Article

AI for Product Managers: 5 Foundational Concepts & 10 Tool Options

July 01, 2024

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Emily May

The speed of change in the product world is unprecedented, thanks to artificial intelligence. 

Product managers are under pressure to become AI experts for improved job security, team communication, decision-making, risk assessment, and value delivery. 

This article explores the five foundational AI concepts that product managers need to know to lead their product teams effectively and ten tools that help streamline the development process. 

Why Product Managers Should Approach AI With Caution

Before jumping headfirst into AI tools with your product team, Robb Reid, Senior IT Manager at ICAgile, recommends weighing the risks and proceeding with caution. “Security, privacy, and reliability have not been adequately addressed at this juncture of the life cycle of AI. At this stage, any investment into these technologies should only be at the investigatory level as these products come and go at a rate never seen before in the industry,” he said. 

Reid adds that it is possible to build a product with AI assistance when an organization is equipped with a team of data scientists, a data governance framework, and a significant amount of data to draw upon; however, the risks are still substantial. Instead of being “all-in” on off-the-shelf AI solutions, he suggests using and testing these tools experimentally.

5 Foundational AI Concepts for Product Managers

cartoon of a product manager and ai

Product managers considering AI implementation to jumpstart and streamline the development process should first master these five foundational concepts.

1. Understanding AI Algorithms

When you ask ChatGPT a question, the answers aren’t magically pulled out of thin air. Instead, we have AI algorithms to thank, providing the instructions and rules that guide systems like ChatGPT in performing tasks for us.  

According to ChatGPT, the main types of AI algorithms include but are not limited to:

  • Supervised learning algorithms
  • Unsupervised learning algorithms
  • Reinforcement learning algorithms
  • Deep learning algorithms
  • Ensemble learning algorithms
  • Evolutionary algorithms
  • Dimensionality reduction algorithms

    • Product managers with a working knowledge of AI algorithms are empowered to make informed decisions that align with business strategy and the particular task at hand. Additionally, understanding the limitations of AI algorithms is critical for quality control and monitoring for biases and inaccurate results. 

    2. Neural Networks: AIs Developing Brain

    As humans, our brains can create new neural pathways when we engage in new experiences or learn new skills, a process called neuroplasticity. 

    In the same way, AI also learns and recognizes new connections as it performs tasks for humans and calls upon its training data. AI’s neural networks enable handling complex tasks like image and speech recognition.

    Neural networks are the crux of many advanced AI tools, like LLM applications or large language models. With this knowledge, product managers can effectively communicate how these tools work within their teams and even oversee the development of AI products as AI product manager roles continue to rise. 

    3. Natural Language Processing or NLP

    cartoon of person talking to ai

    Natural language processing, or NLP, enables machines to comprehend, interpret, and produce human language. 

    For example, many of us are familiar with online chatbots, often the first line of communication in customer service interactions. The bots can make sense of our questions or concerns using NLP to provide an answer or filter the interaction to the correct department.

    Familiarity with natural language processing is crucial, as many product managers will likely consider programs to create more customer-centric experiences for their users, directly contributing to positive customer sentiment. This branch of AI can also help product teams assess the implications of artificial intelligence on the organization’s business strategy.

    4. Machine Learning

    Machine learning refers to the training of AI through data sets so that the models can make independent predictions and decisions–a standard training method for many AI applications. 

    One critical element of machine learning is selecting the appropriate learning models for training. AI models can only be as effective and accurate as the data it has learned. Therefore, the limitations of machine learning models are still vast.

    AI is imperfect, and product managers will likely encounter inaccuracies and biases in developing their own AI products or in the AI programs the product team uses to optimize their workflows. An awareness of potential weaknesses in machine learning can help product managers keep a more watchful eye and prevent potential issues from arising. 

    5. Image & Video Analysis Via Computer Vision

    Computer vision, the technology used in facial recognition systems, allows machines to interpret visual data and make decisions, similar to what we see in sci-fi movies.

    As with all artificial intelligence programs, there are real limitation risks, as it is possible to trick this technology with visual illusions or lighting. 

    Product managers should understand computer vision's benefits and limitations before integrating this image and video interpretation technology into their product or team’s workflow.

    10 AI Tools for Product Managers

    graphic of an ai brain

    AI tools are evolving quickly, and a product manager must stay informed about new features and applications so their team doesn’t fall behind.

    1. ChatGPT

    ChatGPT is a significantly popular AI language model, but it is still relevant to include in this list due to its wide range of uses for product teams (and it’s free!). 

    Use it for brainstorming, market research, writing documentation and user stories, automated customer support responses, feedback analysis, and more.

    2. KOMO Search

    KOMO Search is an AI-powered search engine that was trained “using a diverse range of data sources, including public datasets, web crawls, human labels and generated data.”

    Use it for market research, competitor analysis, and industry insights.

    3. Seenapse

    The Seenapse team describes the brainstorming tool as giving “creative people hundreds of divergent ideas in minutes.” The tool's premise is to save time and money during the ideation process so that individuals and teams can focus on implementing ideas. 

    Use it for productive brainstorming sessions and unique product ideas.

    4. Kraftful

    cartoon of kraftful ai

    The Kraftful platform is self-proclaimed as “the ultimate copilot for user feedback.” This AI tool offers product managers and their teams a way to gain insight into customer sentiments by analyzing feedback like reviews, surveys, calls, and support tickets. 

    Use it for actionable customer insights, survey generation, and user experience. 

    5. Monterey AI

    Monterey AI analyzes large datasets, such as calls, emails, chats, and more, to help product teams better understand their customers and collect insights. 

    Use it for actionable customer insights, data-driven decision-making, and user experience. 

    6. Delibr

    Delibr is a tool for product managers and their teams to “become better at collaborating in their feature refinement process” through AI-assisted writing and planning. 

    Use it for product specifications, user stories, and documentation.

    7. ChatPRD

    ChatPRD describes the tool as “an on-demand Chief Product Officer that writes & improves your PRDs.” AI features include the ability to draft full requirements documents and brainstorm improvements.

    Use it for requirements documentation, brainstorming target metrics, and AI-assisted feedback. 

    8. Figma Wireframe Designer

    cartoon of a person making figma wireframes

    The Figma Wireframe Designer harnesses the power of AI to generate wireframes “with a single click.” Product teams can use the tool to create and share prototypes for a speedy design process. 

    Use it for wireframes, prototypes, and product design. 

    9. WireGen.ai

    WireGen.ai, a Figma plug-in, generates “UI wireframes in seconds, based on your provided topic or description.” Rather than spending time and money on one wireframe, product teams can create multiple prototypes to compare and test. 

    Use it for wireframes, prototypes, and product design. 

    10. Uizard

    The Uizard platform is used by “disruptive product teams” that need to turn their sketches and mockups into modifiable digital designs to speed up the product development process. 

    Use it for modifiable prototypes and scanning sketches into digital wireframes. 

    Conclusion

    Mastering the evolving landscape of AI is an undertaking, but approaching the topic with a beginner’s knowledge creates a foundation for product managers to build upon with their teams.

    To further explore the concepts in this article, consider our Foundations of AI certification. This course dives into the history and evolution of AI and teaches you how to implement artificial intelligence within your product team while practicing an agile mindset. 

    We hope to meet you soon in one of our Foundations of AI courses!

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    TAGGED AS:
    Learning Excellence, Leading Change, Foundations, Foundations of AI

    About the author

    Emily May | ICAgile, Marketing Specialist
    Emily May is a Marketing Specialist at ICAgile, where she helps educate learners on their agile journey through content. With an eclectic background in communications supporting small business marketing efforts, she hopes to inspire readers to initiate more empathy, productivity, and creativity in the workplace for improved internal and external outcomes.

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