As businesses continue to expand and evolve, the need for AI products becomes more prevalent. AI Product Managers (AI PMs) ensure the successful development, testing, release, and adoption of AI products. To achieve this, they must clearly understand the AI lifecycle and how it differs from traditional product management.
The responsibilities of an AI PM are vast, including determining the AI product’s core function, audience, and desired use. They must also evaluate and maintain the input data pipelines throughout the AI product’s entire lifecycle. AI PMs must orchestrate cross-functional data engineering, research, data science, machine learning, and software engineering teams. Additionally, they must decide on the key interfaces and designs, including user interface and experience (UI/UX) and feature engineering.
Building an AI solution is identifying the problem that needs solving, which includes defining the metrics that will demonstrate success. AI PMs must work with senior management to design and align appropriate metrics with the business’s goals. With clarity on metrics, it is possible to do meaningful experimentation. A product manager must also consider ethics throughout product development, particularly when defining the problem.
Once the metrics have been defined, AI PMs must run experiments to determine if the AI product can map to those business metrics. Experimentation should occur during three phases of the product lifecycle: the concept phase, the pre-deployment phase, and the post-deployment phase. During the concept phase, evaluate whether an AI product can move an upstream business metric. In the pre-deployment phase, AI PMs must ensure that the core functionality of the AI product does not violate specific metrics thresholds. Finally, in the post-deployment phase, AI PMs must continue monitoring the product’s performance, gathering feedback, and identifying improvement areas.
The AI lifecycle is a continuous building, deploying, and iterating cycle. It requires constant monitoring and evaluation to ensure the AI product meets the business’s goals. AI PMs must work closely with engineering, infrastructure, and site reliability teams to ensure that all shipped features can be supported at scale.
In conclusion, AI Product Managers are vital in bringing AI products to market. They must navigate the complexities of the AI lifecycle, work with cross-functional teams, and ensure that the AI product is aligned with the business’s goals. AI PMs can create ethically responsible AI products by building a group that includes people of different backgrounds who will be affected by the products differently. Through continuous experimentation and evaluation, AI PMs can iterate on the AI product and ensure its success in the market.
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