How to switch from an internal tools PM to an AI PM
Tags: ai, product, pm, career
Published: 2024-01-02 18:32:47

### **Transitioning from Internal Tools PM to AI PM: A Comprehensive Guide**

As Artificial Intelligence (AI) continues to revolutionize industries and reshape the landscape of technology, **the demand for skilled AI Project Managers (AI PMs) is skyrocketing**. This burgeoning field presents a unique opportunity for experienced Internal Tools PMs to elevate their careers and contribute to the forefront of innovation.

However, **successfully transitioning from an Internal Tools PM role to an AI PM role requires a strategic approach and a willingness to adapt to the nuances of AI projects**. To guide you through this transformative journey, we've compiled a comprehensive guide that outlines key strategies and considerations for making this career move effectively.

## 1. **Understanding the AI Landscape**

The AI landscape is a vast and ever-evolving field. It encompasses a wide range of technologies and applications. Many of them are extremely niche. As an AI project manager, **it's crucial to understand this landscape to guide and manage AI projects** from ideation to deployment effectively. This section will help you build that understanding.

![The AI landscape](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/c4vjy0rubsfzb4nns97j.png)

(Image source: [Landscape Foundation](https://landscape.lfai.foundation/)) 

### **Grasping AI Fundamentals**

Immerse yourself in the fundamental principles of AI, including machine learning, neural networks, natural language processing, and other relevant technologies. This foundational knowledge will empower you to communicate effectively with technical teams, comprehend project requirements, and identify potential roadblocks.

- **Machine learning:** Understand the core concepts of supervised, unsupervised, and reinforcement learning, as well as different machine learning algorithms.

- **Neural networks:** Grasp the fundamental architecture of neural networks, including perceptrons, activation functions, and deep learning models.

- **Natural language processing (NLP):** Learn about techniques for natural language understanding and generation, such as text classification, machine translation, and sentiment analysis.

- **Computer vision:** Gain knowledge of image and video processing techniques, including object detection, image segmentation, and facial recognition.

- **Recommender systems:** Understand the principles of recommendation algorithms, such as collaborative filtering and content-based filtering, and how they are used in various applications.

To go deeper, explore open-source AI frameworks and platforms like TensorFlow, Keras, and PyTorch for building AI models. 

**Foundational knowledge of AI helps effectively communicate with technical teams and understand project requirements to build such solutions.**

### **Industry Applications of AI**

AI is being applied across various industries, from healthcare to finance. Understanding how AI is used in different sectors can provide valuable insights into potential project challenges and opportunities.

- **Healthcare:** In medical diagnostics, AI can be used to analyze medical images, such as X-rays and MRIs, to detect anomalies or abnormalities that human doctors may miss.

- **Finance:** AI can be used for fraud detection. It analyzes financial transactions to detect fraudulent activity.

- **Manufacturing:** AI can predict when equipment is likely to fail, allowing for preventive maintenance and avoiding costly downtime.

By understanding how AI is utilized in different sectors, you can anticipate the needs and expectations of stakeholders in diverse AI projects.

**This knowledge aids in anticipating the needs and expectations of stakeholders in diverse AI projects.**

## 2. **Transitioning Skills and Knowledge**

As a seasoned project manager, you've acquired invaluable skills and knowledge that can serve as a powerful foundation for success in the dynamic world of AI project management. This section will explore **how your existing project management skills can be translated and adapted to AI projects' unique challenges**.

![the mondav dev dashboard](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/uxx8w98w3paqfqyw4ors.png)

(Image source: [monday dev](https://monday.com/dev)) 

### **Leveraging Project Management Expertise**

Your experience in project management, encompassing aspects like planning, organization, and stakeholder management, serves as a solid foundation for success in AI projects.

**Translate your expertise in managing timelines, budgets, and team dynamics to the context of AI projects.**

- **Planning:** Adapt project planning methodologies to accommodate the iterative nature of AI development. Break down projects into smaller, manageable phases and establish clear milestones and deliverables for each phase.

- **Organization:** Implement effective project management tools and techniques to organize and streamline development. Utilize tools like Gantt charts, Jira, and Trello to manage tasks, track progress, and foster collaboration among team members.

- **Stakeholder Management:** Proactively engage with stakeholders throughout the AI project lifecycle. Communicate project updates regularly, promptly address concerns, and gather feedback to ensure project objectives are aligned.

**Utilize your experience in managing timelines, budgets, and team dynamics, adapting these skills to the AI context.**

### **Adapting to Agile Methodologies**

AI projects often benefit from Agile methodologies due to their iterative nature. Familiarize yourself with Agile practices and consider how they can be applied to manage AI projects effectively.

- **Scrum:** Divide projects into sprints with short iterations, allowing for rapid feedback and continuous improvement. A more rigid framework than Kanban.

- **Kanban:** Visualize and manage tasks on a Kanban board, efficiently prioritizing and moving work items through the development pipeline. It is more flexible than Scrum and may be better suited to AI's hyper-iterative (and sometimes open-ended) nature.

**Agile frameworks can accommodate the evolving nature of AI projects, allowing for flexibility and continuous improvement.**

## 3. **Navigating AI Project Challenges**

There’s a lot that goes into building AI solutions. To name just a few things, you must consider data management, LLM monitoring (to avoid bias, assure adherence to guardrails, monitor costs, etc.), and ethical considerations.

![futuristic image of a robot](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/8xw5xc571w7wqxokop69.png)

### **Data Management and Quality**

AI models rely heavily on data. Understanding data quality, data collection, and processing challenges is vital. **AI PMs should implement robust data governance practices to ensure data integrity, consistency, and compliance with data privacy regulations**. They should also leverage AIOps platforms to monitor and manage the performance of AI models, flagging potential data quality issues early on.

### **LLM Monitoring**

**[Large Language Models](https://www.cloudflare.com/learning/ai/what-is-large-language-model/) are AI systems that generate human-quality text**. However, LLMs can also perpetuate biases and generate harmful or offensive content. To mitigate these risks, AI PMs should **use LLM bias detection tools** to identify and address potential biases in AI models. 

Additionally, they can **utilize LLM sentiment analysis tools** to assess the emotional tone of text generated by LLMs, ensuring the production of respectful and appropriate content. 

AIPMs can also implement **LLM cost monitoring tools** to track LLMs' usage and resource consumption, optimizing costs and preventing overspending.

### **Ethical AI**

AI project managers play a crucial role in promoting responsible AI development. **Develop and implement [ethical guidelines](https://www.ibm.com/topics/ai-ethics)** for AI projects, outlining the principles that will guide the development and deployment of AI solutions. **Regular ethical audits are also essential** to assess potential risks and implement mitigation strategies.

## **The Role of an AI PM: Mixing Product and AI**

Project management is undergoing a significant transformation, fueled by the rapid advancement of AI. As it becomes increasingly integrated into our products and services, the role of the project manager is also evolving.

**AI project managers** **are a critical new breed of professionals** who oversee the development and deployment of AI-powered solutions. **They translate business needs into actionable AI strategies**, manage AI projects from conception to launch, and ensure that AI is effectively integrated into the overall product or service.

**monday.com, a leading project management platform, is a prime case study for how AI PMs will work in the future.**

![monday ai features](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/vlqx0ak29frecc22zst1.png)

(Image source: [monday AI](https://monday.com/ap/ai)) 

**The AI PMs at monday.com** are leading the way in this emerging field. They have been responsible for developing [monday AI](https://monday.com/ap/ai) and [monday dev](https://monday.com/dev) (powerful new products in their portfolio). monday AI powers the whole ecosystem, while monday dev focused on streamlining product development workflows and project management.  

**Here are some examples of how the AI PMs at monday have used their expertise to build and deploy AI-powered solutions:**

- **Automated Task Generation:** Developed an algorithm that automatically generates task lists based on client briefs and project goals. This saves time and ensures all tasks are accounted for from the start.

- **Intelligent Time Tracking:** Built an AI-powered tool that monitors team members' activity and identifies potential productivity bottlenecks. This information can be used to improve resource allocation and make better-informed decisions about project scheduling.

- **Dynamic Resource Allocation:** Created an AI-powered tool that matches team members with tasks based on their skills and experience. This can help to optimize resource utilization and improve team productivity.

**The work of the AI PMs at monday.com is an example of the work that AI PMs will be doing in the future.** As AI becomes more prevalent in our products and services, AI PMs will be needed to manage and deploy these AI-powered solutions.

## **Bonus Tips for AI Project Managers**

In addition to the comprehensive guidance provided in this blog, **below are some bonus resources for AI project managers to enhance their skills and expertise**. Each of these will be incredibly valuable in your journey to becoming a top AI PM. 

- **Deeplearning.ai:** Provides comprehensive video courses on deep learning, covering topics such as TensorFlow, PyTorch, and natural language processing.

- **Siraj Raval:** Delivers engaging lectures on AI, covering topics such as artificial general intelligence, robotics, and the ethical implications of AI.

- **Towards Data Science:** A popular platform for sharing and discussing data science and AI-related articles and tutorials. 

- **Kaggle:** A platform for data science competitions where AI practitioners can exchange ideas and work on joint projects. Get your hands dirty on some real-life projects to solidify your knowledge!

**The transition from an internal tools PM to an AI PM is exciting and rewarding** but also demands dedication, adaptability, and a willingness to learn. 

By immersing yourself in the AI landscape, expanding your project management toolkit, navigating AI-specific challenges, and continuously enhancing your AI competencies, you can successfully navigate this transition and significantly impact a dynamic and rapidly evolving field. EXCITING!

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