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Episode: Applying CPMAI Methodology to AI Projects: Interview with Laetitia Callegari [AI Today Podcast]

Author: AI & Data Today
Duration: 00:11:28

Episode Shownotes

Companies of all sizes across the globe in just about every single industry are looking to see how AI can provide them a competitive edge. They want AI to provide efficiencies and improve ROI in today’s competitive landscape. As a result, this creates tremendous opportunity in the field of AI

for project professionals who are CPMAI certified and follow the CPMAI methodology. Continue reading Applying CPMAI Methodology to AI Projects: Interview with Laetitia Callegari [AI Today Podcast] at Cognilytica.

Full Transcript

00:00:01 Speaker_01
The AI Today podcast, produced by Cognolitica, cuts through the hype and noise to identify what is really happening now in the world of artificial intelligence.

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Learn about emerging AI trends, technologies, and use cases from Cognolitica analysts and guest experts.

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Hello, and welcome to the AI Today podcast. I'm your host, Kathleen Mulch.

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And I'm your host, Ron Schmelzer. And we've been hearing still from many of you, we really appreciate all the feedback that you give us. Sometimes positive, sometimes negative. We can handle the criticism because

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AI is the topic of every conversation today. It's funny because when we started with the AI Today podcast seven years ago, well over 400 episodes ago, it was still an interesting topic, got a lot of press, but it wasn't everybody's topic.

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Now, when like your mom texts you at some time at night asking you about some crazy GPT thing, now you know AI is the topic of every conversation. So, you know, it's part of that.

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One of the things we like to do here at AI Today is really not just tell you about where things are, where things are going with all of our series, but also connect with folks who are in the real world doing real things with AI.

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So you can see that this is not all just conversation, but it is real action and activity.

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Exactly. So if you haven't done so already, make sure to subscribe to AI Today so you can get notified of all of our upcoming episodes. We have a number of different podcast series that we do, but we also interview thought leaders in the space.

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And you know that we are big advocates of CPM-AI, the Cognitive Project Management for AI methodology for how to run and manage AI projects.

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We always like to talk to folks who are CPM-AI certified so that our audience can get their perspectives as well.

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So for today's podcast, we're so excited to have Leticia Caligari, who is Managing Director at PrimeScope Technology Solutions and is also CPM AI certified. So welcome and thanks so much for joining us.

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Thank you, Kathleen and Ron. Thank you very much for having me in the AI Today podcast. I'm really, truly grateful for the opportunity to be here. So my name is Leticia. I hold dual leadership roles as the Managing Directors of

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system implementation at PrimeScope Technology Solutions, but also as a global CEO of SustainAgility Solutions, where the focus is on promoting sustainable practices through the integration of innovative technology solutions.

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I've spent several years, I think around 20 years, implementing software within financial and government industries, mainly focusing on leveraging technology and data analytics. to drive strategic initiatives.

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I've recently joined the board of director at the Project Management Institute, the Sydney chapter, where I oversee the academic outreach portfolio.

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And in addition, I teach computer application in project management at the University of Sydney, and also communication and negotiation. This is mainly aiming to share my knowledge with the next generation of project managers,

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So this background allows me to understand and address the complexities of implementing technologies in a way that aligns with sustainable business practices.

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So my next steps with these companies, with both companies, would be to leverage AI to create solutions that are not only effective, but also responsible and forward thinking.

00:03:31 Speaker_03
That's fantastic. I think a lot of that background is really wonderful because it covers a lot of interesting overlaps in the world here.

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Of course, what's going on with AI, with project management, with the PMI in particular, and people who are certified and who run projects. Now, AI is part of their everyday experience.

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On the one hand, using AI as part of their day-to-day project management activities that may or may not be AI-related. On the other hand,

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involving AI within their projects, whether they're running and managing AI projects or managing projects that have some component of AI.

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So maybe from that perspective, I know you've been involved in various different aspects of this on the data side, the analytics side, and the AI side.

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So maybe you could share with our listeners here some of the challenges that you have seen with running and managing and dealing with data, advanced analytics, and AI projects at all parts of it.

00:04:25 Speaker_00
Yes, for sure. So in my experience, there are several key challenges that consistently arise in data and analytics and AI projects. One of the challenges is ensuring data quality and consistency.

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Data often comes from multiple sources and in various formats, which requires significant effort to clean, integrate and standardize before it can be effectively used.

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and poor data quality can lead to inaccurate insights and undermine the entire project. Another challenge is aligning AI and analytics capabilities with business objectives.

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It's crucial to have a clear understanding of the business problem you're trying to solve and to ensure that the technical solutions are designed to address these specific needs.

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So this alignment requires close collaboration between the technical teams and the business stakeholders to ensure that developed models and analytics truly add value and support strategy goals.

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Another challenge is managing stakeholders' expectations, which is very crucial. AI projects often involve complex and novel technologies that can create unrealistic expectations about the capability and the speed at which they can deliver results.

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So effective and ongoing communication about the project scope, the assumptions, and potential risk is essential to maintaining realistic expectations and secure continued support.

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Another challenge is the ethical considerations and bias in AI models. Ensuring that AI systems are fair, transparent, and accountable requires careful attention to the data used and the algorithms applied.

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Bias in data can obviously lead to discriminatory outcomes. So it's important to implement robust mechanism for identifying and mitigating bias throughout the project lifecycle. The last challenge I will highlight is

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around managing people risks, around skill gaps and the need for continuous learning, just because the rapid pace of advancement in AI and analytics technologies means that teams need to constantly update their skill sets and knowledge.

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So investing in training such as, you know, CPM AI and the development is essential to keep up with the latest tools, techniques, and obviously best practice in the field.

00:06:46 Speaker_02
Yeah, there are so much to unpack in your response. I loved it. Because you're right, you know, I mean, we talk about how data is the heart of AI. And I think we still have data challenges, you know, decades on now from using data.

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And also, you talked about, you know, knowing what problem you're trying to solve. managing the scope of your projects, especially maybe, you know, each iteration, which is incredibly important.

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And a lot of the things that we discuss in CPM AI, the training and certification. So speaking of CPM AI, how has that CPM AI methodology helped grow key skills for yourself? You know, you talked about education is really important.

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So how have you, how has CPM AI methodology helped grow key skills for yourself, including providing the knowledge, the lexicon and the credibility that you need?

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Well, CPM AI methodology has been really instrumental in enhancing my professional skill set, particularly in managing data-centric projects.

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So this is a structured approach that has provided me with a comprehensive framework to effectively plan, execute, and oversee these initiatives, obviously ensuring they align with the organization's goals and deliver measurable value.

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One of the key benefits of the CPM AI methodology is its emphasis on systematic approach to AI project management.

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This methodology breaks down complex projects into manageable phases from initial business understanding and data preparation to model development and deployment.

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And by following these structured phases, I've been able to maintain not only clarity and focus throughout the project lifecycle, but also clear communication, which is crucial for successful outcomes.

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And this methodology, so I took three months to run through the entire course. And this methodology has also enriched my knowledge base with a real robust lexicon of terms and concepts.

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specific to AI and advanced analytics, it has significantly improved my ability to understand and communicate technical details to non-technical stakeholders.

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And clear communication is vital in ensuring that all stakeholders are aligned and that the project goals and progress are understood by everyone involved. The CPM AI methodology has definitely contributed to my professional growth.

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by providing a very structured framework for managing AI and data-driven projects.

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Yeah, that's fantastic. Well, you're singing our song, obviously. We love not only that the methodology is out there to provide a guide and a framework for folks who are running managing projects, but in the day, it's up to people to implement it.

00:09:33 Speaker_03
Something that's a methodology that's just providing guidance, it's nice on a shelf.

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But if it's not actually speeding things up, making projects more reliable, accelerating the pace, and helping people get around problems, getting better buy-in, all this sort of stuff, those are the problems we're trying to solve with AI.

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And I think for our listeners, if you're having it, listen, we have a very popular series called the AI Failure Series, where we go into a lot of the reasons why AI projects commonly fail.

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And it's great, Letitia, you mentioned a lot of the data issues, because, yeah, I mean, AI projects are data projects, as we all know. And

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Bad quality data, bad quantity data, the wrong data, not having access to the data, issues around data security and privacy. All of those problems really can gunk up the works.

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Even not AI projects, just any sort of data project, but for sure with AI, you can't do much without the data. Maybe this brings us a little bit to some point of information.

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And for our listeners, by the way, if you're not familiar with CPMAI, it's the Cognitive Project Management for AI methodology. And if you're interested, we have a free intro course. You can learn more about CPMAI.

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You can go to ai-today.live slash CPMAI and you can learn all about CPMAI there and hopefully join the practitioners who have gone further, gotten trained and certified and can apply CPMAI to their organization. So

00:10:55 Speaker_03
From that perspective, what advice would you have for others as they run their data analytics and AI products and perhaps some lessons learned and gotchas maybe from your own experiences that people may or may not realize as they run these projects?

00:11:11 Speaker_00
Yes, of course. So for those embarking on the area of data and analytics and AI projects, There are several key pieces of advice that can help ensure success. So firstly, always maintain clear focus on business objectives.

00:11:27 Speaker_00
It's crucial to align the technical efforts with the strategy goals of your organisation to ensure that the project delivers real value. and measurable value.

00:11:36 Speaker_00
Define the problem you aim to solve and the outcomes you expect, then work backwards to determine the data and analytics needed to achieve those results. Fostering a collaborating environment is essential. Encourage

00:11:49 Speaker_00
cross functional teams to work together as diverse perspectives often lead to more innovative and effective solutions. This collaboration should extend beyond the project teams to include stakeholders across the organization.

00:12:03 Speaker_00
This is to ensure that their needs and expectations are obviously understood and met. Data governance.

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And ethical consideration should be, I think, a top priority to establish a robust data management practices to ensure data quality, privacy, and security.

00:12:19 Speaker_00
As you mentioned, Ron, be mindful of potential biases in your data and algorithm and create fair and unbiased AI systems. So transparency in how data is used and decisions are made is crucial to building trust with your stakeholders.

00:12:36 Speaker_00
Also be prepared for an iterative process. AI and analytics projects often require continuous refinement and adjustments.

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So, you know, adopting an agile approach to allow flexibility and adaptability as new insight emerge and condition change is also essential. Effective communication is also a critical factor.

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Clearly communicate project goals, the progress, the outcomes to all the stakeholders,

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And this transparency helps manage expectations and ensures everyone is aligned with the project vision and progress, regular updates, open channels for feedback and help address concerns and keep the project on track. And lastly,

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Like every other project, be ready to face challenges and learn from them. So every project comes with its own set of obstacles. So it's essential to view these as learning opportunities.

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So document these lessons learned and best practices to inform future projects and continuously improve your processes.

00:13:42 Speaker_02
Yeah, you know, these are great. And I think it's always important.

00:13:45 Speaker_02
Ron had mentioned earlier that we have a failure series for our podcast that's incredibly popular because folks can learn a lot more from projects failures sometimes than they can learn from successes because you can really see where things went wrong and you say, okay, oh, I could identify that that could be a potential issue at my organization or with my project and what happened, how and exactly why did it fail and then what can you do to overcome it?

00:14:07 Speaker_02
We are big advocates of learning from others. And I think as AI continues to, you know, become more adopted at organizations, especially now with generative AI, there's been a lot of talk about this lately, the need for soft skills comes into play.

00:14:24 Speaker_02
So, you know, collaboration, communication, critical thinking, we actually are in the middle of a podcast series right now on critical thinking for AI and how those skills are needed and why they're needed.

00:14:35 Speaker_02
So when it comes to soft skills, how have you seen them play a critical role when it comes to AI projects?

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Yes.

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So, and especially in the University of Sydney, you know, to create all my, you know, to support the creation of my material for, for uni, but I've, I've actually seen this more used by the students, which is actually also another, another item they've, they've been using, you know, most of the students at the University of Sydney are,

00:15:04 Speaker_00
um, Chinese background with very limited English. So they've, they've been actually using, especially the, uh, the four row, you know, in the, in class.

00:15:14 Speaker_00
So to translate directly from English to, to Chinese, for example, and, and really try to understand it's actually really, really good.

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The thing is they, what has impacted and I would like to actually see more from the, from the students, they creativity and their sense of curiosity as well. So that has really impacted the output of the assignment.

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So I've got a class of 325 students and basically I read the same type of responses throughout the 300 plus assignments. And really what I would like to see is more that curiosity and that creativity out of the students.

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just because they tend to become a little bit lazy because the answers are generated automatically and we're missing out, you know, on what the student really think about the topic. So that is something that I would like to see more.

00:16:15 Speaker_00
And that is really from a university of, you know, the university type of industry, not really, you know, the professional environment, but I would imagine from a professional environment

00:16:28 Speaker_00
There is also, you know, for example, ChargePT has really enhanced or augmented, you know, the level of quality of emails or reports, you know, the way we write business cases as well. This has been really, really good.

00:16:45 Speaker_00
The only thing that would be, you know, we need to be careful is the sources used.

00:16:51 Speaker_00
The, you know, especially, you know, we, we talk about different industries, for example, in healthcare, or for example, in, you know, things that are really important and it would impact people's lives to really be careful on, on the sources of this, of this information coming from, from, you know, the tragedy, for example.

00:17:10 Speaker_03
Yeah, and I'm so glad you mentioned, in the midst of all that, you were talking about curiosity and creativity. Which, of course, these are the soft skills because you can't really learn curiosity from a course.

00:17:24 Speaker_03
I guess you can improve your curiosity, you can't really learn creativity. These are skills that you have to bring as a person. Right. And I think you bring up the great point.

00:17:33 Speaker_03
That's why we are doing this as a series, because we tell people actually how critically important these soft skills are, because that's the one thing the machine can't really do for you. The machine cannot.

00:17:43 Speaker_03
It could give you a response, but it can't be creative. The machine can answer your question, but it can't be curious.

00:17:49 Speaker_03
The machine can provide an answer, but it can't do critical thinking, so it can't tell you if the sources are good or bad, or even if it's hallucinated. Because it would be a chicken and egg thing, right?

00:17:57 Speaker_03
Because if it knew it could hallucinate, if it had critical thinking, it wouldn't have hallucinated.

00:18:03 Speaker_03
So I think what we're seeing firsthand here is, interestingly enough, how as we use more and more AI, it brings to the fore more of these other skills that we might have kind of underrated as their importance in the past.

00:18:19 Speaker_03
So, great you brought that up.

00:18:22 Speaker_02
Thanks. Yeah, really great insights. And it's always nice to hear firsthand how people are using it and how you're observing others using artificial intelligence as well.

00:18:33 Speaker_02
So whenever we have guests on, we always like to wrap up our podcast by asking one final question, and you're able to bring in your own unique backgrounds and experiences and provide such varied responses.

00:18:44 Speaker_02
So no matter how many times I ask this question, we always have our guests you know, answer it in their own unique way. So as a final note, what do you believe the future of AI is in general and its application to organizations and beyond?

00:18:58 Speaker_00
So I think the future of AI is incredibly promising and transformative, and it will, you know, revolutionize various aspects of, you know, both the organization operations and broader societal functions.

00:19:15 Speaker_00
In businesses, I think AI will continue to drive efficiency, innovations, and competitive advantage.

00:19:23 Speaker_00
For example, in the industry that I'm in currently, the insurance industry, AI will streamline risk assessment, accelerate claim processes, and improve customer service by obviously offering more accurate and tailored solutions.

00:19:39 Speaker_00
I'm not working within the healthcare industry, but I can refer to my personal observations. I can enhance diagnostic accuracy, predict disease outbreaks, personalize treatment plans, obviously to ultimately improve patient outcomes.

00:19:54 Speaker_00
But the advancement of AI comes with significant responsibility. So it's crucial to ensure that AI development and deployment are guided by ethical considerations, focusing on transparency, fairness, and inclusivity.

00:20:11 Speaker_00
So addressing issues such as data privacy, we see that throughout LinkedIn posts, Facebook, et cetera, addressing data, these issues such as data privacy, algorithmic bias, and the potential displacement of jobs.

00:20:28 Speaker_00
is essential to harness AI benefits while minimizing its risks. So the future of AI, I think, is very bright with the potential to drive a lot of improvement in various sectors.

00:20:42 Speaker_00
But by integrating AI responsibly and ethically, we can definitely unlock its potential to ensure we enhance business performance and improve quality of life and address global challenges

00:20:56 Speaker_00
you know, to hopefully, you know, contributing to a more equitable world. So, yeah.

00:21:03 Speaker_03
Great. Well, I think Spoken likes true project professional who's in there working with projects, trying to deliver real value.

00:21:10 Speaker_03
So good to hear a lot of aspects of real value and of course, doing so with the guardrails and a lot of things that we like to talk about here on the AI Today podcast.

00:21:18 Speaker_03
So thank you so much, Leticia, you were fantastic sharing all your great insights with our audience and thank you for being with us on the AI Today podcast.

00:21:26 Speaker_00
Thank you so much, Ron. Thank you so much, Kathleen.

00:21:29 Speaker_02
Yeah, thank you. We had such a wonderful discussion today. I know our listeners will enjoy this as well.

00:21:34 Speaker_02
And if you haven't done so already, make sure to subscribe to AI Today so you can get notified of all of our upcoming episodes, including additional wonderful interviews.

00:21:42 Speaker_02
We also have a LinkedIn newsletter that I will link in the show notes, and I encourage you to subscribe to that. We publish weekly and we showcase various thought leadership.

00:21:52 Speaker_02
We also talk about, you know, different topics and different places that Ron and I will be presenting. So definitely Stay subscribed so that you get updated on all of that. Like this episode and want to hear more?

00:22:03 Speaker_02
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00:22:11 Speaker_02
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00:22:24 Speaker_02
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00:22:36 Speaker_02
Check it out at aitoday.live slash list. This sound recording and its contents are copyright by Cognolitica. All rights reserved. Music by Matsu Gravas. As always, thanks for listening to AI Today, and we'll catch you at the next podcast.