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Author: Rick Hightower
This article originally appeared on August 7, 2023 on LinkedIn.
An IBM study shows product managers are early adopters of generative AI. They rank in the top-ten professions that use AI. The report states that 21% of product managers use AI daily. Product Managers are leading the AI charge.
As AI's role in product management increases, product managers must learn how to leverage AI to stay competitive. Product managers using AI differ from their traditional counterparts by leveraging their technical expertise to harness AI's potential in enhancing product management processes.
Let's cover some practical ways to use generative AI in your product management role.
As technology advances, industries find new ways to incorporate AI into their processes, including product management. Companies succeed or fail based on effective product management. Product management is essential to any successful SaaS company or software product company.
The challenge is consolidating real-time product information across numerous tools and sources. Let's explore the struggles and challenges of utilizing various tools and how AI revolutionizes product management to enhance collaboration, decision-making, and overall product quality.
Product managers need to find relevant information to make key decisions. The problem is that the product data might be spread across multiple portals, documents, and systems. This results in lower productivity or poor choices because the required knowledge is not at your fingertips.
Consolidating real-time information across tools like Figma, ServiceNow, Slack, JIRA, GitHub (PRs, releases), CloudWatch (logs, metrics), DataDog, customer metrics, project plans, WIP tracking, business analyst spreadsheets, market data, product data, and customer feedback channels is a daunting task for any product manager. Challenges in finding information can significantly affect product quality and timelines. Then if you do collect all of the data, the next challenge is finding the right product data to make informed decisions.
But having a wealth of knowledge and information is only worth little if you can't find it when needed. It has to be accessible, discoverable, and up-to-date. Having an abundance of information is only valuable with recency. Old data can be as helpful as drinking spoiled milk. Having the data and being unable to find and use it is also useless. Getting data from multiple sources to make informed product decisions is often manual and time-consuming. Correlating data across disparate systems is also tricky.
One solution to these challenges is generative AI. Large language models (LLMs) can be fine-tuned for domain-specific text, allowing for deeper understanding. Semantic search with embeddings offers agility and scalability. Semantic search lets product managers easily access essential information with a simple search. Now it is easy to find that needle in the haystack answer to inform product decisions quickly.
Continuous knowledge management is also crucial for quality insights, ensuring AI can provide relevant insights continually. The data must be collected and groomed. Generative AI is only as good as the contextual information you can give. You must groom the knowledge base for AI and refine and build the knowledge base with AI.
Generative AI tools like ChatGPT, Bard, Perplexity, Poe, and Anthropic, can be used for generation, classification, extraction, and summarization, all of which are essential components of product management. AI can streamline product development, enhance collaboration, and improve decision-making in SaaS and software product companies. All tech companies, really, and these days companies are not using technology for logistics, planning, operations, automation, data analysis, etc.