An Introductory Guide to GenAI for Product Managers

Generative AI has moved from buzzword to baseline skill for product managers. Here’s how PMs are using AI tools in 2026, the productivity payoffs, and how to become an AI-literate product manager.

Generative AI is no longer an emerging trend – it’s embedded in how modern product teams work. In 2026, 91% of businesses use AI in at least one capacity, up from 78% in 2024 and 55% in 2023. For product managers specifically, adoption has been even faster: a 2025 Productboard survey found that 94% of enterprise product professionals rely on AI tools daily.

The productivity payoffs are real and measurable:

• Workers using GenAI save an average of 5.4% of their work hours weekly. Product managers report saving an average of four hours per task on functions like drafting PRDs, competitive analysis, and building roadmaps.
• Businesses report an average 24.69% increase in productivity and 15.7% in cost savings from AI adoption.
• Chief AI Officer roles are now present in 61% of enterprises – a signal that AI strategy sits at the leadership table.

The question for product managers is no longer “should I learn this?” but “how do I use this effectively?” To help you answer that, the King’s Product Management Career Accelerator now features a dedicated AI and Product Management Learning Track.

Become an AI-literate product manager

To give you a glimpse into the series that learners get full access to, you can watch the first Masterclass hosted by AI expert Alexander Cohen. You’ll learn about the foundations of AI and LLMs and the implications of AI on product management.

When Alex presented this Masterclass, he predicted a fundamental shift in how products get built. That prediction has proven remarkably accurate:

“Within five years, AI is going to make a fundamental change in the way that we produce products. One of those fundamental changes is that AI will in many respects replace human engineers. As such, we’ll get to a place where product managers are going to be responsible for defining what goes into a product and what it should do, and then an AI will execute on that.”– Alexander Cohen

In 2026, this is already happening. AI-assisted coding (“vibe coding”) has made building software dramatically cheaper and faster. The most expensive thing a team can do now is build the wrong thing, which puts the product manager squarely at the centre of the business.

Cheat sheet: An intro to GenAI

Following the Masterclass, Alex has created a guide to AI that you can refer to on the job and with your colleagues to help you understand what AI is, why it’s important, and how to use it in your product management career.

Tokens

What it is

Tokens are the fragments of text that AI models use to analyse and generate language. For example, the sentence “Artificial intelligence is fascinating” might be broken down into tokens like [“Artificial”, “intelligence”, “is”, “fascinating”].

Why it’s important

• The way text is tokenised influences the model’s ability to understand and respond accurately. Proper tokenisation ensures the model captures the nuances of language, which is vital for applications like chatbots, translation services, and content creation.
• For product managers, understanding tokens helps in designing AI applications that require precise language processing, ensuring that the outputs are relevant and coherent.
• Token limits have expanded dramatically since 2024. GPT-4o now supports 128k token context windows. Claude supports 200k tokens. Google’s Gemini 1.5 Pro supports up to 1 million tokens. This changes what’s possible for PMs working with long documents, product specs, and research.

Try it yourself

• Use OpenAI’s Tokeniser tool to understand how a piece of text is tokenised: platform.openai.com/tokenizer
• Explore multi-model token counting with GPT for Work’s tokeniser (supports GPT, Claude, Gemini, and Grok): gptforwork.com/tools/tokenizer

Context windows

What it is

A context window is the span of text an AI model can consider at once. In 2024, a typical context window was 4,000–8,000 tokens. In 2026, leading models have expanded this dramatically:

• GPT-4o: 128,000 tokens
• Claude (Anthropic): 200,000 tokens
• Gemini 1.5 Pro (Google): up to 1,000,000 tokens

To put this in perspective, 200,000 tokens is roughly equivalent to a 500-page book. You can now feed an entire product specification, user research report, or competitor analysis into an LLM and get structured output in a single pass. This was impossible two years ago.

Why it’s important

• Larger context windows are crucial for maintaining coherence in long-form work. For product managers, this means you can use AI for tasks like summarising extensive user research, analysing full product backlogs, reviewing entire codebases, or processing hours of meeting transcripts – all without losing context.
• When selecting AI tools for your workflow, understanding context window size helps you choose the right model for the task. A quick competitive question might only need a small window, but reviewing a 60-page product brief requires a large one.

The implications of AI on product management

AI isn’t just playing a supporting role in product management, it’s reshaping the discipline. Over 73% of product managers now use at least one AI-powered tool in their daily workflow, nearly double the 45% adoption rate in 2024. For PMs, AI won’t take away your job, but it is fundamentally redefining what’s achievable in your work.

Use AI for market insights and opportunities

• AI-driven analytics provide deep insights into market trends and customer behaviours, enabling data-informed decision-making. This helps in crafting precise value propositions and aligning product vision with market needs.
• Predictive analytics and machine learning models assist in anticipating customer needs and identifying opportunities for innovation, enhancing strategic planning and competitive positioning.

Use AI for routine and repetitive tasks

• AI automates routine tasks like scoping, documentation, and backlog management, improving efficiency and accuracy. Natural language processing (NLP) tools can generate detailed reports and documentation based on minimal input.
• With AI handling repetitive tasks, product managers can focus on their softer skills, such as effective communication, stakeholder management, and team leadership, fostering better collaboration and aligning teams towards common goals.

“As AI starts to take over the heavy lifting of how products get built, we’re going to start to see this onus coming back to product managers on what actually goes into a product.”– Alexander Cohen

AI tools product managers are using in 2026

The AI tool landscape for product managers has matured significantly. Here are the categories and tools PMs are using most in 2026:

Category	Tools	What it does
PRD and documentation	ChatPRD, Notion AI, ChatGPT	PMs report saving 4+ hours per PRD by using AI to draft, structure, and refine product requirements documents.
User research and feedback analysis	Zeda.io, Dovetail, BuildBetter	AI-powered tagging, categorising, and synthesising qualitative user feedback. Turns hours of interview transcripts into structured insights in minutes.
Roadmapping and prioritisation	Productboard AI, Airfocus, Aha!	AI-assisted feature scoring, opportunity assessment, and roadmap generation based on data rather than gut feel.
Competitive intelligence	Crayon, Klue	Automated competitor tracking, pricing analysis, and market movement alerts.
Analytics and data querying	Julius, Amplitude AI	Natural language querying of product data. Ask a question in plain English, get a chart or insight back.

The key takeaway: AI isn’t a single tool you adopt. It’s a layer across your entire workflow. The most effective PMs in 2026 use AI at every stage of the product lifecycle – from discovery and research through to delivery and measurement.

The rise of AI agents and what it means for PMs

The biggest change since this article was first published in 2024 is the rise of agentic AI – AI systems that don’t just respond to prompts but take autonomous, multi-step actions to complete tasks.

The numbers tell the story:

• Gartner projects that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from less than 5% in 2025.
• 80% of enterprises deploying AI agents report measurable economic benefits, including increased throughput, lower operational costs, and faster product release cycles.
• A February 2026 Harvard Business Review article argued that product management skills are now central to successful AI deployment across organisations.

For product managers, this creates a new set of responsibilities:

• Defining what AI agents should and shouldn’t do (guardrails and scope)
• Designing human-in-the-loop controls for high-stakes decisions
• Managing the behaviour of AI features within your product
• Understanding how agentic workflows change user expectations

If you’re building products in 2026, you’re almost certainly building products that include AI agents. Understanding how they work isn’t optional – it’s core to the PM role.

From coordination to strategy

AI-assisted coding tools like Cursor and GitHub Copilot have dramatically reduced the cost of building software. When it’s cheap to build, the most expensive thing you can do is build the wrong thing.

This puts the PM at the centre of the business. The role is shifting from coordination (“managing the backlog, running standups, writing tickets”) to strategy (“determining what to build, for whom, and why it’s important”). AI handles more of the execution and PMs focus on customer understanding, strategic judgement, and ensuring the right outcomes.

“We’re going to see that subject matter expertise, that domain knowledge, that real-world understanding of how things actually happen for real people, that’s going to be incredibly valuable and a real, great differentiator.”– Alexander Cohen

What is an AI product manager?

As AI becomes embedded in more products, a distinct role is emerging: the AI product manager. While every PM now needs AI literacy, an AI PM specialises in building and managing products where AI is the core capability – not just a feature.

How an AI PM differs from a traditional PM

• A traditional PM defines features and user flows. An AI PM also manages model behaviour, data quality, and output reliability.
• A traditional PM writes acceptance criteria. An AI PM also defines what “good” looks like for probabilistic outputs that may vary each time.
• A traditional PM manages a development roadmap. An AI PM also manages model retraining cycles, bias monitoring, and ethical considerations.

Skills unique to AI product managers

• Understanding how LLMs work (tokens, context windows, fine-tuning, RAG)
• Prompt engineering and evaluation
• Data quality assessment and pipeline awareness
• AI ethics: bias, fairness, transparency, and explainability
• Managing AI agent behaviour and guardrails

The career path

Traditional PM → AI-literate PM (uses AI tools in daily work) → AI Product Manager (builds AI-powered products).

The King’s Product Management Career Accelerator, with its dedicated AI Learning Track, is designed to help you take that first step from traditional PM to AI-literate PM.

Get hands-on with the concepts

Train a neural network: TensorFlow Playground

TensorFlow Playground is an interactive web-based tool designed to help users understand the basics of neural networks. It provides a visual and hands-on way to experiment with different neural network architectures and parameters.

Features

• Interactive visualisation: Allows users to see how neural networks learn and adjust weights in real-time.
• Adjustable parameters: Users can modify parameters such as learning rate, number of neurons, and activation functions to observe their impact on the model’s performance.
• Educational tool: Ideal for beginners to get a grasp of fundamental concepts in machine learning and neural networks without requiring any programming knowledge.

Why it’s important

Understanding how neural networks work at a basic level helps product managers and developers make informed decisions about integrating AI into their products. It demystifies the learning process of AI models and provides a foundation for more advanced AI concepts.

Try it yourself

Play around with TensorFlow Playground right now.

Run a custom LLM model: Ollama

Ollama is a platform that allows users to run open-source AI models locally on their own hardware. Since this article was first published, Ollama has grown to support a much wider range of models – including Llama 3, Mistral, Phi-3, Gemma, and dozens of others – making it the go-to tool for running local LLMs.

Features

• Local model deployment: Users can deploy and run AI models directly on their machines, ensuring data privacy and security.
• Support for a wide range of models: Compatible with the latest open-source models, enabling users to choose the best fit for their needs.
• Customisability: Users can experiment with different model configurations and parameters to tailor the AI’s performance to specific tasks.

Why it’s important

Running AI models locally with Ollama empowers product managers and developers to have full control over their AI applications. It enhances data privacy, reduces dependency on cloud services, and allows for more experimentation and customisation of AI models. In 2026, with enterprise data sensitivity at an all-time high, local deployment is increasingly attractive.

Try it yourself

Download Ollama here to create and customise your own LLM.

Talk to an LLM with emotions: Hume.ai

Hume.ai is a platform focused on creating empathic voice interfaces that can understand and respond to human emotions. Since the original article was published, Hume has released EVI 2 and EVI 3 – their latest voice-to-voice foundation model that responds in under 300 milliseconds with advanced personality mimicry.

Features

• Emotion recognition: Uses AI to detect and interpret a wide range of human emotions from voice inputs, analysing vocal modulations including tune, rhythm, and timbre.
• Empathic responses: Generates responses that are emotionally appropriate, enhancing user engagement and satisfaction.
• Ultra-low latency: EVI 3 responds faster than most humans do in real conversation.
• Full SDK support: Developers can integrate EVI into applications with just a few lines of code (React, TypeScript, Python, .NET, Swift, and more).

Why it’s important

Understanding and responding to human emotions is crucial for creating engaging and effective AI-driven interactions. Hume.ai enables product managers to develop more intuitive and empathetic user interfaces, improving customer experiences in areas like customer service, virtual assistants, healthcare, and interactive entertainment.

Try it yourself

Start a conversation with Hume’s Empathic Voice Interface.

Draft a product requirements document: ChatPRD

ChatPRD acts as an on-demand Chief Product Officer, helping product managers draft detailed product requirements documents that meet high-quality standards. It’s one of the most popular PM-specific AI tools in 2026.

Features

• PRD generation: Produce structured, detailed PRDs from a brief description or conversation.
• Strategy coaching: Get AI-powered feedback on your product thinking, assumptions, and priorities.
• Template flexibility: Works with your existing PRD structure or suggests best-practice formats.

Why it’s important

Writing PRDs is one of the most time-consuming parts of a PM’s job. ChatPRD reduces drafting time significantly while maintaining quality, freeing you to spend more time on strategy, research, and stakeholder alignment.

Try it yourself

Try ChatPRD.

Analyse long documents and research: Claude

Claude by Anthropic is a large language model with a 200,000-token context window – one of the largest available. This makes it exceptionally useful for product managers who need to process, analyse, and synthesise large volumes of information.

Features

• Extended context: Process entire product specs, user research reports, or competitive analyses in a single pass.
• Structured output: Ask Claude to summarise, extract themes, identify risks, or create action items from lengthy documents.
• Nuanced reasoning: Particularly strong at tasks requiring careful analysis and balanced judgement.

Why it’s important

PMs deal with information overload daily. Claude’s ability to ingest and reason over very long documents means you can go from “60-page research report” to “structured brief with key insights” in minutes rather than hours.

Try it yourself

Try Claude at claude.ai.

Understand (or write) code as a PM: Cursor

Cursor is an AI-powered code editor that has become popular among non-technical product people exploring what’s being called “vibe coding” – using natural language to build prototypes and understand codebases without being an engineer.

Features

• Natural language coding: Describe what you want to build in plain English and Cursor generates the code.
• Codebase understanding: Ask questions about an existing codebase and get plain-language explanations.
• Rapid prototyping: Build quick proofs-of-concept to validate ideas before involving engineering.

Why it’s important

In 2026, the line between “technical” and “non-technical” PMs is blurring. Tools like Cursor let product managers prototype ideas, understand technical constraints, and have more informed conversations with engineers – without needing to become developers themselves.

Try it yourself

Download Cursor.

Prompt engineering basics for product managers

Knowing that AI tools exist is one thing. Getting great outputs from them is another. Here are practical prompting techniques PMs can use immediately:

Drafting a PRD

Instead of: “Write a PRD for a notification feature.”

Try: “You are a senior product manager at a B2B SaaS company. Draft a PRD for an in-app notification system. Include: user problem, success metrics, scope (in and out), user stories, and technical considerations. The audience for this PRD is the engineering team and the VP of Product.”

Why it works: Giving the AI a role, context, structure, and audience produces dramatically better output.

Analysing user feedback

Try: “Here are 50 pieces of user feedback from our NPS survey. Group them into themes, rank the themes by frequency, and for each theme suggest one potential product improvement. Present the output as a table.”

Why it works: Structured instructions produce structured output. Specifying the format (table) prevents vague paragraph responses.

Running competitive analysis

Try: “Compare [Competitor A] and [Competitor B] across these dimensions: target audience, pricing model, key features, recent launches, and market positioning. Present as a comparison table. Then suggest three opportunities where our product could differentiate.”

Why it works: Breaking a complex task into dimensions and asking for a specific format gives you an output you can actually use in a strategy deck.

Common mistakes to avoid

• Being too vague (“help me with my product”) – always provide context, role, and desired format.
• Not iterating – treat AI like a collaborative partner, not a one-shot oracle. Refine your prompt based on the first output.
• Trusting outputs without verification – AI can hallucinate facts, especially about competitors and market data. Always verify.
• Ignoring your own expertise – AI is a tool to accelerate your thinking, not replace it.

Experience the full AI Learning Track

Learners on the King’s Product Management Career Accelerator get full access to the AI and Product Management Learning Track, which features expert-led live Masterclasses designed to keep pace with how AI is evolving.

The Learning Track is designed to enhance the Career Accelerator’s curriculum and adapt as technology evolves, giving learners the latest tips and techniques to apply AI to their product management work. You’ll also earn a LinkedIn digital badge to showcase your AI literacy to employers and your professional network.

To find out more about the Career Accelerator and Learning Track, download the programme brochure.

Frequently Asked Questions

What is generative AI in product management?

Generative AI in product management refers to using large language models and AI tools to enhance product strategy, research, documentation, and decision-making. This includes drafting PRDs, synthesising user feedback, running competitive analysis, and building prototypes.

How do product managers use AI?

PMs use AI across the product lifecycle: for discovery (research synthesis, competitive analysis), definition (PRD drafting, user story generation), delivery (sprint planning, code review), and measurement (data querying, experiment analysis). Over 73% of PMs use at least one AI tool daily.

What are the best AI tools for product managers in 2026?

Popular tools include ChatPRD (PRD drafting), Claude and ChatGPT (document analysis and research), Productboard AI (roadmapping), Dovetail and Zeda.io (feedback analysis), Julius (data querying), and Cursor (prototyping and code understanding).

Do product managers need to learn to code to use AI?

No, but understanding how LLMs work, how to write effective prompts, and how to evaluate AI outputs is increasingly expected. Tools like Cursor make it possible to prototype with code using natural language, blurring the line between technical and non-technical PMs.

What is an AI product manager?

An AI product manager specialises in building and managing products where AI is the core capability. This requires understanding model behaviour, data quality, prompt engineering, and ethical AI – in addition to traditional PM skills.

Is AI going to replace product managers?

No – but it’s redefining the role. AI handles more execution and routine tasks; product managers focus on strategy, customer understanding, stakeholder alignment, and deciding what to build. The PMs who thrive in 2026 are those who use AI as a force multiplier.

What AI skills should product managers learn?

Start with prompt engineering, understanding tokens and context windows, and familiarity with the major LLMs (GPT-4, Claude, Gemini). Then build toward AI ethics, agent design, and tool evaluation. Programmes like the King’s Product Management Career Accelerator include a dedicated AI Learning Track to build these skills.