Six Sigma, PMP, and ACP, he actively contributes as a PMI Subject Matter Expert.
Recognised as the 2021 Volunteer of the Year, Dan’s publications, and proficiency drive industry standards, helping to push forward agile project delivery.
Agile project management training
Agile project management thrives on adaptability, collaboration, and flexibility, which are pivotal for successful project outcomes.
AgilePM (Agile Project Management) certification equips individuals with the skills to effectively apply agile principles, techniques, and frameworks. The knowledge gained from this certification helps to foster seamless teamwork through better communication, collaboration, and adaptability, ensuring heightened productivity and more successful project deliveries.
Learn Agile with agileKRC
agileKRC’s agile courses cater to varying expertise levels, empowering beginners, and professionals alike. Elevate your skills, boost your confidence, and accelerate your career in Agile with this comprehensive range of agile courses.
Explore more details of the courses with the relevant links below:
Read the full transcript of the webinar below.
00:00:00 Dan Stelian Roman: Thank you and welcome everybody to this my first webinar with agileKRC. It’s about some challenges that AI is introducing in our profession and some areas where the AI is challenged when it’s using project management in my view. We’ve seen a lot of the other side of the coin. AI is very good, it will revolutionise everything, it will replace PM’s. We had this with Agile also that you know once again comes on board. There is no need for PMS we have with a lot of other technologies that came. I’ll try to show you my view about how AI can be used and what are some of the challenges of AI in project management.
00:00:52 Dan Stelian Roman: I wanted to show you a short history of artificial intelligence. It didn’t start in 2000, it started with people thinking if it is possible to have machines that can mimic the human thinking. The term artificial intelligence was a sort of marketing gimmick when it was introduced in 1955, some people wanted the funding for research, and they put this nice artificial intelligence in the title. But even before that study, a lot of research was done in terms of how the computer can be used to simulate thinking. There was a language that I don’t know if it’s still around Lisp that was created specifically to write artificial intelligence programs.
00:01:47 Dan Stelian Roman: Then in 1960s, the robots that it’s one of the reasons for having the artificial intelligence, they started being introduced in the industry. And then there was a very nice software that I was lucky to use in 80s Eliza, that it’s what we call now chat bots or chat robots. And it was very interesting because even for us that we were developers, we knew that it’s software at some point you start thinking that you speak with a real human. What was different with Eliza was that she was learning if we can call her, she. So, whenever you use a word that was not in the in her vocabulary, she will ask you know, what about is this can you give you more details? It’s like what? And that’s one of the dangers that I see we teach her some very nice things.
00:02:53 Dan Stelian Roman: Then a you can see that some people very optimistic in 1960 – 1970 that in a few years there will be a machine that it will be same level average human being whatever average means. I put on this thing that grey area which is freezing period when the funding for artificial intelligence was not that much because there was a huge promise, but maybe the technology was not there. Maybe the research was not there, and the funding dried out over, let’s say, almost 10 years.
00:03:31 Dan Stelian Roman: And then there were some good results, like the IBM computer beating Garry Kasparov. And then the speech recognition that came with Windows, now it’s in every mobile phone and then some people think that what Twitter, Facebook, Netflix does with the marketing, it’s artificial intelligence. Yes, there are patterns, but we can believe that he’s thinking behind finding your preference. He’s just finding patterns in the usage of that product, and you can see that there was also the Xbox with kinetic with identifying the movement and then the speech recognition that is now on the phone. So, it’s a very old thing, it’s not the last 5-6 years. What changed in the last 5-6 years is the technology or 10 years. The technology became more supportive for artificial intelligence, for software that can mimic human thinking.
00:04:40 Dan Stelian Roman: I put some definitions that not because I don’t believe that people know what artificial intelligence is, but because they were very old like 1966. People define what artificial intelligence should be and that it should be compared with the human intelligence. If you want to know if it’s good enough or it’s real artificial intelligence. What is very important it’s the second statement that artificial intelligence should adapt to the environment should understand the context, which is not really the case with a lot of software that now it’s called artificial intelligence.
00:05:19 Dan Stelian Roman: First point yes, is true the artificial intelligence software will look for patterns. But they won’t adapt their thinking based on the context. And the there was a turing test, you know where everybody knows the Imitation Game who Alan Turing was. He said that when “A software will fool us that it’s a person, then that’s artificial intelligence”. As I said I know many programmes that can fool people that they are intelligent, but they are not really intelligent. And this quote it’s from Pascal, Blaise Pascal’s, the one that gave the name to the language. So, in 1660 he said that the mathematicians and it’s very valid for developers, software developers. They lose track of a reality because they want to formalise everything, and this is what artificial intelligence supposed to do. It’s supposed to use algorithms and thinking and software to mimic the real world. It was hard in 1660, it’s also hard today and it’s good to understand that if we want to speak about real artificial intelligence. It’s if we are able to describe the thinking, the human thinking in an algorithm or in a formal way, so the computer can do what human being does, which is still a challenge, and I don’t think that we have there yet, maybe someday we will be there.
00:07:07 Dan Stelian Roman: So, what people think, or this is the general image about whether we use artificial intelligence and I choose some of the ones that can be used in project management. There is a big push now on the content creation, so you will know that we go, and you have homework. And some software will give you the homework and you just go to the school the next day, so that’s an achievement. We all know that there are digital assistants in teams, and they can take minutes of the meetings and do some planning, do some statistics. There are software artificial intelligence programmes that will provide coding, or some codeless software just based on we want to have a programme that does this.
00:08:03 Dan Stelian Roman: Of course, image Internet is full of image generated by AI. But to get good results from artificial intelligence from software in general, you still need the human expertise, so you need experts that will design the algorithm that will write the software. The initial software that writes other software and you do need large amount of data, and this is probably the biggest change from 1970s, 1980s today. Because the Internet you have access to a lot of information. You maybe know that there are like some newspapers that said don’t use our database to teach artificial intelligence and also things like Facebook, Twitter. There is a lot of information that is going to this database is Google that is searching. So, without humans and without a lot of market data, I don’t think that the software the robots can learn.
00:09:11 Dan Stelian Roman: And I took another example from a book that was written some years ago, many years ago. So, from what that person imagined in the 1990s. Of course, the terminology with window pens and all that stuff it’s what they thought 15 years ago. The one that is missing today that was not achieved when we speak about the artificial intelligence supporting meetings, it’s exactly the artificial intelligence we have, all the technology that can help us, but we don’t really have something that will help us to do decisions, to make decisions, to create something, so we still need the humans in that meeting to discuss the topic. And maybe it’s funny for at least for me, was very funny that they thought that there will be only electric cars and not that many cars on the road. You can see that that’s not the case. Yes, we have electric cars, but it’s not the way they imagined. So yes, some people are, let’s say they saw the future of artificial intelligence. But reality it’s a bit different and for me, it’s always very interesting to go back and read books that were written in 70s, 80s, about Agile, about other topics that are very popular today and see what they thought at that time.
00:10:45 Dan Stelian Roman: So, as I said, there are many, many books and they try to formalise what artificial intelligence can do and how far can you go with thinking. The important bit I hope that you’ll get access to the recording, and you can read each and every books. I split these things in what can be achieved with software and what it’s hard. It’s a challenge for artificial intelligence to achieve and this is not what we think today. It’s what people thought long time ago when I did the research in artificial intelligence as an academic exercise. And what they found and what they concluded is that when we go to more than formulas and algorithms and patterns that can be recognised by the computer. Artificial intelligence will be unable to mimic human thinking.
00:11:57 Dan Stelian Roman: And it will always be challenge, now let’s see what’s happening in the next tenth, twenty to fifty years. But even at the moment, for example translations, I think everybody was sometimes annoyed when you know both languages. So, let’s say you translate from French to English or from Russian to English, which is worse, and when you read the translator takes by the machine, you find out that the usage of some words it’s completely different. Because the computer doesn’t understand the context, there are a lot of words that mean completely different in different languages. And the danger, and that’s the challenge for the project managers is that when by translating with a machine, you change the meaning of that text and people don’t know that it’s a machine generated the text, then that becomes the rule.
00:13:03 Dan Stelian Roman: And I can see I speak a couple of languages. I’m not, you can say I’m not a native English speaker. I can see that in my mother tongue. There are a lot of words imported from English that mean completely different and the problem that generates. So, this situation is that when somebody speaks English and use that word in the context of his language or her language, then they basically translate different information, and it will be completely wrong what the other person understands. And the point of this research about this, but the conclusion is that whenever there is an exception, whenever there is something that it’s not pattern, it’s not formula. The computer won’t be able to replicate that kind of thinking. And speaking of agile, this is exactly what we do when we use agile in project management. We invent every day, we adapt, inspect, adapt and my view on agile is that you should never try to use the framework to implement the framework by the book. You should always adapt and that’s where the computers at the moment will be challenged.
00:14:31 Dan Stelian Roman: So, if we look at what the artificial intelligence software can do and can’t do, most of the things that the software can do at the moment are what the project coordinator does. So, in that way, yes, if we use software, if we use artificial to either replace or to help a project coordinator that works, so the whole organisation meetings, even creation of the draft of the documentation is fine when we have some more performant software. Yes, that software can find patterns, and we’ll create like you know games, they can look smarter than a human because they beat the human, but they beat the human just because they do a lot of combinations to find out which one will be the right one. And you can see that at that time and even today. Games were one of the most important areas of study for artificial intelligence. Because of that perceived random or unknown thing and the with the big data, with the large volumes of data, the computer indeed can find some rules, some patterns.
00:16:06 Dan Stelian Roman: But you can see 1990s later that the computer won’t understand the context, and I’m pretty sure based on my experience with chat bots, that it’s still a challenge to understand what the actual person wants. Based on keywords and I remember a conference when somebody from Google did a presentation about how they came up with this very good algorithm of a searching. And their revolutionary idea that it’s a new idea, it’s something that only a human can do at the moment. Was that instead of looking for keywords and do all this searching based on keywords. They start learning from the moment when the person stops searching because they assume then I think it’s correct that when you stop searching means that you found what you were looking for and comparing that result with the search parameters, they that created better algorithms. They just you know searching by keywords.
00:17:21 Dan Stelian Roman: And now I’ll go into the challenges that not only me, but a lot of other people see now. And you know there is a lot of talk about governance from humans. So, challenges of using artificial intelligence and the loss of human connection. And we can see this even after COVID when we work remotely, and some people find very difficult to adapt to office face to face because some of them they went from university doing online to work. And now some companies are calling back people to office and they found hard to connect with their colleagues.
00:18:09 Dan Stelian Roman: Lack of transparency it’s very, very important. Anybody who did an algorithm or a programme or whatever knows that first you do that thing manually and that would be the baseline, the benchmark and then once you write the algorithm, the software you compare your manual results. With what the computer says and that will give you the confidence that the algorithm is correct. When we can’t see, we can’t validate in any other way what the computer output is. Then it’s a danger and in project management, if we get a schedule that was done by software. And we take that schedule, and we try to implement or to manage the project starting with the wrong information of course will lead to wrong results. Bias and discrimination is something that we can see not only on artificial intelligence, but even when people use checklists. So, if you try to judge something without considering the context.
00:19:17 Dan Stelian Roman: And you just go through a checklist, you will get to the wrong outcome. Maybe the output is the right one. You know, we take everybody that it’s with five degrees and has 20 years’ experience and we put them in certain role and that doesn’t mean that they’ll perform very well in the role. And with the discrimination is because somebody, let’s say, may not have a university degree, but has the experience and the knowledge for that role. It won’t be chosen by the computer because it doesn’t have the university degree. Of course, everybody’s talking now about the ethical dilemmas. So, if we let the software the AI the computer to make decisions and then those decisions will impact real people then that’s a big, big, big risk.
00:20:13 Dan Stelian Roman: And we can see more and more, and my generation remembers when we use the phones, the landlines, we knew, phone numbers, we knew a lot of things without looking at the mobile. And these days, very few people will remember phone numbers. Of course, we have many more numbers than we used to have. And a lot of other information also we just go and Google and whatever Google says we take that the real thing. I’ve seen artificial intelligence programmes for project management and that’s what happened when you don’t challenge the output of that software. If you try to implement what the software said, it’s not so likely that you succeed with that project. In this slide I put, maybe you know, maybe you don’t know how the robot word was created. So, it’s a Slavic word for slave for work and that time in 1921 somebody wrote a play and in that play the robots look like humans. Of course they are actors in that thing, so that was the vision of what robot is. When they start imagining the machines that do what the people do.
00:21:44 Dan Stelian Roman: And the reality today is that we do have many millions of robots, but they do the work that humans either can do. Because it’s too dangerous, it’s too repetitive, and sometimes the environment is very toxic, so it’s not something that humans would like to do anyway. And that’s where we use robots to not replace people, but to help people to do certain things. And I think artificial intelligence should look at the same kind of usage. So, activities like the project coordination documents, meetings that are trivial for the project manager and will save time rather than thinking that software robot will create a very good business case that will be approved as generated by computer. Yes, the computer can generate a draft and even with translations you can get the draft, but then a human that understand the context and the implication of that document should go through the document.
00:23:00 Dan Stelian Roman: And what also we need to remember always that artificial intelligence is a tool, and it is a lean tool. There is also a confusion between Lean and Agile. So, lean it’s always about standardisation, about making things cheaper, faster, whatever, which is completely against the agile which is changing all the time, finding new ways, experimenting, failing. If we teach the robot that failure is not an option like we hear from a lot of managers, then the software will tell us the options. Where do you have a degree of success. But maybe by the time you get the output from that software, your input data is change and what was before either impossible or not a good option will be the best option that you can have.
00:23:56 Dan Stelian Roman: And something that is good, there is a fear based on the marketing materials that robots will replace project managers. I heard this about Scrum, in Scrum you don’t have a project manager, you don’t need a project manager, they’re only split between the team PO and Scrum Master. Any new technology there was a field that you it will replace people and we find out that that new technology will create more jobs than it replaces. Of course, the new jobs will be different kinds of jobs, but the number is still high.
00:24:34 Dan Stelian Roman: And I’m really pleased with the result of that poll at the beginning, it’s what I wished not what I expected. My view is that indeed, artificial intelligence will need more experienced project managers because the not so experienced project managers can be replaced the project coordinators can be less than we have now. Because there are still things that the project code can do, but the need for experienced people will increase. Same with the robots just because we have robots doesn’t mean that we don’t have humans in that factory. I saw a programme on TV when they said that everything in the production is automated, robots and everything else.
00:25:26 Dan Stelian Roman: But they have 5000 people in design, so 5000 it’s a huge number that will just use their brain to design new things and those things will be made by robots. And I think that will be the same thing with the artificial intelligence. There will be a lot of data manipulation information, manipulation that will go to people that we have the skills, the knowledge. To use that information to adapt the information to the context.
00:26:00 Dan Stelian Roman: I said before about Eliza, if you are curious, please read about this thing, it was written in 1966. I had an argument with somebody that yes, the programme maybe was in 1965 because in 1966 he just published the results. It’s very important to know that the author Weizenbaum he created this software to prove that computers can think. And you will always be able to find out that it’s not the human, so they won’t pass cheating test. The result is completely different, a lot of people including myself, they got very hooked in this chat bot. And yeah, I was not thinking that it’s practitioner, medical practitioner and it will solve my problems. But I thought that, and we thought my team that we can teach the software to do very smart things. We failed, maybe we were not that good but sometimes you know humans, they are too creative for a computer to understand what they want.
00:27:15 Dan Stelian Roman: And he also made the difference between decision and choice. So, you may have a certain decisions that look right, and that’s what the computer tells you based on our algorithm based on a pattern, doesn’t matter. But that will be what decision the computer it’s recommending. But the humans to choose what decision they make from the proposed decisions in that context, especially from the ethical point of view, very simplistic. If we have a software that says by replacing the phones with teams, we can reduce the workforce with 50% immediately. I don’t think that’s an easy decision or a wise decision to go and tell people that from tomorrow 50% of the workforce will go. Because we implement the new software that will eliminate the need of humans and then in few months you find out that you have a lot of problems you create more problems than solve.
00:28:27 Dan Stelian Roman: People are with my experience in the field they remember when the computer department was like 20 people, 30 people for the same size of a company like we have now. When the IT department can be hundreds of people. And out of hundreds of people, very few are software developers. Most of them are looking after the software systems. So, when we speak about replacing and the computer will give us very good decisions, we should be very, very careful based on this experience. With this programme, that fool a lot of people in thinking that they speak with the medical practitioner.
00:29:12 Dan Stelian Roman: So again, back to challenges, the computer is a black box. AI the software is a black box we don’t know, for example, how Google is finding whatever it’s presenting to us. We don’t have any way of validating that the search results are the ones that are the best that we need. Yes, most of them will be close to what we need. But how do we know that the output it’s exactly the best choice. I use a lot of risk management software like Monte Carlo simulation. And yes, on paper, using that kind of software is telling you when attached should start or whatever.
00:29:58 Dan Stelian Roman: But the whole calculation is based on the assumption that everybody working on that project has the same skills, experience can do what they want to do. And if we can’t validate in a different way manually or whatever, we just rely on that black box, and we should assume that it’s right. And if there are errors that will impact the output. When we write the argument, this is what we did with Eliza. We just put our thinking, our experience, in that programme. So, what she learned from us was not always the right way to do. And as I said, you know, when we based decisions on what a computer will tell us, then the risk is that we will have unethical decisions.
00:31:06 Dan Stelian Roman: And you never know what will be the consequences, so not as bad as in sci-fi movies when the robots take over and all that stuff. But when we impact large group of people, especially when you discriminate people based on some attributes, that will be a big social problem. And again, thank you very much for confirming my thinking about the need of experience project managers. The way I see it is that using the artificial intelligence should not lead to less knowledge, less experience in the project manager. It should add value to project managers that know more than an average project manager does now, so not only project manage, but many other things.
00:32:03 Dan Stelian Roman: And that’s where the value of artificial things come in place. By helping us with tasks that can be done by the computer, that frees us time for us to make better decisions, to learn new things, and so on. So, we shouldn’t look at artificial intelligence as replacement. For a part of what a human does, we need to look at artificial intelligence as a tool that will enhance what a skilled people can do. This is a comparison between what a project manager does, what a scrum master is expected to do according to the Scrum Guide to the definition of Scrum Master. I know that a lot of frameworks now they took the term. And there’s Scrum Master there’s many other things I know that in many organisations the Scrum master is basically a project manager. And what artificial intelligence can do in some of the domains that we manage.
00:33:11 Dan Stelian Roman: For example, leading the team yes, artificial intelligence can help, but it’s far from even a scrum master in terms of the efficiency of leading the team. I don’t see any way when a software can manage conflict. Building the team again, what can a software does when you have people, and you have to go to phone store to perform. It’s very, very hard for me to believe that the computer will define the ground rules because they always depend on context and so on. So, there are human to human relations and dependencies and things that an artificial software will never do.
00:33:59 Dan Stelian Roman: And is the same story with Scrum Master versus Project Manager. I always think that a project manager should be able any project manager should be able to do the raw scrum master in a project. The Scrum master is not created as a replacement for PM. The Scrum Master is created in a certain framework to do the certain things. And yes, using larger using Scrum you can do things some things better. Especially when it’s coming to team management, then the traditional project manager, but I don’t see a lot of value from artificial intelligence when it’s coming to people management.
00:34:43 Dan Stelian Roman: And these are the areas of project management, the 10 areas that PMI defined. I know that most of you are from UK, and you don’t follow PMI. I think they are valid in any framework, and you can see that the Scrum master is very challenged when you speak about integration like programmes and portfolio and all that stuff. Because the scrum master is looking after small Scrum team. So that’s my definition of agile, it’s very close to what Scrum does. Scrum Masters and Artificial Intelligence will be very challenged in risk management. We all know project manager that there are risks that we can’t put in the risk register. We can’t escalate, nobody wants to hear about them, but we have to manage them.
00:35:38 Dan Stelian Roman: Of course, people resource management when it’s coming to people, it’s very unlikely that AI software will help too much or can manage resources with the cost. Yes, software may know more than what the Scrum master is supposed to do and can help us with some things. But I don’t think that we can leave the cost, the budget management, the software without the project manager.
00:36:07 Dan Stelian Roman: And with the communication, that’s where the both agile, scrum master, and AI perform well, not because they do well, the communication is. Because they enable communication or there are channels and in terms of agile there are ways to communicate that are better than what we used to do in traditional project management procurement. Yes, you can have some software to raise PO’s, but to get the approval for that purchase of the it’s something that we have to spend a lot of time and again people, stakeholder management. I don’t see AI helping too much other than being the tool.
00:36:57 Dan Stelian Roman: So, I come back to somebody that it’s considered the smartest person that ever lived. One of them, Einstein. He said that the imagination is more important than knowledge. And this is where artificial intelligence it’s really, really challenge these days. They don’t imagine they took existing information they collect; they find patterns. They can do something, they can help humans, but that’s just knowledge is not imagination. So, when we see all these nice pictures, they are not new things. They are collected from whatever that software found on the Internet.
00:37:41 Dan Stelian Roman: And again, my conclusion, artificial intelligence is a tool is not a replacement for human intelligence. So, we should be very careful when we delegate some of the thinking to a machine because that machine may not be as good as us. To use artificial intelligence, you need more skills and knowledge to understand the how to use the output of that software, you need to understand what you need and the context. I don’t see in near future or even not so near future machines, software managing people and not the Skynet and all that stuff. But even in our organisation when you have team to delegate the role of team lead to a software.
00:38:35 Dan Stelian Roman: Everybody who manage projects knows that technology is not as challenged as the people, people are different. Each person has different skills, different goals, different personality, and you need all of them in the project. That’s what a good project manager and Scrum master does. Putting the people that are different and making the team. As I said, being a lean tool oriented towards efficiency, cost cutting and so on, it can be an inhibitor for agile, for creativity, for risk taking, and so on. So, we need to be very careful that when we use best practises. We don’t forget to create new practises that will be the best practises in the future.
00:39:26 Dan Stelian Roman: And like robots, AI should focus on tests that don’t have a social impact, can be done better than a human. So just replacing even in in the organisation with charts and the e-mail we replace human communication with tools. I’m not sure that’s the best thing everywhere and every time I work in even small startups where 10 people. Just use chat every day so the manager can see them that they are chatting instead of going to the other cubicle and speak.
00:40:06 Dan Stelian Roman: And of course, from the business point of view if by implementing artificial intelligence we increase the total cost, we don’t save then from the business perspective it’s a mistake. And as I said, like any new technology, it will create more jobs than it replaces. So that was my presentation. I have to say that this is my view, is not somebody told me it’s a training course and I’m just repeating what I was told. I started looking at agile trying to understand when they started and. Not only artificial intelligence, agile in general, you know as a new way of thinking as something that is not based on patterns is not based on algorithm, is not based on very formal defined processes. And I learned a lot of things in the context. It’s my learning, it’s my view, so don’t take it as something that is the right way or the absolute truth, it’s my view.
00:41:15 Dan Stelian Roman: And I’ll leave Sevcan come to go to the last slides.
00:41:25 Sevcan Yasa: Thank you, Dan that was a very interesting presentation and we do have some interesting comments, but just before we head over to the Q&A, just like to give you all a reminder, this is a list of all the courses that Knowledge Train and agileKRC have. If you are interested in any courses, please do let me know. I am going to pop down my e-mail just in the chat. So, if you’re interested, always give me an e-mail I’ll be more than happy to help. And then right at the end of the Business Analysis course, we have Business Learning Library. This is actually something new that Knowledge Train has. Dan, would you mind going on to the next slide please.
00:42:12 Sevcan Yasa: Thank you. So, as I mentioned, it’s new we have over 200 courses. You have the option to pay monthly or annually, so that’s totally up to you. Just the main course topics are leadership, Project and Change Management, Soft Skills, Health and Well-being, Personal Development, Business Administration and Human Resources. We also do have a demo if you are interested just to try it out. And I think the demo is in Project and Change management if I’m not mistaken. So, you know you can always give the demo a try, if you’re interested, you can always e-mail us or you can always book online. And then I’m going to quickly put up a survey, if you do have any questions, please do put them in the chat of the feedback we will go through them.
00:43:20 Sevcan Yasa: OK, so you have a question.
00:43:30 Sevcan Yasa: OK. So, Dan, the first question, how do you get experienced project managers if all of the non-experienced project managers are no longer needed and so can’t gain experience?
00:43:42 Dan Stelian Roman: Very good question I was writing them that I couldn’t change the slide. So, the problem is not only with AI, it’s was also introduced by COVID when a lot of companies they stop projects they put on hold change and all that stuff. So how do you create new experience project managers when there are no projects and even the experienced project managers are challenged when they find when they try to find work. The answer is not simple, what I recommend, especially to Scrum Masters is to look at what a project manager does to Scrum Masters, team leads and so on. So, roles that have some experience with managing people and have experience with responsibility to deliver something, a product whatever. Have a look at what the project manager does and see if you like that life of stress and a lot of other things. So, somebody said that the PM will become just an oversight role and AI will take a lot of responsibilities. My view is that the other around the AI will help project managers to make decisions, but the decision will still be a human decision. We heard this one even with project manager that Agile scrum will take over and there will be no need for project manager. So, if there is a project manager that project manager will just do stuff and want to interact with the team it doesn’t happen.
00:45:28 Dan Stelian Roman: I’ve seen a lot of organisations that failed with agile, and they go back to worst formal processes than they had before, so I don’t see that the PM will become just the sort of high supervisor. Another question was about the reports, so I’ll be happy that I get the report from the software, and I pass that report. My experience is that if the person that reads the report doesn’t find what they are looking for in that report, there will be problems.
00:46:09 Dan Stelian Roman: As I said, with the risk register, there are things that you can’t put in your steering committee pack. So, what if those things are in the risk register because you keep your risk register for yourself somewhere, but you don’t publish everything, and using the software that stuff will get visible and you will create problems, so it’s same with translation. I’ve seen translation done by people, so they signed the translation. But it’s obvious that it was done with tools like Google, and they basically missed the message of that communication. Translating word for word doesn’t mean that without knowledge, without experience, you’ll understand what that message is supposed to pass.
00:47:03 Dan Stelian Roman: And the other question that I wrote down it’s let’s see what the AI and the graphs that I did will look like in 10 years. I did those graphs for the Scrum master 10 years ago and I looked at what agile was when it started. So, what happened from what people wanted when they created this new concept of agile. As maybe you saw in the presentation artificial intelligence it’s already 70 – 75 years old and all we can see it’s sort of formalised plagiarists in many places so you take bits and pieces from what you can find on the Internet, and you create a new content.
00:48:04 Dan Stelian Roman: I don’t see that AI will be much more advanced in 10 years. There are areas where it helps, like if you have on a vehicle on Mars and that vehicle should identify like a small. What is dangerous, what can be done, where it can go if there is an obstacle, yes, that’s a very good way of using AI in terms of documentation translation. Yes, we can have better drafts than we have now. And maybe there will be less time for the human to validate that information. But taking something from a software generated by software and passing as our creation and being used later, I don’t know. I personally don’t think that in 10 years will be a big difference.
00:49:02 Dan Stelian Roman: Any other questions Sevcan.
00:49:06 Sevcan Yasa: That was the last question, if I’m not mistaken.
00:49:19 Dan Stelian Roman: So, I’ll have a few more minutes allocated, I was hoping for more questions, so if you have more questions, please put them in the chat related to this, what will be in the 10 years. That was the starting of my research, let’s say in agile my passion to see when agile started as a concept. I was sort of surprised that it started the in the US Department of Defence with the project that is called Project Agile. So of course, the world is used for many, many centuries, is not something new, but agile as a product development approach. The first thing that I found was study in the American Department of Defence where they wanted to find a way for the manufacturing industry to produce faster the things that they needed.
00:50:19 Dan Stelian Roman: Those days in 60s – 70s to get the product to market, you needed 3, 4, 5 years from the idea. To the actually manufacturing physically manufacturing the product, and they created the a study group in 90s, the group that came up with the term of agile manufacturing so it’s not 2021 discovery. And what they imagined was not the small team of five to nine people that it’s also like they started with an academic research. They imagined a new way of organisations so breaking down the big organisations and delegating different aspects and maybe some of those things can go to artificial intelligence things like now we already start outsourcing payroll.
00:51:20 Dan Stelian Roman: Some parts of accounting, so things that are very repetitive very clearly defined and they can be done by software. And keep the organisation with the core business, that was the intention of agile. If people don’t know Java was created to integrate this unit, so the language itself was intended initially to let machine speak with machines. So, all these robots that are in production so from that dream vision in 1980s. We are now 34 years later, we just look at we need the Scrum master, we need the certificate, we need the PO, we don’t need project managers. It didn’t happen I can see, at least in Australia, in our market that the need for project managers is now again much more than Scrum Masters.
00:52:26 Dan Stelian Roman: And a lot of jobs that are labelled Scrum master because people want to be agile are in fact project management jobs, things like procurement, financial management are not what the scrum master should do, and they’re not part of the agile tool set the requirements.
00:52:48 Dan Stelian Roman: OK, so that was all for today, thank you very much. I hope that I’ll have another one and yeah, take care and just think twice when you rely too much on artificial intelligence.
00:53:02 Sevcan Yasa: Thank you so much, Dan. If anyone has any more questions, please write them down on the chat. We will wait for a few more minutes. If you do have any questions, you can see we are getting a lot of thank you. So, thank you so much for joining, just to mention that this webinar is recorded so you will all receive the slides, the recording, and a podcast version, most probably next week it will be emailed to you.
00:53:54 Sevcan Yasa: So, I think that’s it from the audience.
00:54:01 Dan Stelian Roman: There is a question that I didn’t answer because the answer is none, so it’s what are the most useful from Steven so AI software tools that I’ve seen in PM. I’ve seen digital assistance, and I don’t know which software was digital assistant for the meetings to give us the minutes. So that’s a good thing and I used in the past, the Monte Carlo simulation that is also some sort of AI, but I tried the things with the schedule generation. Even you know some backlog management tools. I spend more time fixing the output than using it.
00:54:48 Sevcan Yasa: Thank you, probably did miss that question out, so just apologies Steven. Thank you, Dan, for realising. So, if anyone doesn’t have any more questions, I am going to end the session here. So, thank you everyone for joining. Thank you Dan, for attending and hope you all have a good evening.
00:55:16 Sevcan Yasa: Thank you everyone, bye.
agileKRC has helped shape agile thinking by leading the teams that developed AgilePM® and PRINCE2 Agile®. We take a practical, success-oriented approach. We begin by taking the time to listen and understand your needs, before offering our real-world experience and expert guidance.