AI Product Management: From Challenges to Solutions
As our world becomes increasingly digitized, the importance of Artificial Intelligence (AI) products has grown significantly. AI has become integrated into various sectors, including finance, healthcare, and entertainment, revolutionizing our daily lives and work practices. The evolution of AI products has been remarkable, progressing from simple rule-based systems to complex machine learning algorithms and neural networks.
However, with the widespread adoption of AI products comes a new set of challenges that demand careful management and oversight. Ensuring the delivery of high-quality, reliable AI products that meet the needs of end users requires effective product management. In this context, we will explore the crucial role of product managers in delivering good-quality and dependable AI products.
Initially, AI products were limited to academic and research purposes, but with big data and cloud computing, AI products have become more accessible and cost-effective. Today, AI products are being utilized to solve various problems, from detecting fraudulent activities in financial transactions to diagnosing diseases in healthcare. Product managers are critical in developing and delivering quality and reliable AI products, working closely with the development team and other stakeholders to ensure timely and budget-friendly product delivery that meets customer requirements and specifications.
The Role of Product Managers
Product managers play a crucial role in ensuring the development of good-quality and reliable AI products. They are responsible for overseeing the entire product lifecycle, from ideation to launch, and ensuring that the product meets the needs of its end users.
One of the most significant challenges faced by product managers in AI development is the misconception that AI processes are the same as any software development process. However, AI products require specialized knowledge and expertise that is not typically found in traditional software development. Therefore, product managers must work closely with data scientists and AI experts to ensure that the product is based on sound scientific principles and capable of delivering accurate and reliable results.
Another challenge that product managers face is managing stakeholder expectations. Stakeholders often have unrealistic expectations of what AI products can achieve or how quickly they can be developed. It is the product manager’s responsibility to manage these expectations and ensure that stakeholders have a clear understanding of the product’s capabilities and limitations.
Research is an essential component of developing AI products, but it can be complicated and uncertain, making it challenging to manage expectations. The research process’s outcome is often unknown, and it is impossible to predict whether the research will yield positive or negative results. To mitigate these uncertainties, product managers can conduct small-scale tests and experiments before scaling up the product development process.
Tipical AI model flow
The life cycle of a product based on AI or Deep Learning models typically involves several stages, which can be summarized in the following diagram:
- Data Collection and Preparation: The first step in the AI product life cycle is to collect and prepare the data required for model training. This stage involves gathering relevant data from various sources, cleaning and preparing it, and ensuring that it is of high quality and sufficient quantity for model training.
- Model Training: Once the data is collected and prepared, the next stage is to train the AI or Deep Learning model. This stage involves selecting the appropriate algorithm, setting up the training environment, and feeding the prepared data into the model to generate a trained model.
- Model Testing and Validation: After the model is trained, it must be tested and validated to ensure that it is accurate and reliable. This stage involves setting up a test environment, testing the model with a separate dataset, and validating the results against known standards.
- Integration and Deployment: Once the model is tested and validated, it can be integrated into the product and deployed in a production environment. This stage involves integrating the model into the product’s codebase, setting up the infrastructure to support the model, and deploying the product in a production environment.
- Monitoring and Maintenance: After the product is deployed, it must be monitored and maintained to ensure that it continues to function correctly. This stage involves monitoring the product’s performance, addressing any issues that arise, and updating the model and infrastructure as necessary to ensure that it remains accurate and reliable.
The cycle of developing AI products involves two main issues for product management. Firstly, when building products based on AI or deep learning, the results are uncertain. For example, creating an e-commerce platform only requires time, whereas developing an app that identifies the name of a dish from a photo requires accurate and functional models. The success of such a product heavily depends on the accuracy of the trained model. Secondly, due to the uncertainty in the model training process, the iteration cycle of data collection, preparation, model training, testing, and validation makes it challenging to provide accurate delivery dates. How can an AI product manager tackle this situation?
To address these challenges, most AI development companies split the model training process and product development into two different departments. This means investing resources in exploring different models, solutions, or products, similar to how big companies like Meta, Google, or Amazon operate. For example, ChatGPT was created due to the availability of this model. If someone had tried to develop a product like ChatGPT five years ago, they would have continued to adjust their schedule without achieving any results.
To develop AI products effectively, it’s crucial to have a comprehensive understanding of the creative process behind AI or deep learning models. This involves closely collaborating with the team and having a strong grasp of the technology to establish achievable key results and avoid unnecessary iteration due to a lack of knowledge.
For instance, if the current state-of-the-art detection rates for a product are a True Positive Rate (TPR) of 95% and a False Positive Rate (FPR) of up to 0.2%, proposing a TPR of 99% to stakeholders or the technical team would be unrealistic. However, with an understanding of the technology, proposing achievable targets such as a TPR of 92% for an Android app is possible. This approach allows both parties to work with a shared understanding of the technology and realistic expectations.
In situations where there is no existing technology to build upon, conducting a preliminary Proof of Concept (POC) phase is crucial. This involves creating a basic prototype of the AI product or solution to test whether it can deliver the desired functionality. Although this may seem like additional work, it can help to reduce uncertainty in the AI process and enable the establishment of clear requirements and acceptance criteria.
The POC phase can also help to identify potential technical challenges and limitations early on in the development process. This allows for adjustments to be made before significant time and resources are invested in developing the full product or solution. By conducting a POC, developers can gain a better understanding of the capabilities and limitations of the AI technology they are working with. This understanding can help to guide the development process and ensure that the final product meets the needs of end-users.
Conclusion:
AI products represent a significant shift in how we approach product and project management. It is clear that these products have the potential to revolutionize industries and change the way we work. However, it is important to note that AI product development is not a closed process, and there is still much to explore and learn.
As we continue to push the boundaries of what is possible with AI, it is crucial that we remain open-minded and flexible in our approach. We cannot rely on outdated management theories and practices, and we must be willing to adapt to new technologies and processes. Maybe in 5 years, AI products will simply be a part of our everyday products, and we will look back on this time as the beginning of a new era of innovation and progress.
What do you think are the biggest challenges Product Managers face when developing AI products, and how do you think they can overcome them?
