AI Landscape for Product Managers

The AI Product Landscape 2025

5 min read

The AI landscape evolves rapidly. Here's what you need to know as a Product Manager in 2025.

The Four Categories of AI Products

Category What It Does Leading Solutions
Large Language Models (LLMs) Text understanding and generation GPT-4, Claude 3.5, Gemini, Llama 3
Vision AI Image and video understanding GPT-4V, Claude Vision, Google Gemini
Speech AI Voice recognition and synthesis Whisper, ElevenLabs, Azure Speech
AI Agents Autonomous task completion AutoGPT, Claude Computer Use, Devin

Large Language Models (LLMs)

The core technology behind most AI products today.

Key Players Comparison

Model Provider Best For Pricing Model
GPT-4o OpenAI General purpose, large ecosystem Per token
Claude 3.5 Sonnet Anthropic Long documents, reasoning, safety Per token
Gemini 1.5 Pro Google Multimodal, long context Per token
Llama 3.1 Meta Self-hosting, cost control Open source

When to Use Which

  • GPT-4o: Broadest capabilities, largest community, most integrations
  • Claude: Complex reasoning, long documents (200K+ tokens), safety-critical applications
  • Gemini: Google ecosystem integration, multimodal from the start
  • Llama: When you need to self-host for privacy, cost, or customization

Vision AI

AI that understands images and video.

Common Use Cases

Use Case Example Technology
Product recognition Visual search in e-commerce Image classification
Document processing Extracting data from invoices OCR + LLM
Quality control Manufacturing defect detection Object detection
Content moderation Flagging inappropriate images Image classification

Key Decision: API vs Self-Hosted

  • API (OpenAI, Google): Fastest to implement, ongoing costs, data leaves your system
  • Self-hosted: Higher upfront cost, more control, data stays internal

Speech AI

Voice-to-text, text-to-voice, and real-time conversation.

The Tech Stack

Component What It Does Top Options
ASR (Automatic Speech Recognition) Voice to text Whisper, Azure Speech, Deepgram
TTS (Text to Speech) Text to voice ElevenLabs, Azure, PlayHT
Real-time Live conversation OpenAI Realtime API, LiveKit

PM Considerations for Voice

  • Latency matters: Users expect <500ms response time
  • Accents and languages: Test with diverse speakers
  • Background noise: Real-world conditions differ from demos

AI Agents

The emerging frontier: AI that takes actions, not just generates text.

What Agents Can Do

  • Browse the web and extract information
  • Execute multi-step workflows
  • Use software tools (like a human would)
  • Make decisions and course-correct

Current Limitations

Promise Reality (2025)
"Fully autonomous work" Needs human oversight for complex tasks
"Replaces entire roles" Best as copilots, not replacements
"Works reliably" Still prone to errors, expensive failures

PM Guidance on Agents

  • Start small: Automate well-defined, low-risk tasks first
  • Human in the loop: Build approval checkpoints
  • Measure carefully: Track success rate, error cost, human time saved

Choosing the Right Technology

Use this decision framework:

What's your primary use case?
├── Text tasks (writing, analysis, Q&A)
│   └── LLM (GPT-4, Claude, Gemini)
├── Image/video understanding
│   └── Vision AI (GPT-4V, Claude Vision)
├── Voice interaction
│   └── Speech AI (Whisper + ElevenLabs)
└── Autonomous task completion
    └── Agents (with human oversight)

Build vs Buy Decision

Factor Build Buy (API)
Time to market Months Days
Control Full Limited
Cost at scale Lower (if successful) Predictable but ongoing
Maintenance Your responsibility Provider handles
Data privacy Stays internal Leaves your system

Key Takeaway

The AI landscape is broad, but your choice narrows quickly based on your use case. Start with the problem you're solving, not the technology you want to use.


Up next: Test your understanding with Module 1 Quiz. :::

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Module 1: AI Landscape for Product Managers

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