Decoding the Puzzle of AI Project Management
In the fast-paced world of AI and data projects, choosing the right project management method is like picking the right tool for a space mission – it can make or break the journey.
Globally, experts in Agile, PMP, and CRISP-DM are diving into the deep end of AI, but there’s a twist.
Despite these top-notch methods, AI projects often stumble, with a staggering 70% not crossing the finish line.
This begs a million-dollar question: Why do these tried-and-tested methods seem to trip over AI projects? Let’s unravel this mystery.
Why Traditional Project Management Approaches Struggle with AI
Imagine trying to use a map from the 90s in today’s world – it’s the same dilemma with traditional project management methods in AI. These methods, like the all-rounder PMP, are great for a variety of projects, from building bridges to managing health care systems.
However, when it comes to the tech-savvy world of AI, they’re like trying to fit a square peg in a round hole.
AI projects are a different beast. They require a unique blend of flexibility and precision, something these traditional methods, designed for a broader scope, struggle to provide. It’s like having a Swiss Army knife when you really need a laser cutter.
These methodologies, while impressive in their own right, lack the specific tools needed to tackle the unpredictable and intricate nature of AI projects.
The Limitations of Agile in AI Projects
Agile is like that popular kid in school everyone wants to be friends with — it’s been the go-to method for developing software for a good reason. It’s fast, it’s flexible, and it’s all about getting things done quickly.
But when it comes to AI projects, Agile is a bit like trying to use a sports car for off-road racing — it’s not quite the perfect fit. AI projects aren’t just about speed; they’re about navigating a maze of complex data and algorithms.
Agile, with its focus on quick deliveries, can scratch its head when it comes to the ‘what’ and ‘how’ of AI deliverables. It’s a bit like trying to cook a gourmet meal but only knowing the first half of the recipe.
Understanding CRISP-DM and Its Application in AI
Now, let’s talk about CRISP-DM. Think of it as that classic recipe book that’s been on the kitchen shelf for ages. It’s been around since the dawn of data mining, focusing on a step-by-step approach to discovering and using data.
But here’s the catch: it’s a bit old-school when it comes to the flashy, fast-evolving world of AI. It’s like using a map from the 2000s to navigate today’s city traffic.
CRISP-DM hasn’t quite caught up with the times, especially for the ever-changing and specialized needs of AI projects. So, while it’s got the basics down, it’s a bit like trying to solve a modern puzzle with a tool from the past.
Cognitive Project Management for AI (CPMAI) – A New Approach
Enter CPMAI, the new kid on the block in the world of AI project management. Think of CPMAI as the Swiss Army knife for AI projects – versatile, adaptable, and just what you need in the AI wilderness.
This method cleverly blends the best of Agile’s speed with AI’s complexity. It’s like having a GPS for the AI project journey, guiding you through the twists and turns with a blend of old-school wisdom and new-age smarts.
CPMAI is about being data-first, smart and nimble, adapting as you go, and always keeping an eye on the AI prize.
The CPMAI methodology is a unique approach to managing AI projects. It’s a blend of best practices, AI-specific strategies, and proven project management techniques. This methodology is central to the CPMAI certification, offering a structured and effective way to handle AI projects.
The Six Phases of CPMAI
Like CRISP-DM, CPMAI offers six primary phases:
The six phases of the Cognitive Project Management for AI (CPMAI) methodology are as follows:
- CPMAI Phase I: Business Understanding – This phase involves mapping the business problem to the AI solution. It’s about understanding what the business needs and how AI can help meet those needs.
- CPMAI Phase II: Data Understanding – This phase is focused on getting a hold of the right data to address the problem. It involves identifying, collecting, and understanding the data that will be used for the AI project.
- CPMAI Phase III: Data Preparation – This phase is about getting the data ready for use in a data-centric AI project. It involves cleaning, processing, and organizing data to make it suitable for analysis and model building.
- CPMAI Phase IV: Model Development – In this phase, the focus is on producing an AI solution that addresses the business problem. This involves selecting algorithms, building models, and iteratively improving them based on feedback.
- CPMAI Phase V: Model Evaluation – This phase involves determining whether the AI solution meets real-world and business needs. It’s about assessing the performance of the AI model and ensuring it delivers the expected results.
- CPMAI Phase VI: Model Operationalization – The final phase is about putting the AI solution to use in the real world, and iterating to continue its delivery of value. This involves deploying the model, monitoring its performance, and making continuous improvements.
Each of these phases is iterative and allows for progression backwards or forwards during AI project development, depending on the need and challenges met in the real world. This methodology is designed to be flexible and adaptive, acknowledging the unique challenges and dynamic nature of AI and machine learning projects.
Unlike CRISP-DM, CPMAI fits into the best practices Agile methodology and is explicitly defined as a highly iterative process.
Some Related Questions
- How can AI be used for project management?
- AI can impact AI by assisting with many aspects of the daily documentation and task management drudgery of project management, but also Imagine a super-smart assistant that predicts problems and suggests solutions. That’s AI in project management.
- Is Agile for AI possible?
- Definitely! It’s about tweaking Agile to fit the AI puzzle – a bit of a custom fit. This is where CPMAI comes in – fitting in the gaps to make Agile AI relevant.
- Is AI the future of project management?
- Absolutely, AI is set to be the star of the project management show, bringing in efficiency and insight.
- What is the difference between AI and Agile?
- AI is all about smart technology, while Agile is a methodology. Together, they’re like a dynamic duo.
- What is a CRISP-DM in AI?
- It’s an old but gold framework, needing a bit of an AI twist to stay in the game. This is again where CPMAI comes in, applying AI to CRISP-DM and applying iterative and agile best practices.
- What is crisp in project management?
- It’s more about data mining, less about AI. Like an old tool in a new toolbox.
- What are the 6 phases of the CRISP-DM model?
- Think of it as a six-step dance: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
- Is CRISP-DM used for machine learning?
- Yes, but it’s like using a manual guide in an automated world.
- Is CRISP-DM an iterative model?
- Not as originally envisioned. But you can use it as a way to constantly refine the process.
- Is CRISP-DM Agile?
- Not quite. It’s like a cousin, related but with a different personality.
- How popular is CRISP-DM?
- Once the star of the show, now a bit of a classic hit in the world of data mining. CPMAI is the new belle of the ball!