What are the latest AI project management trends and best practices?

AI projects certainly have some special features that distinguish them from conventional software development projects. However, to describe them as generally more complex and less challenging is complete nonsense. In particular, there is no difference when it comes to uncertainty. Anyone who has ever worked in software development knows one thing for sure: requirements change all the time. But even if the comparison with traditional projects is not accurate, we can still assume that we are dealing with complex projects in which AI comes into play.

Según mi experiencia, tener una metodología de trabajo como el ciclo CRISP-ML es vital para que un proyecto de IA se desarrolle de forma correcta. Sin embargo, esto se traduce en que hay incertidumbre en cuanto a los resultados finales y tiempos de un proyecto de IA que muchas veces son una barrera de adopción para empresas mas tradicionales.

For a data sciences project usually, you can use the CRISP DM standard process model. This is specific for data mining and data science tasks and the implementation life cycle that a team would take on. But in some teams, I had seen them using agile frameworks like Kanban as well in their respective projects.

AI project lifecycle involves: Problem definition, Data collection, Preprocessing, Model development, Evaluation, Deployment, Monitoring & Maintenance, Feedback & Iteration - ensuring solutions meet objectives and evolve to address changing needs.

The AI project lifecycle revolves around data and ethics. It starts with meticulous data collection, refining like key recipe ingredients. Models are crafted iteratively, akin to a musician perfecting a performance. Ethics are central, addressing biases for fairness. Deploying AI isn't simple; it involves integration, scalability, and functionality. Diverse teams—data, tech, ethics, business—collaborate for success. Explainability matters; users seek transparency, like a mentor explaining grading. Teamwork, like assembling a puzzle, is vital. Balancing data precision, ethics, and teamwork defines AI projects, setting them apart from traditional software development.

Technical profiles are great but you need more than that. A role that I found often overlooked in a AI Team is the Adoption. Making sure that the tools and processes are really used and followed inside an organization. It requires team empowerment, clear communication and robust project management.

Let's be honest. Of course, AI projects bring in a number of new experts that have been sought in vain in traditional projects up to now. However, this fact is no reason to completely change the way we work together. Anyone who understands agility and implements it consistently is not doing anything wrong.

Key trend is the integration of AI with traditional project management tools, enhancing predictive capabilities and decision making processes. Best practices now often include continuous learning and adaptation, where AI systems are regularly updated to keep pace with evolving data and environments.

The key is to have a multidisciplinary team with individuals possessing expertise in different areas ensures comprehensive coverage of the AI project lifecycle—from conceptualization to development, deployment, and maintenance. Collaboration and effective communication among team members are crucial for successful AI project outcomes. I'm sure that in the nearest future we will see Ethics Specialists in majority of AI project teams.

The domain expert is a key component, and he should keep some distance to the AI and be knowledgeable about the tool at the same time. Very difficult to find persons with these properties, to be taken care of very carefully!

One cute way in AI projects is testing with parallel machines, or in a hackathon mood. Several systems get fed the same data input, and then the differences in the results are looked after.

AI-powered features in tools like Slack and Microsoft Teams can automate scheduling, provide project updates, and facilitate smoother communication among team members.

Personally I use notion, Google is developing but isn’t quite fulfilling my requirements as yet. I’m sure it will develop quite quickly to super cede notion.

AI projects utilize various tools: Machine Learning Libraries: TensorFlow, PyTorch, Scikit-learn for model development. Data Processing: Pandas, NumPy for data manipulation. Deployment Platforms: AWS, Azure, Google Cloud for hosting models. Development Environments: Jupyter Notebooks, IDEs like PyCharm or VS Code. Version Control: Git for managing code versions. Visualization: Matplotlib, Seaborn for data visualization. AutoML Tools: Auto-sklearn, H2O.ai for automated model selection. These tools streamline tasks across the AI project lifecycle, enhancing efficiency and productivity.

Selecting tools for AI project management is akin to an artist choosing their palette – each tool adds a unique shade to the project 🎨💻. The trick lies in matching the tool to the task, whether it's cloud services for data or advanced frameworks for modeling 🌐📈. This toolkit isn't static; it evolves with the project's needs, ensuring each phase from data handling to deployment is as smooth and efficient as a well-oiled machine.

Reliability, reliability, reliability ( and quality assurance)-- ensuring the reliability and consistency of AI systems is crucial, thus it is essential to develop proper QA practices. Assuring results is especially challenging for the simulation products that take end-user inputs. Time and again, models that passed traditional validation techniques, such as Precision, Recall, and F1 Score fail very basic "sniff tests" by the domain expert -- often around edge conditions.

Explainability is one of the most important techniques. In AI it refers to the ability to understand and interpret the decisions made by AI models. It has gained prominence due to the need for transparency and trust.

I would recommend to take a look to DM-CRISP methodology. It provides a good example of how cumbersome and non-linear Data Science methodologies can be. The word "science" means that DS projects involve the use, at least in the initial phases, of the scientific method. Failure and uncertainty are part of the scientific method. Testing hypothesis most of the time leads to an unsuccesful results. We all understand that a virologist researching a new vaccine is not going to succeed in the first attemps. Why do we forget that when tackling a data science challenge?

Do not be afraid to fail. Building on a shaky foundation or trying to over engineer outcomes will not salvage a project. I was on a project that racked up way too many hours and costs due to a data set that would never reflect the outcome the project sponsors wanted to see and highlight to their end users. We tried to convince clients that this was never going to change but they insisted on trying to intermingle data to their advantage. It was a mess.

In our AI projects we address data quality risks by implementing robust data quality checks and continuous monitoring mechanisms to maintain the integrity of the datasets and ensure that AI models are built on reliable and representative data. Data security risks can be addressed by employing encryption techniques, access controls, and secure data handling protocols. Mitigating data bias risks involves diverse data collection and implementing techniques like fairness-aware learning and bias detection algorithms to ensure equitable and unbiased AI models. Fostering transparency in AI decision-making processes is a crucial step in addressing ethical risks and ensuring the responsible deployment of AI technologies.

AI project best practices include: Clear Objectives: Define project goals and success criteria. Data Quality: Ensure high-quality, unbiased data for model training. Cross-Functional Teams: Collaborate with experts in data, domain, and technology. Iterative Development: Use an agile approach for continuous improvement. Explainability: Prioritize models with interpretable results for transparency. Ethical Considerations: Address bias and fairness issues throughout the project. Data Security: Implement robust measures to protect sensitive information. Continuous Monitoring: Regularly assess model performance and address issues promptly. Following these practices enhances the success, reliability, and ethical soundness of AI projects.

In my experience, 3 best practices are: 1) User-Centric Iteration: Continuously gather and incorporate feedback from users, customers, and stakeholders to enhance the product you are delivering and ensure it meets their needs effectively. 2) Communication and Value Assessment: Maintain clear communication with all project participants and regularly measure the project's impact and effectiveness through defined metrics or OKRs/ KPIs / MBOs. 3) Focus on Explainability: Prioritize making the AI's decision-making process transparent and understandable to build trust and improve the reliability and accuracy of the AI system.

Best practices for adopting and implementing Artificial Intelligence(AI) - Assessement of information technology infrastructure - Determining the most important use cases - Understanding the raw data - Training the models - Admit and acknowledge all the wins - Team guidance and cooperation - Measurement and recording the results

Involve stakeholders from various departments to ensure the AI solution is well-rounded and addresses multiple perspectives. This includes collaboration between technical teams, business units, and cybersecurity experts.

Encourage a culture of continuous improvement within AI project management. This involves regularly evaluating processes, methodologies, and technologies used in projects to identify areas for enhancement and adaptation to emerging trends or changes in requirements.

AI projects must align with business processes, ensuring solutions integrate smoothly into existing workflows and add real value. They should be scalable and maintainable, designed for long-term use and adaptability to changing data and contexts. Additionally, a solid crisis management plan is vital for dealing with unforeseen AI system behaviors or failures, including strategies for quick rollback or shutdown to ensure operational stability and safety.

AI projects can encounter technology roadblocks, scalability issues, and ethical challenges. Addressing these requires expertise, proper resource allocation, and a scalable AI strategy that can adapt to real-world applications. Taking AI from theory and strategy into delivery takes a team that truly understands technology, their process and their business in order to capture the value.

The AI project management landscape rapidly evolves, focusing on agility and adaptability. In my experience, incorporating Agile methodologies has proven effective in managing the dynamic nature of AI projects. This approach facilitates flexibility, continuous improvement, and quick adaptation to change. One trend I've found particularly impactful is the emphasis on cross-functional collaboration, integrating diverse expertise from data scientists to domain experts, ensuring comprehensive understanding and innovative solutions. Another best practice is continuous learning and upskilling of the team to stay abreast of the latest AI technologies and methodologies, which is crucial for the success of AI projects in this fast-paced field.

One thing I've found helpful in the realm of AI project management is role of change management seamless AI tool integration. A key step is initiating a pilot program. This allows teams to familiarize themselves with the new technology in a controlled, low-risk environment, identifying potential challenges and opportunities before a full-scale roll-out. It's essential not just to teach teams how to use AI tools, but also to help them understand the value these tools bring, fostering a culture of innovation and adaptability. Regular feedback sessions post-implementation ensure continuous learning and improvement, making AI adoption a journey rather than a one-time event. These steps, pave the way for greater long-term success in AI projects

Embrace AI-driven automation to streamline project workflows and enhance efficiency. Leverage predictive analytics for better project planning and risk management. Implement AI-powered tools for real-time collaboration and communication within teams. Prioritize ethical considerations in AI project management, ensuring fairness and transparency. Stay updated on emerging AI trends to continually adapt and optimize project management strategies.