For too long, product management has been a series of manual tasks. Sorting through data, reading user feedback, translating in-app messaging—the list goes on.
When the widespread adoption of conversational and generative AI shook up historical workflows, product managers found creative ways to simplify processes and add AI capabilities to their products. The dust settled, revealing new ways to build and use AI-powered tools.
Nobody understands a company’s market, users, or product better than its product managers. Because of this, they’re uniquely positioned to impact the company’s strategic outcomes—and many are doing this by building AI-powered tools or weaving AI into existing ones. New tools come with new challenges, and the traditional product management playbook won’t cut it in the age of AI.
To help you build the most impactful AI products, we compiled 20 best practices from internal product managers at Pendo across four strategic areas.
Research and development (R&D) is an integral piece of building new products and improving existing ones. It’s also manual, time-consuming, and expensive. In 2022, R&D performed in the U.S. cost an estimated $885.6 billion—up $84.1 billion from 2021.
To perform more effective and efficient R&D, product managers are using and building AI-powered tools to:
Before building analytics into products became the status quo, product professionals didn’t have enough data to inform decisions. Now, they’re faced with the opposite problem: too much data, not enough information.
Data can only become information with context. Many product managers today use AI to make it easier to understand and act on data, whether we’re analyzing qualitative inputs from review sites to deeply buried usage patterns. Here’s how to analyze large batches of data with AI, for AI products:
Knowing each customer comes with the territory of being a small-scale startup. As a business expands, there’s simply not enough time to maintain personal relationships with each user.
Qualitative data (e.g. from NPS responses and in-app feedback) can be the golden ticket to planning, developing, and refining AI tools. To build better AI products, PMs should:
To standardize, summarize, and find ideas from large volumes of qualitative data, product managers should use customer intelligence tools.
Features are only as successful as their adoption rates. Onboarding, walkthroughs, and resource centers should be part of every new feature and tool you launch—especially for AI/ML tools that introduce new workflows, technologies, and ways of working.
To ensure your AI features are as valued and adopted as possible, product managers should:
If you fail to plan, you’re planning to fail—especially when it comes to sparkly new AI features. What tips and tricks have you learned along the way?
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