What can you learn from AI project successes and failures?

Understanding AI projects is more than just knowing technical details; it's about embracing a learning culture. When projects succeed or fail, they provide a chance to grow and adapt. By studying these outcomes, teams can develop a mindset that encourages creative problem-solving, collaboration, and resilience. This attitude, distinct from traditional project approaches, may prove essential in navigating the complexities and uncertainties unique to AI, leading to more innovative and effective solutions in the future.

Every AI Project that actually involve experimentation on a custom dataset is actually a research. Unlike the academic research, it will be considered as a directed research. Planning, management and the execution of a directed research is a more constrained than the academic counterpart. Planning, management and the execution are artifacts of the research culture. Ignoring the research culture will eventually either make things be delayed and/or exhaust data scientists in the managerial chaos with fading hopes of the product. I believe this is one of the most overlooked things. Thus, research projects cannot be managed like agile software projects and should be planned w.r.t. organizational goals.

From my experience, AI projects unveil layers of complexity not seen in traditional software projects. When diving into data, it’s like sifting through an archaeological site; you're never quite sure what you'll find. Some patterns may reveal themselves immediately, while others might take several iterations. A successful AI project has taught me the value of perseverance and iterative learning, while failures have often underscored the importance of setting right expectations. Engaging with AI isn't about securing a definite outcome from the start, but about fostering a culture of continuous learning and adaptation.

All AI projects will fail or seriously fall short of expectations unless there is an innovative and motivated employee culture.

Learning to work on AI projects helps to develop programming, data analysis, machine learning and problem-solving skills. Developing AI projects help in addressing real-world problems by leveraging advanced algorithms and data analysis, positively impacting the lives of individuals. As the core of AI project lies in experimentation, detailed analysis of specific AI/ML algorithm and choice of model provides better outcomes. As AI projects involve iterative development cycles, embracing a culture of continuous improvement through perpetual learning results in effective solution. As AI is rapidly evolving field, engaging in it allows us to stay updated on latest trends, technologies and best practices which helps to build effective model.

One important area that may take time to learn from AI project successes is understanding the context and environment in which a project was successful. Success in one scenario does not guarantee success in another.

Just as AI models improve through training data, we too can refine our strategies by studying past project successes. A practical example might be how Spotify's Discover Weekly curates songs. It's not just about matching listener preferences but also constantly fine-tuning its algorithm based on feedback.

It's helpful to learn from an AI project's success by drilling into the particular stage of the project. All successful projects that I have managed included a strong focus on data rationalization and validation that set the stage for the models to create meaningful outputs. The more upstream successes you can create in the lifecycle of an AI project, the better the chance of the entire project going live in Production.

The success of AI projects should be looked at through the lens of value chain analysis. To prep a POC, it is essential to examine how accuracy, cost reduction, and increased productivity have contributed to the success, what algorithmic insights have driven the improvements, and what compliances were essential. These will form the basis for building customer trust, exploring partnerships, and attracting investment. By learning the elements of success and adjustments one can refine procurement, production, distribution, and customer service to increase businesses efficiency and reduce costs.

Success stories in AI provide valuable insights into effective product management and technology application. For instance, Tesla’s Autopilot system's evolution showcases how continuous data collection and iterative improvements can enhance AI functionality in real-world environments. This emphasizes the value of an agile approach and the importance of aligning AI projects with real-time user feedback and evolving datasets.

Another important aspect of AI project failure to consider is the emotional and organizational impact of failure. Failure in an AI project can lead to losing confidence among team members, stakeholders, and customers. Building a culture that tolerates and embraces failure as a learning opportunity can mitigate this risk. Facilitating open and non-judgmental reviews, creating support systems for stakeholders, and setting expectations that failure is a natural part of innovation can turn a potentially harmful experience into a positive growth opportunity.

Examine the key issues such as quantity and quality of data which provides the model, the ability to learn properly. Invest your time in Exploratory Data Analysis and data processing techniques, so as to understand patterns and figure out potential challenges such as missing data, outliers and feature scaling before building machine learning models. Concentrate on the proper model selection as according to business objective and feature selection based on most relevant features for prediction. Work on reducing the risk of overfitting and underfitting, so that model generalizes well to test data and foster accurate prediction. Cultivate mindset of perpetual learning and fine-tune your model based on continuous feedback and evaluation.

Agile and iterative way of approaching the problem is a key learning from AI project. You start with a problem and when you iterate it keeps improving your solution accuracy/ approach towards problem

AI-driven leadership embraces failures as stepping stones to innovation and success. Examine the fundamental reasons for issues, such as compromised data quality, lack of alignment with business goals, or insufficient risk management. Evaluate teamwork, digital capital, communication practices, and market conditions. Enhance tactics by incorporating these observations. Openly disseminate insights throughout the entire organization to avoid similar mishaps in the future and facilitate ongoing enhancements.

AI project failures can provide vital lessons, especially when they emphasize that not all problems require complex AI solutions. Overestimating AI capabilities and underestimating problem complexity is a common pitfall. It's crucial to define the problem with clarity, choose the right tools, and diligently prepare data for effective AI deployment. On the subject of hype, it's equally important to manage stakeholder expectations and stress that AI is a tool, not a silver bullet that can magically solve all problems.

While the strategies outlined for applying lessons learned from AI projects are essential, they can be enhanced by taking a more holistic and long-term view. This includes focusing on individual projects and integrating lessons learned into an organization's core strategy, culture, and values. Developing an organization-wide repository that records successes and failures and lessons learned can be valuable for future projects. Promoting a culture of continuous learning and reflection can foster innovation and resilience.

Conducting retrospective is a key step to list and reinforce learning from AI Project. This can give you inputs to modify and customize the lessons to fit your specific problem, data, and constraints.

In the realm of AI projects where the core lies in experimentation, failures help in refining strategies and better outcomes. Conduct a thorough review of the built AI project and identify what worked well, what didn't and any unexpected challenges. Create a detailed documentation, which includes information on data challenges, model performance, algorithm choices, and any other relevant aspects. This documentation serves as a valuable resource for future projects. Establish a feedback loop and collect feedback from team members, end-users to gain diverse perspectives which fosters the culture of continuous improvement. Stay updated with the latest advancements and methodologies in AI which is essential for building an effective model.

AI projects yield valuable knowledge and can be effectively applied by following a well-planned strategy. It involves improving processes that rely on data, expanding our understanding of algorithms, establishing trust and partnerships, refining operations, and embracing failure as an avenue for innovative breakthroughs. By addressing the underlying causes of issues, empowering teams, and widely sharing insights for continuous improvements, we ensure ongoing success. It is crucial to implement feedback loops and iterate consistently for sustained achievements.

Actually I disagree with the idea that project failures can be disappointing, frustrating and costly. Successes can also be costly, frustrating, and, at some levels, disappointing... A failure, in essence, is just an option or strategy that didn't pan out. In the context of AI projects, where experimentation is at the core of the project, understanding these failures is crucial. Failures help us to conduct better risk management assessments, to devise more robust contingency plans and force us to develop better monitoring mechanisms. Failures make the AI process management stronger and more resilient. In my view, we need failures in order to achieve more successes.

Also apart from the resources mentioned above: 1) Consider engaging in online communities and forums like Reddit or Stack Overflow 2) Participate in AI hackathons and competitions for hands-on experience 3) Explore university courses and research papers 4) Experiment with AI-powered project management tools branching into related fields 5) Explore consulting or freelance opportunities IMPORTANT: Remaining curious and adaptable is essential in the ever-evolving AI landscape.

I also see hands-on experience as an irreplaceable aspect that is often overlooked. Engaging in real-world projects, even small or experimental ones, can provide insights that traditional learning methods may miss. Working with interdisciplinary teams and individuals from different backgrounds and industries can expose you to different approaches and ways of thinking. Similarly, participating in hackathons or open-source projects related to AI can enhance your practical skills and expand your network.

Diving Deeper into AI for Finance: A Book Recommendation For those particularly interested in applying AI to the world of finance, a deep dive into the specific challenges and intricacies of this domain is crucial. One standout resource in this area is the book "Advances in Financial Machine Learning" by Marcos Lopez de Prado.

10 Key takeaways to learn from AI projects: 1. Data quality is crucial for accurate predictions and fairness. 2. Ethical considerations are vital to avoid biases and harm. 3. Continuous monitoring and evaluation ensure performance and adherence to standards. 4. Interpretability and explainability enhance trust and understanding. 5. Collaboration across disciplines leads to comprehensive solutions. 6. Human-centric design improves usability and effectiveness. 7. Robustness and security safeguards against adversarial attacks. 8. Data privacy issues must be addressed to protect individuals' sensitive information. 9. More investment needs to go to knowledge transfer and build human capital. 10. More quality data lead to more stable performance

From AI project successes, we can learn the importance of clear problem definition, adequate data quality, and iterative development. Successful projects often have well-defined goals and utilize suitable data for training and testing. Learning from AI project failures emphasizes the need for managing expectations, addressing ethical concerns, and avoiding overreliance on black-box models. Failures underscore the significance of transparency, interpretability, and continuous monitoring to ensure models behave as intended and deliver real value.

What is missing in most AI projects is a project leader versed in the mindset and language of all stakeholders. While everybody may speak English they may not understand each other. Compare it to the car salesperson at a dealership. Is that the person who knows best the car? No, the engineers do but they are not at the showroom. The sales person is someone who can read the client - prefer to talk about horsepower or the type of leather seats? Who can figure out what is important to the client and has enough deep knowledge about the car to answer all the key questions. The same for AI projects: if you don't have that project leader who can "translate" between stakeholders then your AI project is at risk.

Fixed Price Projects are like well-mapped roads: clear scope, defined costs, and a set destination. 🛣️ AI Projects, on the other hand, are like exploring uncharted territories: dynamic, evolving, and requiring continuous learning and adaptation. 🌐 While fixed price offers predictability, AI thrives on innovation and flexibility. And its success is more often than not, based on a previous failures.

From AI project successes and failures, you can learn valuable lessons for future endeavors. Successes can highlight effective strategies, like well-defined goals, proper data preparation, and iterative development. Failures can teach you about the importance of managing expectations, addressing ethical concerns, and understanding limitations. Analyzing both successes and failures helps refine project planning, team collaboration, and resource allocation in the AI domain.